Designing fuzzy-genetic learner model based on multi-agent systems in supply chain management

Expert Systems with Applications 36 (2009) 10120–10134

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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Designing fuzzy-genetic learner model based on multi-agent systems in supply chain management Payam Hanafizadeh *, Mohammad Hussein Sherkat Department of Industrial Management, Allameh Tabataba’i University, P.O. Box 14155-6476, Tehran, Iran

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Keywords: Supply chain management (SCM) Multi-agent system (MAS) Fuzzy inference (FI) Genetic algorithm (GA) Self organized maps (SOM)

a b s t r a c t Supply chain requirements and challenges in recent years have made managers to explore new methods in dealing with supply chain management (SCM) problems. Methods with high flexibility which can adapt plans to real conditions help one to make a decision at the right time. In the SCM, distribution and allocation problems are of enormous significance and due to their applications in the cross-functional and final parts of SCM problems, they are in a particular position among the SCM problems. In this paper, by proposing an architecture based up on multi-agent system (MAS), a model is developed to tackle such problems as the nature of supply chain distributions, dynamic distributions systems (DS), uncertain parameters in DS, management of diverse objectives in DS, need for flexibility in DS and other factors considered as challenges and designing requirements in an agile model which can be all found in the SCM. MAS was used in this article owing to their special attributes and features. In MAS, each agent follows up a duty in a self-contained way and is able to adapt it to the environmental changes, after all helping the system to stay alive. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Given the transfer of market’s power to the customers, supply networks providers are undergoing more and more pressure to provide products of high quality, more variation and cheaper price in a shorter time. Meeting customer’s demands in today’s interactive world not only involves producers but also the whole supply chain and its resources. Therefore, all the supply chains have to utilize the most appropriate information technologies to apply suitable SCM methods by sharing facilities and sources, which act such as an orchestra in a harmonic way. In these cases, enquiring agility in SCM is an essential challenge to service-centered and manufacturing organizations, to survive in the market and operate more profitably (Lin, Chiu, & Chu, 2006). Gaining agility requires quick, high quality and low cost response to demands and market interests, reducing the product life cycle, increasing products variety, close interaction with external sources of an organization such as customers, suppliers, and rivals. Extending cooperation with external sources by maintaining independence in decision making for each of them and also

* Corresponding author. Present address: Haft Paykar Corner, Nezami Ganjavi St., Tavanir Ave., Vali Asr Ave., Tehran, Iran. Tel.: +98 21 8877011; fax: +98 21 8877017. E-mail addresses: hanafi[email protected] (P. Hanafizadeh), mh_sherkat@ yahoo.com (M.H. Sherkat). 0957-4174/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.01.008

different decision makers with various skills and targets within the organization makes it quite difficult to use hierarchical decision making structure or completely horizontal one. On the other hand, thanks to productive and communicative technological developments, new options have begun to emerge since 1990 as tools for designing productive and controllable structures in SCM structures capable of arranging and planning organizational sources (customer, rivals, staff, machines and human forces) to meet SCM objectives, of high quality learning skills and appropriate adaptability to the environmental changes surrounding the organization. Furthermore, these structures should be defined in a way that SC adapts itself to the rapid environmental changes with the least resistibility and considers them as opportunities rather than threats. In this study, based upon the formation of interactions between distribution agents in the MAS, a model has been developed to promote the agility in the SC. Starting with the introduction, this article arranged in six parts, is then followed by the second which provides the statement of the problem. Then a review of literature tires to gives us a background of work done in this case. Part four discusses theoretical concepts of MAS, as we go further, in Part five, a model of SC based on the agents and cores pending case study is illustrated. Last but not the least the final part deals with the results and conclusions.

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2. Problem definition To run an agile SC requires the assessment of the challenges of SC Structure. Undoubtedly approaches which are neither able to rise to all challenges or preferably a majority of them nor meet the subsequent requirements do not succeed in getting access to agility. In the following, some of the challenges and requirements which are essential in the article are briefly sketched. 2.1. Nature of distributed supply chain Meeting the SC targets can be achieved by receiving service form different organizations that are members of SC and have distributed nature (Agarwal, Shankar, & Tiwari, 2007). Distributed nature of supply chain means that organizations in SC are independent and also geographically distributed and each of them involves specific processes to achieve certain targets in a SC. Independent elements of a SC can be defined as self-contained organizations. In other words, independence means that each element regards its own profits and necessary decisions are made without interference of other elements or organizations (White, Daniel, & Mohdzain, 2005). Cruz (2008) believes that SC is an open system in which, active organization might enter or leave the system spontaneously and because of high interactions and limitations, it results in a distributed and heterogeneous systems. Because of uncertain and unpredictable parameters, it provides possibility to change the environment of a SC agent. So in these circumstances it is expected of the system to respond properly. Nevertheless, it should be mentioned that while the system is processing the conditions and making a decision, environmental conditions might change. This distributed nature leads SC parts to focus on raising profits and reducing internal expenses regardless of other elements. In this situation, stronger elements of the SC which have high bargaining abilities exert more pressure on weaker elements of the SC so that an inappropriate communicative environment forms among the SC elements (Agarwal et al., 2007).

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ments to the performance, behaviors, and integration in response to the changes. On the other hand, and the flexible method of quit involves the abandonment of competition and inability to meet SC requirements. These researchers acknowledge that understanding changes in process or information flows and their impact on SC, is highly effective in preserving dynamism and helps the SC to adapt itself faster and better to the environmental changes. 2.3. Uncertainty in a supply chain Uncertainty is one of the main characteristics of systems which are associated with customers. Patel, Gunasekaran, and Mcgaughey (2004) believes that the main reason for uncertainty in a SC is its dynamism and states that a multi level SC is subject to a great deal of uncertainty due to its sets of service providers and the existence of uncertainties of elements in each level. This high level of uncertainty reduces SCM abilities to predict future conditions. For example, uncertainty as to the amount of order or customers’ demand and corresponding supply time are just a few to name. 2.4. Management of multi objectives in SC It seems probable that the SC elements have different specific objectives at different time sat any one time. These objectives might not be accomplished simultaneously or be in conflict with each other (Hinojosa, Kalcsics, Nickel, Puerto, & Velten, 2008). For instance, It can be referred to such objectives as reduction in the stock, no shortage, complete response to customer’s needs and reduction in financial recession. On the other hand, activities done to meet these various objectives and in some cases in conflict with each other rely on both internal conditions and environmental conditions. For example, suppliers are inclined to final producers who are committed to purchasing on a large scale with constant quantity and with flexible delivery times. While most producers tend to consider their program as a long-term planning, they have to be flexible in face of variable demands and requirements of their customers (Simchi & Kaminski, 2000). 2.5. Necessity of information sharing in supply chain

2.2. Dynamism of supply chain Given the fact that SC has dynamic nature, it develops gradually and the communications within its different elements are being promoted. Objectives and chain elements plans, environmental conditions and organization’s facilities depending on the type of services provided by them are in the continuous process of change (Simchi & Kaminski, 2000). According to Shang, Anling, and Tadikamallas (2004) the knowledge required to establish coordination among chain’s components is a necessity for maintaining dynamism in SC. This knowledge should be based on the component’s recognitions, processes and chain’s information structure as well as the designed model or tool to manage SC. should be able to rehabilitate this knowledge. Xiangtong, Bard, and Ganng (2004) believe that dynamism of a SC includes two dimensions of risk and power. Risk means that organization’s success within the SC not only depends on the performance of the organization but also the data obtained from other organizations. Power denotes that some of the elements hold information power. For example, in most of chains, retailers have most information power due to the fact that they are in the constant process of communicating with end users. Based on work done by Chatzidimitriou, Symeonidis, Kontogounis, and Mitkas (2007), in reaction to environment dynamism, SC strategies comprises of two flexible methods of ‘correction’ and ‘quit’. The flexible method of correction refers to the adjust-

Competition is a well-known concept in today’s complex world. Lowering final prices, developing services to customers, quick response to customer’s demands and promotion of the quality of products and services are among those which are essential to survive in competition for each product and service in SC. So it is indispensable to all elements and components involved in manufacturing cycle or service, to put it another way, supply chain components, to be effectively interrelated with each other and adopt appropriate mechanisms to reduce prices and improve quality of products and services. According to the studies done by Newman and Krehbiel (2007), one of things which can be done to reduce SC expenses is the issue of placing orders and managing stock. In addition to its direct effect on SC stock costs, this part also has great impact on establishing relationship with chain’s customers and providing services at the right time. Newman and Krehbiel (2007) maintain that the main reason for the malfunction of ordering and SC stock management sector is lack of informational sharing in different levels of SC. By information sharing, we mean customer’s demand information at lowest level of chain is rapidly transferred to the entire levels of SC. Kim, Chatfield, Harrison, and Hayya (2005) refer to it as centralized demand information. According to Shang et al. (2004) contribution of information is a necessity for a dynamic SC environment. One of the effective agents in dynamism is quality and extension of informational con-

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tribution. In their opinion, high quality and extension of information can provoke appropriate reaction to unpredicted changes. 2.6. Necessity of flexibility in supply chain Gong (2008) believes that SC is built upon integration and continuity of organized processes so that flexibility of SC to changes originates from internal flexibility of components and communicative flexibility to the external environment. Shang et al. (2004) consider an adaptive SC to changes as a value chain which is able to produce rapid and accurate responses to changes. In their opinions, sequences of adaptation to changes are maintained as exploring opportunities, speeding up response to expectations and improving processes. To provide flexibility in SC, they have suggested the following steps:     

On-line integration. Process management and updating SC. Event management. Component cooperation and coordination. Standardizing the environment.

They view that for the sake of information integration and process coordination in SC, a system in addition to its ability to analyze and make intelligent decisions is required to perform and establish cooperation in an organization or among organizations. Also Choy, Chow, Tan, Chan, and Mok (2007) presume that an efficient and effective tool in dynamism and adjusting SC to the changes should include the characteristics below in its structure: 1. Process standardization and communicative concepts exchanged among SC components. 2. Modular Structure: although in these structure agents are separated from each other, they can be combined with each other and this gives us advantage to offer more flexibility for systems. Modular structure does not need to repeat unit exchanges, therefore, reduces coordination expenses. The challenges and requirements mentioned above encourage managers to seek for new methods for SCM problems. Flexible approaches which are able to adapt programs to real conditions and, if need be, help the decision maker. Since one of the most common methods for SC modeling in recent years is quantitative modeling, a brief overview of this method and its inadequacies in meeting challenges and needs will be provided. 2.7. Quantitative modeling, a common approach to SC modeling Quantitative modeling of systems which is referred to as the mathematical modeling and operational research approaches is one of the most pervasive methods for modeling and problemsolving which is widely used. Nevertheless, in order to obtain an answer or answers which are optimized,operational research models are in need of a complete set of accurate input into the model which in natural conditions (in the best ones) these inputs are hardly ever available (Chen Chen, Yang, & Liam, 2007). While Turksen and Fazel Zarandi (1999) touch upon the most important issues in the area of SC management such as production planning (programming), stock control, scheduled models, transportation planning, logistics management, distribution channels and similar cases and refer to the fact that all these issues are being faced with quite uncertain conditions. They consider lack of certainty, the existence of error and the deficiency of information as the three main reasons for reflecting the inaccurate environmental conditions in such issues. Li, Hendry, and Teunter (2007) maintain

that at high levels of decision making in SC, managers are involved in long-term planning in the entire SC and are generally interested in applying those models which help them make decisions. As a result, due to the nature of decision-making at the strategic level, models which are merely mathematical should be replaced with analytical ones. Che, Wang, and Sha (2007) highlight the weaknesses of quantitative models; state the followings as the most critical deficiencies: A. quantitative models’ need for accurate input; B. various difficulties in describing quantitative models for managers; C. the language which is used in quantitative models to explain the results and discuss the findings is not so much understandable to managers. On the other hand, if it is assumed that the essential data is available for modeling and problem-solving, models in operational research are facing difficulties in finding out the solution. In addition, their complexities make it quite difficult to understand them. Similarly, the researcher has trouble solving the models. It should also be mentioned that it is too much costly to design software with specific applications as well as hardware which are compatible with these software. It is noteworthy that these models are not so much flexible that for each new problem and model there are new costs and complexities (Chen Chen et al., 2007). Contrary to the criticism leveled at the quantitative modeling methods and operational research approaches, these methods have been widely used in recent research on prominent issues such as in SCM. Relaxation, heuristics and Meta heuristics have been among those approaches which have been exploited in modeling and problem-solving in operational research to reduce the effect of the difficulties and complexities mentioned above. However, the implementation of quantitative models in recent years has revealed a variety of problems such models for designers and users (Che et al., 2007). Given what was already mentioned, in order to achieve the appropriate flexibility and gaining agility in SCM, actions should be taken to design and identify methods and tools which can meet the earlier mentioned challenges and needs. 2.8. Distribution system in SCM Of prime importance in SC are allocation and distribution. These two issues are of such a high position in SCM owing to the fact that they attract attention both as cross-functional problems and also as an activity of the final part of SC. As another reason for the importance of these two subjects we can refer to the close interaction between allocation and distribution in SCM (Li et al., 2007). Since the distribution is much more evident in systems which are engaged in physical products, allocation and distribution are sometimes referred to as distribution system (Weigel & Cao, 1999). According to the definition offered by Weigel and Cao (1999), the distribution system is composed of a set of computational and operational activities which lead to decisions taken about determining the quantity, type and the delivery way of products and services or dividing from one level of chain to other chain elements or marginal chains and/or sources for activity at the same level and/or at other levels through distribution channels. The efficacy of the distribution system in SCM influences the satisfaction of the level or the receiving agent (order agents or active agents in the distribution channel) and/or limited to the last level, it also impacts the satisfaction of order agent or customer, on-time supply of products and services, reduction in stock expenses, planning for producing products or services and in general promoting the efficiency of the SC (Li et al., 2007).

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In systems which outsource distribution agents, administering keeping justice regarding work or wealth distribution among distribution agents and ensuring equality of job equality in organizations which employ internal and external agents, respectively, to distribute products and services are among other crucial outcomes of an appropriate distribution system that enhances the efficacy of SC and reduces the inner tensions (Hendry, Teunter, & Li, 2007). On the other hand, the distribution system in SC is classified as complex. Alter (1996) believes a system’s complexity ensues from the variety of system elements, the number of elements and the nature of the elements which interact with each other in that system. Scuricini (1998) attends to four parameters having impact on complexity and mentions that such agents as the multi aspects, variety of components, component’s type and organizing or interacting way among components, organization and/or the interaction between the elements exert influence on the magnitude of complication. According to the definitions mentioned above, the distribution system in SC is categorized as complicated because a host of constraints and variables which are far numerous regarding their number, variety, and type and interact differently and extensively with each other are imposed on this category of systems. In the case of the distribution system at the last level of a simple SC of a hypothetical goods, such factors as multi aspects and variety of transportation means with technical and quality capacities and constraints, the existence of variety of distributable products and different quantities of each order, structural differences in carriage vehicles and distribution agents, various priorities for the supply of products type and their significance, the diversity and the existence of limitations of supply centers and their geographical dispersion, the diversity and variety of customers and their significance, the possibility of defining different levels of the supply of services, and the diversity of distribution networks and channels are among some of the factors, you name it, which add complexity to the distribution. To efficiently manage such a system in SC, various objectives and limitations should be attended to simultaneously. Of these objectives and constraints it can be referred to the utilities and distribution agent limitation, time and credibility priorities of order agent, type and amount of order, capacity, technical and qualitative limitations, the sequence of orders, rules and regulations dominant over the allocation problem, flexibility in coping with specific and environmental conditions. The above-mentioned factors having impact on the design of such systems may be quantitative or qualitative. The amount and intensity of factors vary to different environmental conditions. In addition, presentable goods or services and distribution agents are dynamic in terms of number and attributes by the time. On the other hand, some of the objectives and constraints placed on this system are qualitative by nature and are not measurable quantitatively. These objectives are not necessarily unidirectional and some of them are contradictory although they have to be taken into account simultaneously. The degree of importance of these objectives during the process of decision-making varies to different environmental conditions. As it can be seen the distribution system in an SC has a profound effect on the agility and efficacy of SC. In addition, to achieve the agility and efficacy, it faces the same challenges and requirements which were mentioned earlier in defining the problem. It can be claimed somehow that all challenges and requirements needed by an efficient and agile SC are hidden in a distribution system. To design an efficient and agile model in a system with abovementioned characteristics, methods which are able to obtain qualitative and quantitative values at one time as well as to respond rapidly to different environmental changes should be used. Such a model has to be able to design a system that can take acceptable

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decisions under environmental conditions which has not perceived yet and has not been planned for it and in the case of committing errors, is able to learn and self-correct. The reason that can be mentioned for examining the possibility of using multiple-agents systems in modeling the distribution system in this article is their specific characteristics of these systems. In an MAS structure, each agent carries out a given task in a selfcontained way and is able to adjust itself to environmental changes, which such a characteristic helps a system survives. Since self-control is indispensable to the survival of a system, agents can control processes based on comparing the real results with the target ones. On other hand, agents are adjusters and if outputs differ from target results, it takes actions to reduce the differences between the outputs and target ones. Given what mentioned earlier it is expected that owing to their specific characteristics including coordinating models, integration capability and information coordination, regularly offer more appropriate solutions than the current ones to deal with challenges and requirements of SCM and SC. Characteristics which have been taken into consideration to assess the presented model based on MAS of SCM and distribution systems are of significance from two following perspectives: 1. Centralizing decision-making, sharing the information along the distribution system, and taking into account the entire SC in decision-making. 2. Receiving direct feedback for agents regularly form distribution system and independent and automatic reactions of levels based on these feedbacks. 3. Review of the literature. Multiple-agents systems are new approaches in which decisions are made based on the processed information from different sources and various natures (Wooldridge & Jennings, 1995). It can be stated that for the first time it was Morley who practically employed multiple-agents systems. He used the concept of multipleagents systems in planning workshop’s floor. The research attempted to investigate the planning which is done in the case of painting workshops of General Motors to minimize the change in color of chambers and preparation times. In the model suggested by Morley, respective to each chamber, an agent is defined and the interactions among was formed on the basis of auction mechanism. Then Longerman and Ehlers (1997) suggested using MAS to schedule the maintenance and repair operations, operational activities, crew allocation as well as scheduling and allocating flights at airways for one airline. Furthermore, Lin et al., presented a method which can be used to model the scheduling system of an SC on the basis of various agents. In their research, two types of agents have been defined. Physical agents which are working unit representatives and carriers of the materials flow-performance agents that control the information flow and are representatives of the existing information systems in every chain elements. In their research, Sadeh (1998) proposed the idea of utilizing multiple-agents systems in designing the software structure of controlling products flow in SC. In that study a library of software concepts has been developed, this consists of two agents, structural agents and control agents. Structural agents include retailers, distribution centers and suppliers. Control agents are composed of control policies depending on the information, requests, logistics and materials flow which governs products flow. In other words, structural agents are those which exploit control agents to communicate and control products flow in SC. Of the advantages of soft system used in Sadeh (1998) research is the lack of need for the repeated attempts to develop the model of software system of product flow control to cope with environ-

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mental changes and the possibility of using the designed structure again in under different circumstances and in various SC. Hirai and Mori (2000) have investigated an architecture by a mobile agent to implement the independent databases distributed in a two-level informational system of SC. Fox (2000) getting the idea from the system developed by Lin, Tan, and Shaw (1998), presented a model of SC planning system on the basis of six agents of order, logistics, transportation, scheduling, sources and distribution. In a study done by Lou, Zhou, Chen, and Springor (2003) on the application of multiple-agents systems to determine the order amount in SC and the comparison of this approach with classical ones, they came to the conclusion that the utilization of multiple-agents systems are providing better solutions, compared with the classical methods which analytically and computationally attempt to determine the rate of optimized orders and do not take environmental changes into consideration. Cicirello and Smith (2004) have examined the usage of mechanisms dominant over insects’ social systems in forming interactions between agents in multi-agents systems (MAS) to schedule dynamic activities. Whiteson and Stone (2004) have applied MAS concept to designing learner computer systems to enhance performances of this group of systems and developing an appropriate solution to a fully active environment without human aids, just drawing on their previous experiences with interacting with the environment. In another research done by Liang and Huang (2005) on modeling in SC system, ordering is based on MAS. Their findings revealed the fact that the use of MAS concept has reduced ordering and order alteration expenses. In their survey, to predict the demand and quantity of ordering, Genetic Algorithm was utilized and it was also attempted to depict the efficiency of MAS usage and its impact on the performance of SC using defined characteristics. Labarthe, Espinasse, Ferrarini, and Montreuil (2007) have done a study to investigate the effect of using MAS in reducing SC expenses. The overall cost of SC as an agent of the highest importance in assessing SC performance is a function of variables such as budget at disposal and delayed order value in each level of SC during the period of the study. This study revealed that due to the possibility of negotiations in different levels of SC and of change in accordance with new conditions in each SC the application of MAS in designing ordering system decreases goods budget value at all levels of SC, which in itself resulting in the reduction of expense and promotion of SC performance. Referring to the fact that in an SC because of stocking in predictable error and the increase in available budget level, the higher we move toward the upper levels, the higher the cost of the levels, Mele, Guill’en, Espu~na, and Puigjaner (2007) investigated and analyzed the function of MAS in reducing chain’s expenses due to central decision-making and SC level’s budget relationship between other levels owing to the communicative mechanisms between agents. Their research indicates that the usage of agents can promote SC efficiency characteristics without disturbing level of service providing to customers and cut SC expenses. Forget, Amours, and Frayret (2007) studied the employment of MAS scheduling distribution in a wood industrial SC. With respect to the fact that planning in lumber industry is dependent on natural resources and its prediction of producing resources is function of complicated and different variables and, on the other hand, production resources are geographically distributed. They developed a production planning system based on MAS structure. In their study, a system of distributed decision problems was designed by blending operational research concepts with MAS structure. According to the system designed by these researchers each production center can plan the distribution independently (in a selfcontained way) based on regional requirements and available re-

sources. This is the case when constraints such as budget amount, supply conditions and coordination of prime objectives are removed. To deal with rapid environmental changes in the case of any variation in the amount of demand and order in military logistics, Laua, Agussurjab, and Thangarajoob (2007) exploited a system of MAS due to its authority level and independent agents. In that research, a fuzzy framework was designed to perceive environmental conditions and transfer the information received from the environment to the decision-making agents. Moreover, the designed model was assessed in simulated conditions to plan resources in the military logistic system.

3. Introduction of MAS MAS grew out of today’s world need and a far-sighted view about the future’s environmental conditions. On the one hand, the need for learner, self-organized and knowledge-oriented organizations, and on the other hand, the pressing need for decentralized problem-solving methods to control and plan complicated production systems and to predict the performance of social and economical complicated systems, has made the MAS the focus of attention. Nowadays, a lot of researchers in different disciplines and professions supported by universities and big international companies attempt to figure out better strategies to deal with more complicated problems and systems. In MAS approaches, decision-making is based on the analysis of the information form different resources and of different natures (Wooldridge & Jennings, 1995). MAS consists of a series of decentralized agents with mutual relationships, which each of them is able to make independent decisions; in the other words, they are self-authorized (autonomous) and make modeling the entire system feasible. Each system agent has its own wisdom and its behavior is the result of its observations, knowledge and interactions with other agents and the environment. The effect that an agent performance has on other agents and finally on the overall system performance is cooperation among agents and learning abilities. Key points of MAS foundations are described in the research done by Wooldridge and Jennings (1995), Jennings, Sycara, and Woolridge (1998), Weiss (1999), Ferber (1999) and Stone and Veloso (2000). The distinguishing feature of agents is their learning ability which usually takes the form of consolidating (reinforcement) learning and occurs while they are interacting with their surroundings. In MAS, each agent attempts to achieve its objectives in spite of environment uncertainties. Each decision taken by an agent leads to an activity that can influence the future conditions of the environment and in the same way the opportunities and options provided by the future conditions of the environment. Therefore, proper decision-making of an agent relies on attending to indirect consequences or those accompanied by delayed decision in current situation for decisions made by other agents and future conditions. It is obvious that it is not entirely possible to predict the effect of an agent’s decisions and activities on future circumstances, so an agent should control its environment regularly and provoke appropriate reactions to it. Each agent can improve its performance over time and in a process of gaining experiences. According to Wooldridge and Jennings (1995) agent-oriented systems are those which are planned on the basis of the interaction of one or more agents with their environment and move towards system objectives. Therefore, the following definitions can be presented: Entity: It is something which is not able to make decisions and a set of characteristics or so-called characteristic prototypes. Each

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Fig. 1. Agent behavioral mechanisms (Wooldridge & Jennings, 1995).

Entity is the same as decision variables whose attributes are a function of objectives and inherited limitations (Wooldridge & Jennings, 1995). Characteristic pattern: A pattern is a vector with elements including most of the adventives of objective function and imitations of the problem (Wooldridge & Jennings, 1995). Agent: An agent plays the role of a decision-maker in a problem. The agent communicates exclusively with entities and only understands their existence and cannot make any other sense of the environment (Wooldridge & Jennings, 1995). According to the definition provided by Wooldridge and Jennings (1995) an agent consists of three segments named reception, adaptation and calculation. In the reception part, the entity pattern is received and in the calculation part, the degree of desirability of each entity is estimated regarding their characteristic pattern and the use of agent policy functions. In adaptation part, each agent tries to improve its policy function to achieve more desirability. Fig. 1 demonstrates an agent’s behavioral mechanisms based on the researchers’ opinions. 4. Supply chain modeling based on agents According to the definitions provided in previous sections, each component of SC is involved in various activities such as: planning and controlling stock, quality control, procurement, marketing, relationship with customers, sale, distribution, etc. Therefore, concerning MAS definitions and concepts, an SC can be considered as MAS in which each element of chain has the nature of an agent. On the other hand, each component of SC can be viewed as MAS in which every agent communicates with other MAS of SC in addition to their interaction with other agents of MAS they are a member of. So each SC can be seen as a system of n agents in which the magnitude of n depends on the number of activities, the nature of performance and complexity of SC in question. As mentioned earlier, this article aims at investigating the possibility of modeling a distribution system of multi-level SC of physical goods in the case of orders received from customers outside the organization. It is also assumed that orders are sent form external agents (companies or distribution institutes) to customers. The structure of this SC is one of the most common that is found in SC of different goods such as oil productions, medicine and food. An attribute of this type of SC structure is the competition among external agents involved in transportation and physical distribution. Due to the competition, these agents attempt to maximize their profits which can be in conflict with previous SC elements or the whole SC objectives. For instance, at the time of resource allocation these agents try to assume responsibility for distribution of goods with minimum cost and maximum profit.

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Therefore it is quite obvious that customer’s order distribution which is of less attraction to distribution agents is disturbed. In most supply chains, due to the complication of resource allocation and categorizing the solution to this problem in NP-Hard problems, distribution agents job allocation and distribution are based on negotiations between SC allocation operators and agents or so-called bargaining mechanisms to reach the utility point that each one’s profits, not completely but relatively are made. This issue is another reason for consistency of distribution modeling system with MAS (Mallik & Harker, 2004). What is done in bargaining mechanisms in distribution systems consists of announcing a number of priorities presentable to the agents and picking out one of them by the agent on the basis of maximizing policy. Of course, what is effective in making a list of priorities which can be announced by allocation sector operator is the rules and regulations governing the distribution system, customers’ orders, technical and quality conditions of agent consistent with distributable entity and the like. 4.1. Overall view of proposed model Main members of an SC can be introduced as supply agents and consumer agents. Given the fact that in an SC, the customer of the current stage is the seller to next stage, it can be stated that all agents in an SC are in some way both supplier and consumer. Among the main agents of an SC there might be communicators that fulfill the role of transferring goods to one or more supply agents or consume agents. These agents can also be viewed as supply or consume agents but we assume them as transferring agents. Each agent consists of smaller agents. If a combination of one or more consume agents with one or more supply agents and transfer agents can be called a distribution system, then an SC can be modeled as a series of distribution systems (Fig. 2). Such an SC demonstration is more important in chains whose most of their added value is concealed in allocation methods and products distribution among different chain agents and there is much emphasis on appropriate distribution systems. Consume agents, supply agents and transfer agents all communicate each other with their surroundings. Communications interpretations vary depending on the type of distribution system, but in each case there is flow of information form one side and services or goods from the other side. For example, in communicating with the environment, consume agents obtain information about customer’s demands, government policies, market and competitors’ conditions as well as predict market’s future. Based on the predic-

Fig. 2. SC modeling as series of distribution systems.

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tions, then orders are placed to supply agents. Regarding the orders received form different consume agent, market conditions, its supply situation, production line conditions, entity of intermediate and final goods, supply agents embark on planning to provide for orders. This plan affects the supplier’s environment. In Fig. 2 information flow form consumer agent to supply agent is assumed without intermediary and goods flow from supply agent to consume agent is achieved by transfer agent as an intermediary. Arrow lines between the supplier part and consumer part of each entity expresses supplier–consumer relationships within each chain entity. In this figure, it is assumed that each transfer agent itself is composed of smaller agents. Environmental events of other SC are assumed as the system environment. 4.2. Interaction between agents in the proposed model What seems crucial in designing SCM based on MAS concept is defining an appropriate mechanism for cooperation or competition between agents within a chain. One of the methods of interaction establishment between agents in MAS is market mechanism (Cicirello, 2001). Such a method devised on the basis of Adam Smith’s theory on free market movement to a suitable balance point. According to market mechanism principles, each agent with regard to its capabilities, wealth and utility function for goods that are offered in the market proposes a price and of course it will be winner provided that it purchases the goods (services) in a situation that there is no higher offer. The fact that seller wants to sell his goods at the maximum possible price is absolutely logical; of course, it is expected that rich agents’ tendency toward purchasing low quality products should be less than that poor agents that are aware of their chance of gaining quality products. Along with the mechanism described above, negotiation and bargaining mechanism between agents can also be used as a complement mechanism which of course requires defining special communicative protocols between agents respective to chain agents. As with features and concepts of SC, this method has the maximum compatibility with the research topic. In this method, by prototyping form market mechanisms in modeling interactions between SC agents it can stated that supply agent transfers its goods or services to the agent which provides maximum utility degree in transferring goods or services to consume agent.

what was referred to in the case of MAS Table 1 defines the policy and environment of these three agents. Each of the above agents itself can consist of various low-level agents. For example, supply agent is composed of following subagents: Sale, production, planning, etc. Fig. 3 simply illustrates some of the agents forming SC and their interaction with each other and the distribution system environment. In distribution systems, an intermediary agent with distributable entity allocation role as distribution agents can boost marginal profit, agility and SC efficiency. This intermediary agent here is referred to as allocation agent. Allocation agent can be subsidiary to each of triple distribution system agents which lead DS (supply, transfer, consume) agents. In situations where supplier controls (adjusts) the market, allocation agent is subsidiary to supply agent. Fig. 4 shows how supply and environment agent interact with each other under such circumstances. In this figure allocation agent along with two ‘‘supply and sale agents” are represented as subsidiary to SC. SC consists of more insignificant agents but because of the high and effective interaction of sale and production agents with allocation agent only these two subsidiary items have been pointed out and the rest have been neglected. Fig. 5 depicts allocating agent as subsidiary of supply agent in an SC. As indicated by the figure, sale, transfer, production and consumption agents all lie in allocating agent but only sale, production and transfer agents are in direct contact with allocating agent. Regarding the fact above and one of the objectives of this research which is designing an efficient allocating agent, the environment can be summarized. For example, allocating agent receives all the processed information’s about orders and consumption agents from sale agent.

4.3. Characteristics of main agents in the proposed model As it was already mentioned in Section 1–4 for the sake of SC modeling, we have assumed three agents of supply, transfer and consumption. Based on theoretical concepts of MAS, each agent is comprised of two main parts; Perception part that perceives environment and policy function that forms an agent’s decision through interaction with the environment and other agents. Given

Fig. 3. The graphic representation of the interaction between agents in a DS.

Table 1 Characteristics of main agents in the proposed model. Name

Environment

Policy

Supply agent

Such as: customer’s orders, market conditions, policies, rules, governmental conditions and supervisory organizations Such as: orders before distribution in formation on paths and accessibility of consume agent, transportation costs, geographical situation of transfer agent geographical situation of consume agent, etc.

Such as: production to meet customer’s demands with maximum profit, cost reduction, etc. Such as: transfer of presentable entity form supplier to consume agent in a way that it takes the most utility Maximum utility can be found in cases such as transportation cost reduction, increase in profits, and possibility of distribution of all entities after transmission or initial entity delivery closeness to desirable geographical situations. Possibility of achieving other economic activities at destination Such as: responding to needs with minimum cost and maximum utility

Transfer agent

Consume agent

Such as: final consumer orders or other chain levels, market conditions and environmental conditions

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Fig. 4. The interaction between supply agent and the environment.

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Fig. 6. Interacting way of allocating agent and environment.

According to fundamental concepts of MAS, three main parts of allocating agent can be defined as follows:

Fig. 5. Position of allocating agent in DS.

Therefore, consumption agent can be eliminated form its environment. As another example the connection between allocating agent and DS environment is limited to the received information about rules and regulations. It obtains all other required environmental information from agents such as sale or production agents. Allocating agent also receives the goods production plan through its connection with the production agent. On the other hand, through interaction with sale agent, it receives customer’s order list, its significance, delivery time, the required quality and other relevant items. The latest governmental legislations on performance of that DS is also prepared by the external environment of supply agent for allocating agent. The collection of such information forms the environment characteristics vector. Based on the characteristics vector, allocating agent sets out to make decisions. Fig. 6 illustrates a summarized model of the interactions of allocating agent with its surrounding environment. it is noticeable that in this article a method for designing an allocating agent as a constrictive component of DS has been presented and the study is limited to this agent and its interaction with its surrounding environment.

Sensor: it is a part that helps the agent perceives its environment. In the proposed model, the sensor receives information on the transfer agent, service applicant, characteristics of the orders received from customers, customers’ priorities, etc. Policy function (decision making): in the proposed model, the decision atmosphere of allocating agent is composed of different order entities received from sale agent that can be allocated to applicant transfer agent. Each decision made by allocating agent changes and updates the environment. Memory: this part itself consists of short-term and long-term memory that can be employed to keep the agent’s behavioral records for learning. Learning: this section is designed to improve agent’s behavior in the environment with respect to recorded behavioral history in the designed agent memory.

4.4. Features of the agent and environment in the proposed model of DS According to the SC in question in this article, the studied DS has available discrete, certain and dynamic environment. The environment is discrete because the number of cases is countable despite the fact that number of possible cases is so huge and they are unpredictable. The environment is dynamic because new environmental conditions might occur in a second. Besides allocation effect of each order in goods DS is absolutely clear and definitive and the environment is available since the agent has all the required information for decision-making at his or her disposal and no section of the environment is hidden from him or her as such the environment is certain. The proposed communicative mechanisms for SC agent in this article are Blackboard structure. This communication method is a communicative system between agents in which each agent establishes communications with other agents through making a change in the common atmosphere (blackboard). In fact, this communicative system is a mechanism for changing the interactive environment (Teruaki, 2000).

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Table 2 Features of agent and environment in DS model. Agent

Environment Interaction between the agent and the environment Decision-making environment (action)

Type: profitable and learner Profitable function: a set of objectives and different utilities depending on the decision-maker’s opinions (multi-aim function), utility function of decision makers are not exactly clear and only an overall (not detailed) information are available Dynamic, certain, available, discreet (a lot of cases) Variation in information received by agent from the environment to make decisions Discrete and countable, equal to all possible allocations form distribution entities to transfer agent

Similarly, with respect to the fact that system aims at maximizing decision-making utility, agent can be categorized as the profitable agents. On the other hand, DS performance regulations are not precisely clear, so an agent should be able to improve its decision making method gradually through interaction with the environment. Hence, an agent must be able to learn in addition to being profitable. Table 2 displays features of the agent and environment. Furthermore, since an agent makes detailed observations of the environment, it does not need to develop a model of environment. Regarding the agent and environment attributes, characteristics of the agents required for DS modeling in this article are as follow: 1. Having an assessment function of its decision’s utility. 2. Having policy function with following attributes. i. Learning ability. ii. Logical decision-making ability in environmental conditions that have not been perceived before. iii. Performance which can be perceived by decision-makers. iv. No dependency on the number of environment conditions.

4.5. Allocating agent program in the proposed model According to the MAS concepts, the structure of an agent includes agent program as well as sensors and operators. Fig. 7 shows allocating agent in DS program. According to this figure, whenever allocating agent is called to make a decision, in the first place he or she perceives the environment and then makes a decision based on the perceived environment. This decision is recorded in memory and used for learning of the system at the right time. What is meant by the perception of environment is obtaining information about agent or transfer agent who applied for transporting distribution enteritis, entity value of each of distribution units, orders from consumption agents, the latest regulations and the internal or external guidelines on DS. 4.6. Decision-making process of an agent After perceiving the environment, agent begins to make decisions. Agent’s decision-making process, from the type of information received from environment to the allocation of an entity which can allocate to transfer agent is proposed as follows: In the first place, a tree diagram of decision about rules governing decision-making process must be drawn. This decision tree helps to adapt predetermined environmental conditions and the environment requirements to the agent’s circumstances. It results in more adoptability of the model to the environment in question.

Fig. 7. Allocating agent program.

In the next step, utility of each consumption agent’s orders that can be allocated to the transfer agent or agents is calculated. Next, the interaction mechanism type between system agents is identified. As earlier mentioned, market mechanism in such series of problems is given higher priority. In the fourth step, distributable entity allocation to distribution agent is processed. Finally, characteristics vector of the environment is produced which can be used in learning section of the system. 4.7. Utility calculation in allocating agent Depending on the fact that decision-making of an agent is viewed as futuristic or not, allocating agent utility can be calculated using following methods. In decision-making which is not futuristic allocating agent utility can be estimated as the following: 1st step: Identifying entity orders to the applicant transfer agent. 2nd step: Establishing a new entity for each entity order to transfer agent. 3rd step: The agent’s most principal process is transforming entity characteristics vector which was drawn in 2nd step. 4th step: Entities built in 2nd step are arranged with respect to their utility value calculated in 3rd step. 5th step: M entities with maximum utility value (as arranged in step four) are proposed to allocation system. M is a parameter that is determined by SC model conditions. Utility calculation in futuristic decision-making is quite similar to that of without futuristic view. They only differ in that instead of producing entities referred to in the second step of the method without futuristic view based on transferring agent while receiving service; all the transfer agents in queue are also considered. For each of them allocation utility is calculated given the approximate time of service reception. If we put transfer agents in row and orders in column and for each order we save utility value in the related entry, finally we reach a matrix that can be used as allocation utility matrix. So we define. m: number of transfer agents waiting to receive services or receiving them; n: number of entity orders; i: indices standing for transfer agent;

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j: indices standing for order; ui,j: utility value of allocating j order to i transfer agent; xi,j: allocation variable, equal to 1, if j order is allocated to i order of transfer agent. By solving mathematical model of first equation, we can find optimum allocation of orders to transfer agent.

ð1Þ Max z ¼ 8 > > ð2Þ > > > > > > < S:t ð3Þ > > > > > > ð4Þ > > : ð5Þ

m P n P

xi;j ui;j

i¼1 j¼1 m P i¼1 n P

xi;j 6 A for j ¼ 1; . . . ; n ð1Þ xi;j 6 B for i ¼ 1; . . . ; m

j¼1

x1;j ¼ 1

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4.9. Allocating agents’ learning Agent required to model distribution system in this article must have a policy function with great learning ability. Since the main agent studied in this article is allocating agent, in this section we will describe the proposed model of allocating agent’s learning part. Learning process of the allocating agent is designed according to Fig. 8. As it can be seen in the figure, learning part is associated with short-term memory and decision-making part of the allocating agent. Short-term memory provides the data required for learning section, the learning section, in turn, affects decisions by adjusting parameters of agent policy function. Main processes of learning section of the allocating agent are indicated by numbers from 1-4 to 4-4 in Fig. 8. These processes, respectively, are:

xi;j ¼ 0 or 1

In the above model, Eq. (1) shows problem objective function (which is maximizing allocation utility) and Eq. (2) stating the maximum ordered number (that is displayed by A) can be allocated to transfer agent. Eq. (3) indicates that each order at most can be allocated to several transfer agents (shown by B). Eq. (4) states that an order must be allocated to first transfer agent, which is the transfer agent receiving the service. Eq. (5) also suggests binary nature of allocation variable. 4.8. Policy function of allocating agent As the allocating agent was defined, allocating agent policy function is a function that maps entity characteristics vector built in second step to a real number called utility. This function can be any mathematical equation but regarding the special requirements of the studied model, such as different objectives in decision-making and ambiguity of decision maker’s utility function, tools with high mapping ability can be used. In Table 3, a list of possible tools with such ability is made and their advantages and disadvantages have been considered with respect to DS in SC. As indicated in Table 3, of comparable tools, the only system that can be investigated in the model of research is the fuzzy inference system. According to the information in Table 3, it is clear that Mamdani models are more mental and rules of these systems are usually laid down by experts but in Sogeno model most rules are set by extending non-fuzzy equations available in the system.

1-4 process: deterring the ratio of conflict data to all data; 2-4 process: producing teaching prototypes; 3-4 process: adjusting policy function parameters; 4-4 process: updating short- term and long-term memory of agent; Short-term and long-term memories serve as an information bank the environment characteristic vector each second saves agents decision and behavior. After going through the following stages, some of the records in working memory (short-term) memory are used to teach decisionmaking method to the allocation agent. At adjustment sector of policy function parameters, the objective is to adjust the allocating agent’s decision-making method. Given the fact that in the proposed model, policy function agent is comprised of a fuzzy inference core, learning means adjusting the agent’s fuzzy inference system parameters in a determined domain to reduce the conflict between the expert human operators decision-making and the fuzzy inference system. Of fuzzy inference system parameters that make the system acquire learning ability are: 1. 2. 3. 4. 5. 6.

Fuzzy rules. Membership functions of Fuzzy parameters. Rules weight. Influence method of rules weight on output rule. Method of integrating output rules with each other. Defuzzification method.

Table 3 Comparison of different tools for approximating utility of each entity driver-order in research model. Tool’s name

Advantages

Disadvantages

Expert system

 Perceivable for decision-maker  Mapping the system knowledge

Rules of the system, their sequence and selection logic in research problem is not clear

Feed forward multi-layer neural network

 Ability to approximate the most complicated mapping function from input to output  Learning ability  Working on the basis of expression rules and completely comprehensible  Learning ability  Ability to use detailed system knowledge

 There is no data from history of system for educating network and they cannot be produced because exact utility function of decision-maker is not available  Network performance depends on all calculated utilities for built entities and common learning techniques in neural network cannot be applied to solve the problem  decision-making method is not comprehensible to the user

 Quick  Learning ability  Ability to use detailed system knowledge

 Less comprehensible than Mamdani system to users

Mamdani fuzzy inference system

Sogeno fuzzy inference system

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algorithm, has great searching power parallel to its searching space it is so popular for adjusting fuzzy inference system (Stach et al., 2005). 4.10. Case study and numerical results

Fig. 8. Allocating agent learning process.

To adjust the above-mentioned parameters, when a collection of input–output data is available, different methods can be employed, of which it can be referred to (Stach, Kurgan, Pedrycz, & Reformat, 2005): 1. Reinforcement learning family method such as Q-learning. 2. Neural network’s different methods of learning and teaching such as method of back propagation error. 3. Different nonlinear optimization algorithms such as descending gradient. 4. Different Meta-Heuristic optimization algorithms such as genetic algorithm. Meanwhile the application of neural networks in adjusting fuzzy inference system parameters attracted lots of attention, but these methods suffer from some disadvantages to be used in this the article, of the main ones the following can be referred to: 1. Teaching data are not in input/output format. Because of the learning nature in DS model, type of system teaching data is not in input/output format. Although these types of teaching data can also be used in neural networks form, more complicated algorithms and structures need to be designed compared with those of common algorithms in neural networks (Stach et al., 2005). 2. Learning and teaching methods of family of neural networks are all based on nonlinear methods. Regarding to the variable numbers (threshold value and synapse weights) and complication of search areas, possibility of converging to on inaccurate optimum local solution is so high (Stach et al., 2005). So use of meta-heuristics algorithms such as genetic algorithms is preferable to the neural networks. Considering the above items about nonlinear optimization algorithms in proposed model Meta heuristics search algorithms are preferred to neural networks. Among meta-heuristic methods, methods with efficient search ability in continuous spaces must be chosen. For the same reason, methods such as ant colony algorithm which is naturally a searching algorithm for discrete spaces and its continuous versions that are inefficient (Stach et al., 2005) are waved aside. Among meta-heuristic methods with searching ability in continuous spaces there are various algorithms; one of them, genetic

In this article, the Iranian Oil Products and Distribution Organization’s DS was investigated. SCM and oil products distribution is one of the critical parts of Iran’s economy which has attached lots of attention and can be studied from different perspectives. First, oil products fulfill a significant rule in all aspects of the country’s economy and their adequate supply and distribution have a great impact on the economy and economical security of the country. Similarly, due to the fact that oil products are consumed directly by families and final consumers, their appropriate supply and distribution can also promote public welfare and social security. As mentioned in Section 1–4 allocating agent can be subsidiary to each of triple-agent system that undertakes the role of DS leader (supply, transfer, consume agents). This role in oil product DS is left to supply agent. It is noteworthy to mention that it was assumed in this article that oil products are transported using trucks and here are referred to as order-driver entity. 4.10.1. Decision-making process of the allocating agent After perceiving the environment, allocating agent begins to make decisions. Decision-making process of the agent, from receiving the information about the environment to allocating a product to the transfer agent is depicted in Fig. 9. Circles illustrate decisionmaking points which are labeled by special numbers. 4.10.2. Calculating utility in the allocating agent Calculating utility of entity inputs into the transfer agent depends on whether the allocating agent holds a futuristic view or not. Decision-making without having a futuristic view considers only transfer agent while receiving service; on the contrary, futuristic decision-making incorporates all transfer agents such as transfer agent while receiving service and transfer agents in queue. Compared with non-futuristic decision-making, the futuristic one estimates the utility of the entire system in a more efficient way but its decision-making process takes up more time. Both of them, however, can be used by the allocating agent.

Fig. 9. Decision-making methods of allocating agent.

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In addition, the futuristic decision-making differs from nonfuturistic one in that instead of producing order-driver entities based on transfer agent while receiving service, all other transfer agents in queue are also taken into account. Moreover, for each of them, depending on the approximate time of receiving service, allocation utility is calculated. In the event that transfer agents are put in a row and orders in a column. For each order, utility value is saved in a corresponding entry then we will come up with a matrix that can be used as an allocation utility matrix. Allocated orders will be allotted to the transfer agent while receiving service and when the new agent is ready to receive the service reception station, calculation will be repeated. 4.10.3. Policy functions of the allocating agent As the allocating agent was defined, policy function of the allocating1 agent is a function that maps the characteristic vector of order-driver entity onto a real number called utility. This function can be any mathematical relation but due to the special requirements of the studied system in this article (Section 4.8) Mamdani fuzzy inference system was selected to approximate order-driver entity utility since it much more comprehensible to the users. The first step in designing fuzzy inference system is to recognize the input variables. The next step is determining entity values to each input and output variable. Finally, rules weight must be identified. In this article, analytical hierarchical process was used to establish rules weight. These stages will be described later on in the rest of the paper. 4.10.4. Definition of the input variables and fuzzy outputs To design fuzzy inference system in the case studied in the present paper, first of all we should provide a definition of the input and output fuzzy variables in an inference system. Input variables vary in different samples but output variable is always the ‘‘allocation priority”. This output variable indicates the magnitude of the allocation priority of each authorized order to the driver considering all conditions of orders and drivers. The higher the allocation priority, the higher the utility of the corresponding order to be allocated to the applicant, that is, the oil tanker driver. To formulate linguistic terms of this fuzzy variable, interview with experts was utilized. It was revealed that distribution unit operator at the time of allocating product to driver makes decisions based on five probable cases. In other words, in the mental and behavioral model of each operator, based on different agents such as: the customer’s order priority, product priority, application priority, the last time the product submitted to the customer, the number of cargoes geographically allocated to this distribution agent in this shift, the driver’s expected station, the utility of the station, etc., five product allocation cases to distribution agent can be conceived as very low, low, medium, high, very high priorities. Then the interviewed experts were asked to determine the operator mental quality model range at an interval of (0, 1) for 5 earlier mentioned cases by numerical values. Taking Zimmerman’s (1996) suggestions into account about using triangular membership functions for expressing linguistic terms in human systems, all fuzzy variables in this article were assumed triangular. So linguistic terms range of this fuzzy variable are defined as below (2–6): 2 3 4 5 6

Very low = triangular{0,0,0.2} Low = triangular{0,0,0.4} Medium = triangular{0.1,0.5,0.9} High = triangular{0.6,1,1} Very high = triangular{0.8,1,1}

4.10.5. Input variables of the fuzzy inference system Based on the interview with experts, 11 input variables were extracted as input variables of fuzzy inference system in oil product distribution system which are as follows: product priority, customer priority, regional priority, latest submission time, the tolerance of the oil tanker capacity, cost of oil tanker preparation, oil tanker waiting time, distance to the driver’s desired station, station utility, utility of the deriver’s expected station and justice index. 4.10.6. Definition of the linguistic terms of each fuzzy variable Owing to the little information available on the system studied here, for input fuzzy variables three linguistic terms were defined: low, medium and high. As it was stated earlier, as with Zimmerman’s suggestion about using triangular membership function for expressing linguistic terms in human system modeling, all fuzzy numbers were assumed triangular. Similarly, because of low available information about system, parameters of functions for all variables were defined in the same way in 2–6 relations. It is noticeable that in model designing during learning process, triangular terms parameters for each fuzzy variable was adjusted in a way that system performance approaches to optimum case. 4.10.7. Rules definition To define DS rules, based on input and output variables, we interviewed experts of organization and extracted 35 rules. Primary rules weighing is achieved by experts’ contributions and by AHP without normalizing stage. Table 4 shows number of each rule with its primary weight.

Table 4 Primary weight and corrected weight after learning. Rule no.

Primary rule weight

Corrected rule weight

1 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

0.15 0.21 0.35 0.59 0.39 0.84 0.42 0.38 0.62 0.82 0.3 0.49 0.18 0.55 0.82 0.22 0.89 0.69 0.35 0.25 0.71 0.52 0.34 0.72 0.68 0.15 0.39 0.12 0.42 0.23 0.29 0.79 0.59 0.84 0.42

0.35 0.41 0.52 0.61 0.45 0.79 0.42 0.44 0.59 0.73 0.32 0.49 0.27 0.55 0.72 0.34 0.61 0.69 0.39 0.35 0.71 0.59 0.34 0.63 0.59 0.34 0.39 0.34 0.54 0.56 0.71 0.63 0.59 0.7 0.53

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4.10.8. Parameter adjustment of inference system and output results Given the fact that further information is kept by (prod) t-norm operator for multiplying operations considering rule weight at output of each rule, and (prob-or) s-norm operator for adding operations and combining the results of rules and comprehensiveness of the center of area for defuzzification process which was preferred in Jang (1993) researches, we used these operators in our research model. 4.10.9. Allocating agent learning In such a big and critical distribution system as oil product distribution system, managers disagree to assign every thing to a mechanical system and prefer human operators skilled in bargaining with transfer agents. Therefore, in designing an allocating agent, human operator elements are also considered. Human and automatic allocating agent elements are interrelated and complement each other. Automatic part of the allocating agent gives provides the human operator with enormous different suggestions and then he/she begins to negotiate with transfer agent distribution applicant. Human operator has to allocate one of the priorities at his or her disposal, preferably the first one, to transfer agent but he or she might allocate an entity which is out of the proposed list for different reasons to the applicant. When it happen a conflict record will be registered. The ability of the automatic part to analyze the conflict records and improve its performance to minimize the contradictions between human expert operator behavior and automatic part of the system is called ‘‘learning”. In learning process, it is of high significance to attend to the fact: 1. All conflict records are not necessarily true ones. Some conflicts might have arisen due to some illegal relationships between the human operator and transfer agents. 2. Parameters of decision-making system of the automatic part should be changed in such a way that it leads to the least change in the main system and does not ignore items considered in designing the system so that it minimizes the deviation of automatic and human part behavior. Main processes of allocating agent learning part are as follows. Process Process Process Process agent

1-4 determining ratio of conflict data to all data 2-4 producing educational patterns 3-4 adjusting policy function parameters 4-4 updating short- and long-term memory of an

One of the main problems in using meta-heuristic search algorithms such as Genetic algorithms is appropriate parameter

adjustment. To adjust parameters of this algorithm, we should study efficiency of algorithm several times by changing a parameter and keeping other parameters constant; then, by statistical analysis, suitable values can be proposed. The main problem these methods suffer from is the fact they neglect the mutual effects of parameters on each other. To overcome this problem in this article, clustering techniques and space visualization for simultaneous analysis of genetic algorithm parameters (crossover rate and mutation rate) based on self-organized maps (SOM) were used. 4.10.10. Analysis of the simultaneous effect of Genetic algorithm parameters and selecting appropriate values of Mutation rate and Combination rate Chromosome coding method exploited in this article is based on encoding method of real members and each chromosome string is comprised of two parts. First part included 35 genes relating to rules weight, and the second part consisted of 15 genes of fuzzy variable linguistic terms. In the proposed Genetic algorithm, the first population consisted of 10 000 chromosomes randomly produced. Since the second part of chromosome is a function of special rules and was defined based on triangular fuzzy numbers, the first random population must have been controlled before using them to see whether it is authorized or not form the point of view of the produced population, The computable objective function for each member of society was considered, based on minimizing deviation from objective (in this article deviation from objective is equal to deterrence of proposed utility form declared utility about optional choice by operator in case the operator chooses an option different form the proposed option of system). Algorithm was stopped if the response was not improved in 200 consequent iterations. The fitness function calculation is based on ranking method and parents’ selection is based on cutting method with 10% rate. To select the appropriate genetic algorithm parameters, they were analyzed simultaneously based on informational visualization. To this end, 10 000 informational records form history collection of allocation unit activities were picked out. Therefore, the designed genetic algorithm in learning part is run 10 000 times based on registered data from system natural performance and altering two parameters of crossover and mutation rate. Each time the mean of square errors (MSE) obtained through comparing fuzzy inference responses with allocating unit operator performance result (MSE of objective function) was calculated. Finally, 10 000 informational records were obtained from simultaneous effect of different parameters in genetic algorithm for different values of crossover and mutation rate. The data collected by the use of MATLAB software and toolbox of self organized maps in two visual dimensions was analyzed (Fig. 10). As it is clear from Fig. 10 and on the basis of knowledge extraction rules form self-organized maps, the results are as follows:

Fig. 10. Analyses of simultaneous operator effect and different genetic algorithm on its performance.

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Shape parameters of primary membership function

Very low = triangular{0, 0, 0.2} Low = triangular{0, 0, 0.4} Medium = triangular{0.1, 0.5, 0.9} High = triangular{0.6, 1, 1} Very high = triangular{0.8, 1, 1}

Very low = triangular{0, 0, 0.23} Low = triangular{0, 0, 0.36} Medium = triangular{0.07, 0.47, 0.83} High = triangular{0.64, 1, 1} Very high = triangular{0.75, 1, 1}

 Low crossover rate raises objective function value and decreases algorithm efficiency. If crossover rate exceeds 0.9, it has the reverse effect on the objective function. After all, the crossover rate between 0.7 and 0.8 is assumed an appropriate rate. In the algorithm used in this article the crossover rate of 0.7 (Pc = 0.7) was drawn upon. It should be mentioned that OnePoint Crossover Method was applied.  Mutation rate impact on algorithm efficiency and objective function value in studied domain is not noticeable, but examining the maps it is expected that high mutation rate (about to 0.1) can reduce objective function. So it seems that the suitable mutation rate must be searched at the interval of (0.02, 0.03). In the algorithm applied in this article, mutation rate of 0.03 (Pm = 0.03) was adopted. Mutation method is of uniform type, as well. In order to assess results of the proposed model in oil product DS, proposed model and algorithms were written in Delphi software. Designed software was running for a month in the morning shift in one of oil product distribution regional fuel storage depot to allocate gasoline and diesel. At the end of pilot phase, studying informational records revealed that about 4126 conflict records had been saved in the system. In other words, of 17 613 times of allocating operations, proposed allocation priority of system has been in conflict 4126 times with the proposed allocation of operator. These figures show that about 23% of operator’s performance contradicted the system proposals. Based on the obtained results and learning algorithms referred to earlier in proposed model, the learning process of the system was run. The following results regarding two learnable problems in question were proposed by the system.  Rules weight: At Table 4, rules primary weight and corrected weight of the system rules are represented after learning.  Shape parameters of membership functions: Table 5 indicates primary membership function and values after system’s learning for output and input variables. After and during the learning process, the proposed system which was allocating two products of gasoline and diesel was

investigated for another month again in the morning shift. At the end of period of the study, informational records revealed that about 393 true conflict records had been saved. in other words, of 17 577 times of allocation operation about 393 times the proposed allocation priority of system conflicted with the operator’s allocation. The figures show that about 2. Two percent of operator’s performance went against the system proposals. As it is clear from analyzing the data and Table 6, it can be understood that proposed system was able to have considerable effects on ensuring fairness of order distribution indicator, reduction in the number of customers, agents and drivers’ complaints, decrease in the average waiting time of drivers and customers. Of other advantages of the proposed model we can refer to reducing the allocation time of the product to the oil tanker driver. Therefore, it naturally cuts the expected allocation time of the product and its submission and finally decreases a percentage of drivers, customers, and agents’ complaints. In the proposed system, given the reduction in the product allocation and drivers’ waiting time it can be expected that the operational loading capacity and response to the storage depot orders will increase satisfactorily. There can be then a downsizing in the number of operators in the allocation system; By and large, it results in reducing surplus costs of energy. This system makes it possible to change decision-making parameters rapidly (such as adding or changing decision-making rules, rules weight, membership function shape, etc.) and promotes the flexibility of the system and contributes to manage expert knowledge of DS. 5. Conclusion Multi-agent systems are initiatives of modern artificial intelligent advances which are developed in order to meet the requirements of the new world and are still evolving. In this article, one of the applications of these systems in SC modeling was studied and a new model to shape the interactions between existing agents of DS was proposed. Similarly, different methods of decision-making were studied and several suggestions were proposed based on conditions dominant over SC and DS. As it can be seen from previous studies, a limited number of tools are capable of establishing communication and coordination between processes and integrating information inside and outside levels of SC and DS. These tools, in turn, impose some limitations on SC to adapt themselves to environmental changes. On the basis of the analyses done in this article it is obvious that three particular attributes and capabilities required of an efficient tool in this case: 1. the extensive coordination and interaction with external environment and other chain components to increase flexibility 2. the possibility to gather and analyze information and makes decisions 3. learning and adaptation ability

Table 6 Performance comparison of proposed system. Indicator name

Amount at the first month

Amount at the second month

Amount at the third month

Number of answered orders in the morning shift Volume of submitted orders in the morning shift (million liter) Number of loaded oil-tanker Indicator of fair order distribution in the morning shift Number of customers Number of customer’s complaints per month Number of agents and driver’s complaints Average waiting times for drivers to load (minutes) Average waiting times for customers to receive order (minutes) Number of transport agencies Number of tankers in the morning shift Percentage of allocation system conflict

6286 420 627 71 79 32 57 43 280 34 194 –

5879 393 587 73 79 27 44 39 265 34 194 0.23

5869 392 584 78 79 19 28 22 230 34 194 0.022

10134

P. Hanafizadeh, M.H. Sherkat / Expert Systems with Applications 36 (2009) 10120–10134

The method which agents apply in their negotiations to adapt their strategies to supply chain changes are based on patterning of their agent’s own behavior so that they are able to perceive new changes in SC and analyze them by recalling previous learning and change their performance with their high flexibility and adaptive methods. The method proposed in this article for the adaptation includes change in agent policy in accordance with changes, awareness of changes in other agents’ policy and finally negotiation of cooperative agents at SC to complete information. This adaptive behavior of agents adapts SC to new changes; therefore, it can be said that SC based on MAS will survive the environmental changes. Also form assessing numerical results of applied system it can be concluded that the proposed model has effectively made it possible to rapidly change the decision-making parameters (such as adding or changing decision-making rules, rules’ weight, membership function shape) which leads to an increase in flexibility in DS and help to manage expert knowledge of these systems. References Alter, S. (1996). Information systems a management perspective (p. 114). Benjamin, Cummings Publishing Company. Agarwal, A., Shankar, R., & Tiwari, M. (2007). Modeling agility of supply chain. Industrial Marketing Management, 36, 443–457. Cicirello, V. (2001). A game-theoretic analysis of multi-agent systems for shop floor routing, Tech. Report, Robotics Institute, Carnegie Mellon University, September. Cruz, J. (2008). Dynamics of supply chain networks with corporate social responsibility through integrated environmental decision-making. European Journal of Operational Research, 184(3), 1005–1031. Chatzidimitriou, K., Symeonidis, A., Kontogounis, I., & Mitkas, P. (2007). Agent mertacor: A robust design for dealing with uncertainty and variation in SCM environments. Expert Systems with Applications (available online 26 July). Choy, K., Harry K., Chow, Tan, K., Chan, C., Mok, S., et al. (2007). Leveraging the supply chain flexibility of third party logistics – Hybrid knowledge-based system approach. Expert Systems with Applications (available online 17 September). Chen Chen, M., Yang, T., & Liam, H. (2007). Evaluating the supply chain performance of IT-based inter-enterprise collaboration. Information and Management, 44(6), 524–534. Che, Z., Wang, H., & Sha, D. (2007). A multi-criterion interaction-oriented model with proportional rule for designing supply chain networks. Expert Systems with Applications, 33(4), 1042–1053. Cicirello, V., & Smith, S. (2004). Wasp-like agents for distributed factory coordination. Autonomous Agents and Multi-Agent Systems, 8, 237–266. Ferber, J. (1999). Multi-agent systems. London: Addison-Wesley. Fox, M. (2000). Agent oriented supply chain management. The International Journal of Flexible Manufacturing Systems, 12, 165–188. Forget, P., Amours, S., & Frayret, J. (2007). Multi-behavior agent model for planning in supply chains: An application to the lumber industry. Robotics and ComputerIntegrated Manufacturing (available online 4 April). Gong, Z. (2008). An economic evaluation model of supply chain flexibility. European Journal of Operational Research, 184(2), 745–758. Hendry, L., Teunter, R., & Li, H. (2007). A strategic capacity allocation model for a complex supply chain: Formulation and solution approach comparison. International Journal of Production Economics (available online 24 March). Hinojosa, Y., Kalcsics, J., Nickel, S., Puerto, J., & Velten, S. (2008). Dynamic supply chain design with inventory. Computers and Operations Research, 35(2), 373–391. Hirai, K., & Mori, K. (2000). Autonomous assurance technique in distributed database. In International workshop on autonomous decentralized systems. Jennings, N. R., Sycara, K., & Woolridge, M. (1998). A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems, 1, 7–38. Jang, R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.

Kim, J., Chatfield, D., Harrison, T., & Hayya, J. (2005). Quantifying the bullwhip effect in a supply chain with stochastic lead time. European Journal of Operational Research, 1–20. Lin, C., Chiu, H., & Chu, P. (2006). Agility index in the supply chain. International Journal of Production Economics, 100(2), 285–299. Li, H., Hendry, L., & Teunter, R. (2007). A strategic capacity allocation model for a complex supply chain: Formulation and solution approach comparison. International Journal of Production Economics (available online 26 February). Longerman, J., & Ehlers, E. (1997). Agent-based airline scheduling. Computers and Industrial Engineering, 33(3-4), 849–852. Lou, P., Zhou, Z., Chen, Y., & Springor, W. (2003). Study on multi-agent based agile supply chain management. London. Lin, F., Tan, G., & Shaw, M. (1998). Modeling supply-chain networks by a multiagent system. In Proceeding of the Hawaii international conference on system sciences. Liang, W., & Huang, C. (2005). Agent-based demand forecast in multi-echelon supply chain. Decision Support Systems, 1–18. Labarthe, O., Espinasse, B., Ferrarini, A., & Montreuil, B. (2007). toward a methodological framework for agent-based modeling and simulation of supply chains in a mass customization context. Simulation Modeling Practice and Theory, 15, 113–136. Laua, H., Agussurjab, L., & Thangarajoob, R. (2007). Real-time supply chain control via multi-agent adjustable autonomy. Computers and Operations Research (available online). Mele, F., Guill’en, G., Espu~na, A., & Puigjaner, L. (2007). An agent-based approach for supply chain retrofitting under uncertainty. Computers and Chemical Engineering, 31, 722–735. Mallik, S., & Harker, P. T. (2004). Coordinating supply chains with competition: Capacity allocation in semiconductor manufacturing. European Journal of Operational Research, 159(2), 330–347. Newman, W., & Krehbiel, C. (2007). Linear performance pricing: A collaborative tool for focused supply cost reduction. Journal of Purchasing and Supply Management, 13(2), 152–165. Patel, C., Gunasekaran, A., & Mcgaughey, R. (2004). A framework for supply chain performance measurement. International Journal of Production Economics., 87, 333–347. Simchi, L., & Kaminski, P. (2000). Designing and managing the supply chain, concepts, strategies and case studies (International edition). Irwin McGraw-Hill Higher Education. Shang, J., Anling, L., & Tadikamallas, P. (2004). Operational design of a supply chain system using the Taghchi method, response surface methodology, simulation and optimization. Taylor and Francis Journal. Sadeh, T. (1998). Modeling supply chain dynamics: A multi agent approach. Journal of Decision Sciences, 29(3). Stach, W., Kurgan, L., Pedrycz, W., & Reformat, M. (2005). Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 153(3), 371–401. Scuricini, G. B. (1998). Complexity in large technological systems. In L. Peliti & A. Vulpiani (Eds.), Measures of complexity (pp. 83–102). Berlin: Springer-Verlag. Stone, P., & Veloso, M. (2000). Multi agent systems: A survey from a machine learning perspective. Autonomous Robots, 8, 345–383. Turksen, I. B., & Fazel Zarandi, M. H. (1999). Production planning and scheduling: Fuzzy and crisp approaches. Practical Applications of Fuzzy Technologies, 479–529. Teruaki, M. (2000). A blackboard-based negotiation for collaborative supply chain system. Journal of Material Processing Technology, 107, 398–403. White, A., Daniel, E., & Mohdzain, M. (2005). The role of emergent information technologies and systems in enabling supply chain agility. International Journal of Information Management, 25(5), 396–410. Weigel, D., & Cao, B. (1999). Applying GIS and OR techniques to solve sears technician-dispatching and home-delivery problems. Interfaces, 29(1), 112–130. Whiteson, S., & Stone, P. (2004). Adaptive job routing and scheduling. Engineering Applications of Artificial Intelligence, 17, 855–869. Wooldridge, M., & Jennings, N. R. (1995). Intelligent agent: theory and practice. The Knowledge Engineering Review, 10(2), 115–152. Weiss, G. (1999). Multi agent systems, a modern approach to distributed artificial intelligence. MIT Press. Xiangtong, O., Bard, F., & Ganng, Y. (2004). Supply chain coordination with demand disruptions. Omega the International Journal of Management Science, 32, 301–312. Zimmerman, H. J. (1996). Fuzzy set theory and its applications. Kluwer Academic Publishers.