SDMSim: A manufacturing service supply–demand matching simulator under cloud environment

Robotics and Computer-Integrated Manufacturing ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Robotics and Computer-Integrated Manufacturing journal homepage: www.elsevier.com/locate/rcim

SDMSim: A manufacturing service supply–demand matching simulator under cloud environment Fei Tao n, Jiangfeng Cheng, Ying Cheng, Shixin Gu, Tianyu Zheng, Hao Yang School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, PR China

art ic l e i nf o

a b s t r a c t

Article history: Received 15 September 2015 Received in revised form 5 June 2016 Accepted 31 July 2016

Nowadays, with the introduction and application of new information technologies in manufacturing, various advanced manufacturing modes and national strategies have been put forward and paid more and more attention, such as Industry 4.0, Industrial Internet, Cyber-Physical System or Cyber Manufacturing, Made in China 2025, Internet Plus Manufacturing, Cloud Manufacturing, etc. For these modes and strategies, how to realize the effective and intelligent supply–demand matching (SDM) of various manufacturing resources and capabilities (MR&C) in the form of service is one of the common issues and aims. In order to provide a uniformed research platform for related researchers both in academic and industry, the concept of manufacturing service SDM simulator (SDMSim) is proposed in this paper. A hypernetwork based architecture for the simulator is designed, as well as its seven key functions and subsystems, including manufacturing service management, manufacturing task management, manufacturing service SDM hypernetwork, manufacturing service SDM problem formulation and configuration, matching and scheduling algorithms/strategies selection and design, statistical analysis, and visualization. It illustrates that SDMSim has the potential to serve the users of manufacturing service provider, manufacturing service consumer, manufacturing service operator in the field of SoM, as well as the related researchers. & 2016 Elsevier Ltd. All rights reserved.

Keywords: Manufacturing service Supply–demand matching (SDM) Service-oriented manufacturing (SoM) Simulator Hypernetwork Cloud manufacturing

1. Introduction Along with the application of new information technologies (e.g., cloud computing [1,2], internet of things [3], big data [4,5], mobile internet, service-oriented technology [6], etc.) in manufacturing and industry, a lot of advanced manufacturing modes [7] or national strategies have been proposed recently. The representatives are Industry 4.0 [8], Industrial Internet [9], CyberPhysical System [10,11] or Cyber Manufacturing [12], Made in China 2025 [13], Internet Plus Manufacturing [14], Cloud Manufacturing [15–17], and so on. One of the common issues to be addressed by these modes or strategies is to realize the effective and intelligent supply–demand matching (SDM) of various manufacturing resources and capabilities (MR&C) in the form of services [18]. Furthermore, manufacturing service SDM is also a very important issue in manufacturing service management [19]. Many papers related to manufacturing service SDM have been published. After investigation, the existing research works can be primarily classified into the following three aspects: (a) problem modeling and analysis of manufacturing service SDM, including two main categories of modeling methods: service composition based modeling [20,21], and network-based modeling [22–24]; (b) n

Corresponding author.

Algorithms for addressing manufacturing service SDM problem, such as guiding search method [25–27], system dynamic evolution [28]; and (c) forecast and utility analysis of manufacturing service supply and demand, for the forecast, two categories are concluded, time series analysis based prediction [32–34], causal analysis based prediction [35,36]; for utility analysis, traditional statistical analysis method [29,30], and network based statistical analysis method [31] are concluded. After further investigation, it can be summarized that the general workflow for studying manufacturing service SDM primarily has the following four steps: Step 1: Analyze and formulate the problem. For a specific manufacturing service SDM problem or a class of problems, the corresponding variables, QoS (quality of service), matching rules, optimization objectives (single, multiple or compound) and constraints are firstly selected and formulated. Step 2:Design or select a suitable algorithm. Designing an entire new algorithm, choosing an existing algorithm, or improving an existing algorithm for addressing the given problem. Step 3:Perform simulation and comparative analysis. Performance of the employed algorithms for addressing the problem, results of the optimal objectives, are analyzed by employing a specific simulation tools provided by the third side or developed by the researchers themselves. Furthermore, the influence of different

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Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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variable values, matching rules and others factors on matching result are analyzed, as well as comparisons with other algorithms and methods. Step 4:Make statistical analysis and forecast for supply and demand. Service invoking rate, task execution rate, matching success rate, utility for different users, fault propagation and other statistical characteristics need to be analyzed. Forecast for supply and demand is also conducted. However, when a researcher, especially a new researcher, carries out the work on manufacturing service SDM, the following problems would be faced: Problem 1: For Step 1, there are two aspects of problem: (a) What's the input data? So much input data determines the specific problem formulation which includes the data of manufacturing services, manufacturing service request or manufacturing tasks, QoS of each service, objectives and constrains, and so on. However, it's difficult to get the actual data for many researchers, especially for new researchers. Without data, the specific formulation of a problem cannot be established. (b) Lack of configurability. The traditional formulation process is customized, which leads to low efficiency, poor flexibility, and high requirements to users, due to complexity and diversity of manufacturing service SDM. Problem 2: When it comes to Step 2, there are thousands of algorithms, but how to select a suitable algorithm to solve the problem established in Step1? It's difficult to test performance of all algorithms one by one. And it's also very difficult for one researcher to be familiar with all of the algorithms. Therefore, some tools for method of algorithm selection, algorithm intelligent recommendation, or algorithm automatic configuration are urgently needed. Problem 3: As to Step 3, the comparability of results is poor. Because different simulation and programming environment, different statistical methods, different models, and different algorithms are employed before generating the results. Consequently, the comparative result is unfair and lack of credibility. Therefore, a common simulation platform and some benchmarks are urgently required and very useful for researchers. Problem 4: With regard to Step 4, the result of statistical analysis and forecast for supply and demand is not enough convincing due to lacking of the support of enough available data (namely, big data), and lacking of comparison of a variety of statistical methods or forecast methods. Therefore, a common simulation platform is urgently needed, which is supposed to provide enough history data, various statistical methods and forecast methods, and various configurable statistical indicators. Considering the above issues, if there is a uniform simulator in manufacturing service management (just like Matlab in the field of engineering or CloudSim [37] in the field of cloud computing), which can provide simulation data of manufacturing service and task, enable users to select or load indicators (QoS and utility), objectives and constraints, support users to select or design algorithms, provide a common simulation environment and benchmark experiments for comparative analysis of simulation result, and provide rich statistical characteristics analysis and diverse visual presentation, then the above issues can be addressed in some content. Therefore, the aim of this paper is to design such a simulator in the field of manufacturing service management, namely manufacturing service supply–demand matching simulator (SDMSim), which is the main contribution of this paper. The remainder of this paper is organized as follows. In Section 2, the architecture of the proposed SDMSim is introduced. Seven key functions or subsystems for implementing the SDMSim are designed

and investigated in Section 3. A case is presented to validate and illustrate the workflow of the proposed SDMSim in Section 4. Section 5 concludes the work and points out the future works.

2. Architecture of the proposed SDMSim In order to solve the above summarized four problems, the concept of SDMSim is introduced in this paper. For the issue of SDM and scheduling, many methods have been investigated, e.g., the methods based on template, workflow, artificial intelligence, agent, service composition, graph theory, platform. However, the related studies are still weak for the issue both with the dynamic tasks and the dynamic services. In addition, there is almost no relevant systematic study for the following problems, e.g., how to do the effective analysis based on the current and historical data of matching, how to analyze the dynamic evolution and statistical characteristics [39]. With the view to the above troubles, the model and theory of hypernetwork have many advantages in dynamic analysis, forecasting, and mining of the multi-layers, multi-dimensions, multi-attributes, and multi-criterions networks [22]. Therefore, the proposed SDMSim is constructed based on the model of hypernetwork. The architecture of the proposed SDMSim is shown in Fig. 1, which primarily consists of seven subsystems, (1) manufacturing service management, (2) manufacturing task management, (3) manufacturing service SDM hypernetwork, (4) manufacturing service SDM problem formulation and configuration, (5) matching and scheduling algorithms/strategies selection and design, (6) statistical analysis, and (7) visualization. The main functions of each subsystem in the proposed SDMSim are as follows. (1) Manufacturing service management. It is primarily responsible for manufacturing service publishing and registration, service classification and aggregation, service library management, service selection, simulating the required or specific manufacturing service which is not available in the service library, manufacturing service network (S_Net) generation, S_Net dynamic evolution and service retrieve, and other service related statistics, analysis and visualization. (2) Manufacturing task management. The subsystem covers the following key functions, i.e., manufacturing task submission, simulating the required or specific manufacturing task which is not available in the library, manufacturing task modeling and decomposition, task library management, manufacturing task network (T_Net) generation, T_Net dynamic evolution and task retrieve, and other task related statistics, analysis and visualization. (3) Manufacturing service SDM hypernetwork. It is responsible for matching rules management and selection, mapping relationship between services and tasks management and selection, generation of manufacturing service supply–demand hypernetwork (Matching_Net), Matching_Net dynamic evolution, service and task retrieve, and other SDM related statistics, analysis and visualization. (4) Manufacturing service SDM problem formulation and configuration. The key functions of this subsystem primarily consist of configuration of matching environment (including initial state, implementation process), indicators (e.g., QoS and utility) management and selection, objectives and constraints management and selection, library management (including indicator library, optimization objective library, constraint library), simulating the required or specific indicators, objectives and constraints which are not available in the library. (5) Matching and scheduling algorithms/strategies selection and design. This subsystem aims to realize the following functions, i.e., establishment and management of algorithm library,

Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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Fig. 1. Architecture of the proposed SDMSim.

algorithm selection and configuration, designing specific algorithm which is not available in the library, initialization of the selected or designed algorithm according to the specific problem, and algorithm running visualization. (6) Statistical analysis. The following functions are supported in this subsystem, i.e. enterprises collaboration network (ECN) generation, statistical indicator library management, simulating the required or specific statistical indicator which is not available in the library, characteristics analysis both from the perspective of supply–demand layer (e.g., service invoking rate, task execution rate, matching success rate, coverage rate) and the perspective of enterprises layer (e.g., enterprise collaboration mode, value/utility-adding strategies, network nodes control, supply and demand forecasts), respectively, and diverse visual presentation of statistics. (7) Visualization. Rich visualization interfaces are provided in this subsystem, which are divided into the following five categories, network visualization, algorithm running and comparison visualization, matching and scheduling process visualization, statistical characteristics analysis visualization, and so forth. 3. Implementation of SDMSim As the descriptions aforementioned, there are seven key functions or subsystems in SDMSim. Detailed key functions and

flowchart of implementing each function or subsystem are illustrated in this section. 3.1. Manufacturing service management Manufacturing service providers can submit and publish various MR&C to SDMSim platform with the methods of MR&C digital description and multi-dimensional capability modeling. And then manufacturing service will be classified and aggregated. Afterwards, with the theory of complex networks, nodes of S_Net are established based on multi-dimensional attribute model of manufacturing services, and edges of S_Net are established based on relationship between manufacturing services. Then, the S_Net will be generated. Furthermore, in this subsystem, users can analyze the dynamic evolution of S_Net, Fig. 2 shows the key functions and flowchart of manufacturing service management subsystem. The workflow of this subsystem is divided into the following steps: Step1: SDMSim offers an interface for MR&C digital description and publishing. With the interface, the providers in SoM system can describe the detailed information of MR&C through selecting and loading relevant category, input and output information, processing capacity, etc. Step2: Adopting UML as object modeling tool, the description information obtained from step1 can be transformed into

Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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Fig. 2. Key functions and flowchart of manufacturing service management subsystem.

Fig. 3. Key functions and flowchart of manufacturing task management subsystem.

machine-readable description language. And then, multi-dimensional capability model of MR&C is established. Step3: After accomplishing the digital description and multidimensional capability modeling of MR&C, servitization and classification of various MR&C can be achieved based on the formalization in ontology language. On this basis, a common manufacturing service library (CMSLib) built with the database technology is provided to users. Users can retrieve services according to types of services, processing capacity characteristics, manufacturing times, reliability and other indicators. What's more, when user logins to the SDMSim to simulate the manufacturing service SDM, manufacturing services selected from CMSLib or created by user can be saved in the personal history service library (PHSLib). Step4: After accomplishing servitization of all kinds of MR&C, the service nodes are modeled. Through mining the relationship between manufacturing services, edges of S_Net can be modeled. Therefore, S_Net will be established. The mathematical model of S_Net is built in the authors’ previous work [22]. Moreover, this subsystem can realize self-reconfiguration and visualization of S_Net depending on users’ retrieve conditions. Step5: Through mining methods and rules of service aggregation and self-organization, dynamic evolution model of S_Net is established. Considering join-in and quit-out of services,

attributes changes of services, relationship changes between services, self-organization and aggregation of the various heterogeneous services of the social MR&C can be realized. In addition, users can retrieve information depending on different evolution characteristics. Besides, dynamic evolution of S_Net is visible to users. 3.2. Manufacturing task management This subsystem can solve the decomposition from a whole compound manufacturing task to a series of primitive subtasks (PSs) with semi-automatic mode or manual mode, and construct T_Net with specific combination order of a series of PSs. A common manufacturing task library (CMTLib) is built in this subsystem which can assist the user to decompose the compound task to a series of PSs on one hand, and can allow users to retrieve relevant tasks depending on different conditions on the other hand. The key functions and flowchart of manufacturing task management subsystem are shown in Fig. 3. The key workflow of this subsystem is divided into the following steps: Step1: With the method of multi-dimensional information description for manufacturing task, the SDMSim provides basic

Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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information supporting interface and compound manufacturing task decomposition interface. CMTLib containing a number of successful manufacturing cases is established in this subsystem. The interface can assist users to decompose the compound manufacturing tasks to a series of PSs through semi-automatic mode or manual mode. Besides, when user logins to the SDMSim to simulate the manufacturing service SDM, manufacturing tasks selected from CMTLib or created by user can be saved in the personal history task library (PHTLib). Step2: Users can define manufacturing requirements and processing sequence for each PS which includes the basic attributes and expected attributes, such as manufacturing period, manufacturing cost. Step3: After decomposing compound task and defining manufacturing requirements for each PS, nodes of T_Net are modeled. In addition, edges of T_Net are modeled based on defining processing sequence and mining relationship between PSs. And then T_Net is established through considering the submission characteristics and the aggregation method of tasks. Meanwhile, taking advantages of database technology, manufacturing tasks can be saved in the CMTLib. After generating T_Net, users can manually modify T_Net and obtain the topology visualization of T_Net. It needs two steps to construct the model of T_Net: the modeling from coarse-grained task layer to task decomposition layer, and the modeling from task decomposition layer to fine-grained network layer. The mathematical model of T_Net is built in the authors’ previous work [22]. Step4: Considering demand changes of tasks, failures of PSs, processing sequence changes of PSs, etc., T_Net is a dynamic network. The SDMSim establishes dynamic evolution model of T_Net and realizes dynamic management of manufacturing tasks. Analyzing various manufacturing tasks stored in the CMTLib with the theory of statistics, the demand in the future can be forecasted. This will guide operators in SoM system to adjust the appropriate policy, and guide providers to increase or reduce the supply of resources. 3.3. Manufacturing service SDM hypernetwork Manufacturing service SDM hypernetwork subsystem is mainly to model the mapping rules between nodes in S_Net and T_Net, and then result to the Matching_Net. With this subsystem, the user can obtain the intuitive Matching_Net topology. Fig. 4 shows the key functions and flowchart of manufacturing service SDM hypernetwork subsystem [38]. The workflow of this subsystem is divided into the following steps:

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Step1: Matching_Net can be structured through modeling the mapping relationship between nodes in S_Net and T_Net (e.g., edge's direction, weight, and corresponding relationship). In addition, the mapping rules and mapping types between nodes in S_Net and T_Net need to be set. The mapping types reflect the static or dynamic characteristics of supply and demand. Through setting specific mapping relationship, mapping types and mapping rules, different Matching_Net can be constructed. Matching_Net presents multi-dimensional relationship between nodes in S_Net and T_Net in an intuitive network topology way. The mathematical model of Matching_Net is built in the authors’ previous work [22]. Step2: After modeling dynamic evolution of S_Net and T_Net, the model of dynamic evolution of Matching_Net can also be established. This subsystem can effectively manage join-in and quit-out, attribute change and connecting relationship change of service nodes and task nodes. In addition, the dynamic evolution of Matching_Net is visible to users. 3.4. Manufacturing service SDM problem formulation and configuration This subsystem is mainly to configure matching environment, set matching rules, select or load matching indicators, and set matching optimization objectives and constraints. It contains three libraries, i.e., common service indicator library (CSILib), common optimization objective library (COOLib) and common optimization constraint library (COCLib), which allows users to select corresponding indicators, objectives and constraints. What's more, when user logins to the SDMSim to simulate the manufacturing service SDM, another three libraries will be generated, i.e., personal history indicator library (PHILib), personal history objective library (PHOLib) and personal history constraints library (PHCLib). The key functions and flowchart of manufacturing service SDM problem formulation and configuration subsystem are shown as Fig. 5. The workflow of this subsystem covers the following steps: Step1: Due to the complexity and diversity of manufacturing service SDM problem, the supply and demand are likely to be static or dynamic, certain or uncertain. Therefore, users need to firstly configure the SDM environment, which includes two parts: a) configuration of initial state, e.g., uncertain supply, certain supply, uncertain demand and certain demand; b) configuration of implementation process, e.g., static supply, dynamic supply, static demand and dynamic demand. Besides,

Fig. 4. Key functions and flowchart of manufacturing service SDM hypernetwork subsystem.

Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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Fig. 5. Key functions and flowchart of manufacturing service SDM problem formulation and configuration subsystem.

matching rules also need to be configured, such as one task can be allocated at most only one service, or one task can be allocated by multiple services. Step2: After configuring SDM environment, users need to set optimization objectives through selecting from COOLib or PHOLib, or loading user-defined optimization objectives. SDM optimization modeling contains two aspects, one is configuration of optimization objectives and constraints based on optimization combination of indicators, and the other is utility modeling for operators, consumers and providers in SoM systems. Step3: Constraints also need to be configured. Users can select corresponding constraints from COCLib or PHCLib. Meanwhile, this subsystem provides a software programming interface which allows users to load user-defined matching constraints. 3.5. Matching and scheduling algorithm/strategies selection and design The function of matching and scheduling algorithm/strategies selection and design subsystem is mainly to solve the multiple objective optimizations. A common optimization algorithm library (COALib) exists in this subsystem, and users can select existing algorithms and load their defined algorithms. What's more, when

user logins to the SDMSim to simulate the manufacturing service SDM, algorithm selected from COALib or created by user can be saved in personal history algorithm library (PHALib). Fig. 6 shows the key functions and flowchart of matching and scheduling algorithm/strategies selection and design subsystem. The workflow of this subsystem is divided into the following steps: Step1: Algorithms are used to solve the model of manufacturing service SDM and need to be associated with parameters, variables of SDM models firstly. As a consequence, an interface is designed, which allows users to read parameters from SDM model, such as matching indicators, optimization objectives, constraints. And then initial conditions of algorithm execution can be configured. Step2: In order to facilitate users to select algorithm, get the source codes of algorithm and configure initial conditions of algorithm execution, the COALib and PHALib are developed in SDMSim platform. Furthermore, as an open simulation platform, this subsystem contains a software programing interface for users to load their own intelligent algorithms. Step3: When algorithms run in SDMSim, the solving process will be presented to users in an intuitive way. The visualization of this subsystem contains matching process visualization and

Fig. 6. Key functions and flowchart of matching and scheduling algorithm/strategies selection and design subsystem.

Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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Fig. 7. Key functions and flowchart of statistical analysis subsystem.

matching result visualization. Moreover, users can easily compare the performance of user-defined algorithms with existing algorithms and find out the feature. 3.6. Statistical analysis Matching_Net is characterized by self-organizing, self-similar, small world, scale-scale, etc. Statistical characteristics of Matching_Net can be analyzed in this subsystem through figuring out network static and dynamic characteristics from both supply–demand layer and enterprises layer. On this basis, valuable information is provided to users, including enterprise collaboration mode, value/utility-adding strategies, network nodes control and optimization strategies, supply and demand forecasts, etc. Fig. 7 shows the key functions and flowchart of statistical analysis subsystem. The workflow of this subsystem includes the following steps: Step1: The results of SDM reflect the actual implementation activities of social services. Firstly, this subsystem builds the actual manufacturing ECN through investigating mapping mechanisms between service nodes and task nodes. Step2: Statistical characteristics is analyzed from the static and dynamic perspective of Matching_Net and ECN, including degree distribution, nodes clustering coefficient, average clustering coefficient of networks, shortest path, average path, betweenness, core number, robustness and network pattern, and so on. Therefore, this subsystem opens the relevant information of network models and provides a software programing interface, thus users can load user-defined statistics indicators. Step3: After obtaining the relevant statistical characteristics of Matching_Net and ECN, users can analyze manufacturing data from two perspectives of supply–demand layer and enterprises layer. In terms of supply–demand layer, users can figure out service invoking rate, task execution rate, matching success rate, coverage rate, matching result reliability, network fault propagation, and so on. Similarity, in terms of enterprises layer, it is possible to figure out enterprise cooperation mode, value/utility-adding strategies of service, network node control and

optimization strategies, supply and demand forecasts, and so on. By analyzing the above information, it can help providers, consumers and operators in SoM system solve the following three kinds of problems: (a) for providers, what kind of service should be provided, how to protect the enterprise’s core services, how to develop new services based on the core services; (b) for consumers, how to reduce the cost, how to complete manufacturing tasks with high-quality and cost efficiency; (c) for operators, how to dynamically match supply and demand, how to improve the efficiency of resource allocation of the whole system, etc.

3.7. Visualization Visualization subsystem is to realize the visualization of the process and results of the aforementioned six subsystems, so as to help users get intuitive experience and more convenient understanding. It contains five categories of visualization, namely, network visualization, algorithm running and comparison visualization, matching and scheduling process visualization, statistical characteristics analysis visualization and other function visualization, as shown in Fig. 8. 3.7.1. Network visualization It mainly includes S_Net visualization, T_Net visualization and Matching_Net visualization, which presents network topology depending on different retrieve conditions (e.g., category of resource services, processing size and matching accuracy). Meanwhile, the network topology in the proposed SDMSim supports mouse drag and can be observed more clearly and more intuitively. 3.7.2. Algorithms running and comparison visualization It includes the process of algorithms running visualization, SDM algorithms comparison from different perspectives, optimization objectives and constraints changes curve in the process of matching algorithm.

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Fig. 8. Key functions of visualization subsystem.

3.7.3. Matching and scheduling process visualization There are several aspects in this category, for instance, matching results visualization, matching and scheduling process visualization, comparison between actual and expected constraint indicators of manufacturing tasks, such as manufacturing time, cost, reliability, energy consumption and the other customized indicators. 3.7.4. Statistical characteristics analysis visualization It is mainly used to reflect the results of statistical analysis, including service invoking rate, task execution rate, matching success rate, coverage rate, matching result reliability, network fault propagation, enterprise cooperation mode, value/utility-adding strategies of service, network node control and optimization strategies, supply and demand forecasts. Other relevant statistical analysis results can also be provided to users in the form of visualization. 3.7.5. Others function visualization The other supplemented information can also be analyzed and brought out, such as manufacturing services geographical distribution visualization, history data visualization, etc.

4. Prototype System and Validation of SDMSim An prototype system was developed by the authors' group. To illustrate the workflow of the proposed SDMSim, a case study for manufacturing service SDM for a mobile phone is illustrated to validate the effectiveness of its each subsystem in the developed prototype system. It is assumed that the three processes (or tasks), i.e., module supplier selection, assembly and test, in manufacturing a mobile phone are implemented by invoking various manufacturing services in a networked service platform (e.g., a cloud manufacturing service platform). As shown in Table 1, each process (or task) can be decomposed into (or consists of) a series of PSs (in this paper the PS is defined as a task which can be implemented by invoking only one service), and there are 35 PSs in this case. Meanwhile, it is assumed that 20 enterprises (in this paper, the kth (k ¼1, 2,…,20) enterprise is denoted as ek) can provide totally 100 different candidate manufacturing services (CMSs) for implementing the above 35 PSs. The number of the CMSs owned or provided by ek is denoted as nk, and each CMS is denoted as sj (j¼1,2,…,100). The detailed number and affiliation of CMSs for

Table 1 The 35 PSs for a mobile phone in the three processes. Process

PSs (denoted as tn (n ¼ 1,2, …, 35))

Module supplier selection

front camera (t1) screen (t7) operating system (t13)

rear camera (t2) communication module (t8) adapter (t14)

shell (t3) processor (t9) data cable (t15)

gyroscope (t19)

camera flash (t20)

fingerprint recognition (t21) shell assembly (t27)

Assembly Test

semi-finished products prePCBA components diction (t26) welding (t25) IQC material inspection visual inspection (t32) (t31)

MMI engineering testing (t33)

antenna (t4) RAM (t10) gravity sensor (t16) electronic compass (t22) play screws (t28) coupling test (t34)

speaker (t5) ROM (t11) light sensor (t17) NFC module (t23) clean and PE packaging (t29) QA test (t35)

battery (t6) GPS module (t12) proximity sensor (t18) buttons (t24) labeling and string yards (t30)

Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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Table 2 The quantities of candidate manufacturing service for the 20 enterprises. ek nk sj ek nk sj

e1 6 s1–s6 e11 5 s48–s52

e2 4 s7–s10 e12 4 s53–s56

e3 3 s11–s13 e13 3 s57–s59

e4 6 s14–s19 e14 6 s60–s65

e5 5 s20–s24 e15 4 s66–s69

each enterprise in this case study are shown in Table 2. Due to the purpose of this experiment, other parameters are defined and not presented in detail in this paper, including the specific requirements for each PS, the parameters, attributes and capability of each manufacturing service, and the correlations between the 100 manufacturing services and 35 PSs, and so forth. It is assumed that the user in this case study already has an account in the proposed SDMSim. When the user logins to the SDMSim to simulate the manufacturing service SDM for this case using its account, it is found that 42 CMSs already exist in the CMSLib, and 35 related CMSs already exist in its PHSLib, while 9 services are contained in both of the two libraries. Therefore, when the user builds the S_Net for the case, the above 68 different CMRs can be selected directly as shown in Fig. 9(a-1). The other different 32 CMSs are created (or simulated) by employing the function provided by the manufacturing service management subsystem in the SDMSim, as shown in Fig. 9(a-2). After all the CMSs are selected and generated, according to the defined relationship between services, the S_Net for this case is generated as shown in Fig. 9(a-3).

e6 6 s25–s30 e16 6 s70–s75

e7 5 s31–s35 e17 7 s76–s82

e8 4 s36–s39 e18 5 s83–s87

e9 3 s40–s42 e19 6 s88–s93

e10 5 s43–s47 e20 7 s94–s100

The same way, while building the T_Net for this case study, there are 21 PSs already existing in the PHTLib, which are selected and edited directly, as shown in Fig. 9(b-1). The other 14 PSs, which don’t exist in CMTLib, are created (or simulated) by employing the function provided by the manufacturing task management subsystem in the SDMSim, as shown in Fig. 9(b-2). After all the PSs are selected or generated, according to the defined relationship between tasks, the T_Net for this case study is generated as shown in Fig. 9(b-3). After the S_Net and T_Net are established, by setting the mapping relationship between nodes in S_Net and T_Net (e.g., edge's direction, weight, and corresponding relationship) according to the specific requirements in this case study (such as ‘one to one’, ‘one to multiple’, ‘one to multiple’, ‘multiple to one’, and ‘multiple to multiple’), then the Matching_Net for the case is generated as shown in Fig. 9(c). After the Matching_Net is established, the following is to define and set the specific matching environment, rules, objectives, constraints to formulate the specific problem in this case study. All the PSs and CMSs are assumed to be static and determined. Then,

Fig. 9. Generation of the manufacturing service SDM hypernetwork for this case study.

Please cite this article as: F. Tao, et al., SDMSim: A manufacturing service supply–demand matching simulator under cloud environment, Robotics and Computer Integrated Manufacturing (2016), http://dx.doi.org/10.1016/j.rcim.2016.07.001i

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corresponding SDM rules are set, which consist of: (a) one task can be allocated at most only one service; b) one service can be invoked by at most only one task, as shown in Fig. 10(a-1). While configuring manufacturing service SDM indicators for this case study, four indicators, i.e., Time, Cost, Satisfaction, and Risk existing in the CSILib and PHILib are selected, and other indicators, i.e., Maintainability, and Trust, which are not in the CSILib and PHILib, are defined and created, as shown in Fig. 10(a-2). While setting optimization objectives, three objectives can be selected from COOLib and PHOLib, i.e., minimizing Time, minimizing Cost, maximizing Trust, and one objective needs to be created by user, i.e., minimizing Risk. Meanwhile, the optimization constraints of SDM are set, i.e., Satisfaction (select from COCLib) and Maintainability

(select from PHCLib), which cannot be less than the minimum requirements (i.e., 0.85, 0.70) of PSs, as shown in Fig. 10(a-3). After the objectives and constraints for the problem are configured, the next is to design or select an algorithm to address it. In this case study, instead of selecting the algorithms existing in the COALib and PHALib, an improved particle swarm optimization (PSO) algorithm is uploaded and invoked to address the problem, as shown in Fig. 10(b). After finished the above configuration, the result of the manufacturing service SDM in this case study can be obtained by running the simulator. The visualization of SDM result can be seen in Fig. 11(a), which includes network of SDM result of this case study and task execution Gantt chart.

Fig. 10. The workflow and rendering of SDM problem formulation and configuration and algorithms/strategies selection and design.

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Fig. 11. The workflow and rendering of statistical analysis and visualization.

What's more, based on the simulation result and the characteristics of Matching_Net, the number and proportion of invoked service in enterprise, and task event river are analyzed. As illustrated in Fig. 11(a), the maximum proportion of invoked service appears in enterprise e6, and the corresponding proportion is 14.29%; the minimum proportion of invoked service appears in enterprise e18 and e19, and the proportion is 0.0%, namely that of no CMSs invoked. Meanwhile, through employing visualization methods, SDM result of this case and task execution Gantt chart are displayed, as shown in Fig. 11(b). Lastly, SDM result of this case study is shown in Table 3, including the service invoked by each manufacturing PS, the

manufacturing execution Time (i.e., start/end time and manufacturing period), corresponding Cost (i.e., conversion cost, transportation cost, maintenance cost), Risk, Trust, Satisfaction and Maintainability. Judging from the SDM result, the manufacturing period of this case study is 28 days, total cost for manufacturing a mobile phone is 1,993 RMB, risk is 0.056, trust is 0.85.

5. Conclusions and future work With the recognition of the importance of servitization and socialization in manufacturing, more and more researches on

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Table 3 Detailed SDM result in this case study. PSs

Invoked service

Time Start time

End time

Period /day

Cost/￥RMB

Risk

Trust

Satis-faction

Main-tainability

Module supplier selection

t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 t13 t14 t15 t16 t17 t18 t19 t20 t21 t22 t23 t24

s14 s23 s66 s29 s13 s58 s59 s49 s41 s46 s96 s54 s56 s4 s30 s77 s8 s24 s18 s34 s80 s38 s26 s2

2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1 2014/12/1

2014/12/5 2014/12/5 2014/12/7 2014/12/4 2014/12/9 2014/12/15 2014/12/16 2014/12/10 2014/12/9 2014/12/8 2014/12/6 2014/12/5 2014/12/2 2014/12/7 2014/12/8 2014/12/9 2014/12/5 2014/12/10 2014/12/6 2014/12/9 2014/12/11 2014/12/8 2014/12/10 2014/12/6

5 5 7 4 9 15 16 10 9 8 6 5 2 7 8 9 5 10 6 9 11 8 10 6

52 42 15 13 6 52 194 266 57 175 75 84 58 12 13 38 22 33 77 42 63 53 41 4

0.05 0.06 0.07 0.04 0.04 0.08 0.10 0.03 0.06 0.05 0.04 0.05 0.00 0.08 0.04 0.05 0.07 0.05 0.06 0.07 0.05 0.07 0.05 0.06

0.83 0.80 0.88 0.84 0.77 0.92 0.97 0.84 0.79 0.86 0.89 0.82 0.93 0.90 0.88 0.75 0.83 0.88 0.85 0.80 0.91 0.91 0.93 0.87

0.95 0.93 0.87 0.95 0.92 0.91 0.91 0.93 0.86 0.92 0.87 0.96 0.94 0.95 0.92 0.90 0.90 0.93 0.90 0.93 0.93 0.94 0.88 0.97

0.76 0.78 0.85 0.93 0.82 0.72 0.89 0.76 0.95 0.85 0.80 0.78 0.98 0.84 0.79 0.90 0.81 0.72 0.86 0.73 0.74 0.80 0.75 0.92

Assembly

t25 t26 t27 t28 t29 t30

s28 s25 s99 s63 s71 s50

2014/12/11 2014/12/13 2014/12/15 2014/12/16 2014/12/19 2014/12/21

2014/12/20 2014/12/15 2014/12/23 2014/12/19 2014/12/21 2014/12/23

10 3 9 4 3 3

240 50 10 8 23 12

0.09 0.12 0.03 0.05 0.06 0.05

0.79 0.82 0.76 0.86 0.84 0.94

0.89 0.92 0.94 0.86 0.93 0.94

0.78 0.77 0.70 0.76 0.78 0.85

Test

t31 t32 t33 t34 t35

s53 s40 s42 s15 s37

2014/12/19 2014/12/22 2014/12/23 2014/12/24 2014/12/25 28

2014/12/26 2014/12/26 2014/12/27 2014/12/28 2014/12/28

8 5 5 5 4

15 5 10 15 20 1993

0.07 0.06 0.05 0.06 0.07 0.056

0.86 0.84 0.82 0.88 0.85 0.85

0.85 0.93 0.93 0.89 0.93

0.81 0.73 0.87 0.86 0.90

Optimization objectives

manufacturing service SDM are carried out as one of the hot topics. After investing the existing research works and shortness on manufacturing service SDM, a manufacturing service SDM simulator (SDMSim) based on the models of hypernetwork is proposed in this paper. It makes it possible to enable modeling, simulation, evaluation and statistical analysis for the process and results of manufacturing service SDM and scheduling. The detailed contributions of this paper are concluded as follows:

can analyze what kind of service should be provided, how to protect their core services, how to develop new services based on the core services. Besides, consumers can analyze how to reduce the cost, how to complete manufacturing tasks with high-quality and low-cost. What's more, operators can analyze how to dynamically match supply and demand, how to improve the efficiency of resource allocation of the whole system.

(1) Based on the proposed framework of SDMSim, seven subsystems and the corresponding implementation of each subsystem of SDMSim are designed in details. (2) After introducing SDMSim into the academic research process, a configurable research methodology based on SDMSim is derived, which is helpful for researchers of different level and different research direction. By using SDMSim, researchers can configure or customize data sources, matching indicators, matching rules, matching objectives, optimization algorithms and visualization forms. (3) Besides, it provides different researchers with a potential common simulation platform and a public environment to acquire real manufacturing data, provide visual interfaces in an intuitive way, and make comparison of the accuracy of matching models and the performance of optimization algorithms. (4) As to the different users in SOM systems (i.e., service providers, consumers and operators), with the help of statistical characteristics of Matching_Net modeled in SDMSim, providers

In addition, the authors’ group has carried out relevant theoretical researches, including Matching_Net modeling, SDM optimization based on hypernetwork, manufacturing service management, as well as software development of basic functions of SDMSim. Further works will be carried out from the following aspects: (1) It is necessary to implement all components, functions, and key technologies of SDMSim. (2) SDMSim is supposed to be enriched to support requisite dynamic evolution and statistical characteristics analysis.

Acknowledgements This work is financially supported in part by National Natural Science Foundation of China (NSFC) under Grants 51522501 and 51475032, Beijing Nova Program and Beijing Natural Science Foundation (No. 4152032) in China.

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