A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

Robotics and Computer Integrated Manufacturing 61 (2020) 101841

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

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A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

T

Cheng Qiana,b, Yingfeng Zhanga,b, , Chen Jianga, Shenle Panc, Yiming Rongb ⁎

a

Department of Industrial Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, PR China c Centre de Gestion Scientifique, Mines ParisTech, 75272 Paris, France b

ARTICLE INFO

ABSTRACT

Keywords: Collaborative manufacturing Fixed-position assembly systems Dynamic optimization Resource configuration Smart manufacturing

Assembly stations are important hubs that connect massive material, information, human labor, etc. The fixedposition assembly systems for complex products may deal with hundreds of thousands of processes, making them vulnerable to manufacturing exceptions. Many scheduling problems were described and solved in the past decades, however, the gap between theoretical models and industrial practices still exist. To achieve a practical method for the dynamic scheduling in case of exceptions while reducing the impact brought by the exceptions, an Intelligent Collaborative Mechanism (ICM) was proposed where negotiations on resource configuration may happen among tasks (i.e. assembly processes). The intercommunication among resources was guaranteed by the data-driven ICM framework. The Petri-net-based workflow analysis and the constraint matrix can pick out the tasks that are currently not bound by other ones. The dynamic priority of the processes was defined and obtained using grey relational analysis. The matching strategy among the selected tasks and operators can provide a scheduling plan that is close to the initial plan, so the assembly systems may remain effective even when exceptions occur. The proposed models were analyzed in a case scenario, where the impact brought by exceptions can decrease by 44.3% in terms of the operators’ utilization rate, and by 60.26% in terms of the assembly time. This research has provided a practical strategy to improve the flexibility and effectiveness of assembly systems for complex products.

1. Introduction With the increasing competition in the global marketplace, manufacturing companies have to respond quickly to business changes. The agile and efficient assembly systems play a vital role for this purpose, especially in the fixed-position assembly systems that process complex products. E.g., it is of great importance for the aircraft manufacturers to study the methods to reduce the process planning time of assembly, so as to stay competitive [1]. Therefore, manufacturing enterprises are trying to establish an optimization mechanism for assembly processes to tackle the problems in the current systems, such as the insufficient workflow management and the belated control of the operations, which may arouse from the frequent changes in orders and market requirements. The market change may possibly lead to more serious problems for the manufacturers that assemble large-scared and complex products. Although some production management systems have been introduced

to enhance the ability of process monitoring and control, e.g. the Manufacturing Execution System (MES) [2], the automated assembly planning system [3], it is still difficult to significantly improve the resource efficiency due to the unexpected situations during the execution stage (such as the delayed delivery of components, the unplanned absence of operators, etc.), which are not rare in real-world situations, especially in the assembly stations with fixed-position layouts and mainly based on manual operations. In fact, the advanced planning and scheduling (APS) systems are available in many medium or large sized manufacturing enterprises, yet the actual effectiveness was limited due to the frequent changes and the belated information collecting during manufacturing. Since the related sensors have not been deployed, their process monitoring generally relies on daily reports, which also implies their APS systems are only used to generate the material requirement plan and processing schedules for the near future (because the instantaneity of data collection cannot support the timely update from

Conflict of interest: We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. ⁎ Corresponding author. E-mail address: [email protected] (Y. Zhang). https://doi.org/10.1016/j.rcim.2019.101841 Received 12 February 2019; Received in revised form 17 July 2019; Accepted 17 July 2019 0736-5845/ © 2019 Elsevier Ltd. All rights reserved.

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APS). Therefore, a major obstacle that prevents the fully functioning of the current systems is that the information of different stages or stations in the assembly process is usually isolated among each other. Each assembly station or operator locates in an information island, which limits their whole consciousness and affects the timeliness for the data collection and information sharing among stations. As a result, the assembly system cannot identify and respond to the exceptions accurately and timely. Once an exception occurs in an upstream station, its effect will generally spread to the whole system over time due to the lack of a well-functioning mechanism for the fault identification and handling. Besides, the fixed-position assembly of complex products includes many procedures, where the precedence and many other constraints may apply among the procedures. Therefore, the current manufacturing enterprises claimed it was not practical to deal with most of the changes in order or other exceptional events based on APS and MES, as the parameter setting, data processing, and plan generation could be more time consuming compared to the time used to fix the problems themselves by the management experience. In addition, the rescheduling model may provide a completely different schedule during exception compared to the initial one. However, it is not easy to change the tasks of so many work stations and prepare the necessary resources simultaneously and in a short time. Recently, the technological advances in the Internet of Things (IoT) [4], Cloud Computing (CC) [5], and the industrial practices in the assembly scheduling systems have provided a solid foundation to implement the real-time monitoring and optimization in assembly systems, including the data collection of manufacturing resources [6], the realtime monitoring of the assembly execution systems [7], the dynamic optimization of the manufacturing processes [8], etc. These works have provided the technological basis for the intelligent assembly systems. Based on the general principles of these achievements, this research specifically studied the fixed-position assembly systems, aiming to improve the effectiveness and efficiency of the MES systems in the assembly stage, so that some practical tools can be provided to tackle the real-world problems summarized from the industry. The following research questions are listed in this regard.

observed, the affected tasks can be reassigned to eligible operators to minimize the influences brought by it. It is emphasized that the proposed collaborative mechanism deals with the rescheduling of only the affected tasks while keeps monitoring other unaffected processes, aiming to ensure that they still follow the initial plan. If the resource configuration of the affected processes can be updated without causing additional conflicts to the assembly system, the assembly process resumes the initial plan. The main idea is to limit the impact brought by exceptions and execute the rescheduling processes only in a local range. Therefore, the feasibility and practicability can be guaranteed as the rescheduling process is simple and will not affect most stations in the assembly system. Applying ICM in the fixed-position assembly systems for complex products can increase the transparency and efficiency of production while reducing or eliminating the adverse effects from faulty operations. The rest of the paper is organized as follows. Section 2 reviewed the related literature and analyzed the gaps. Section 3 presented the design of the overall architecture of ICM. The three key models and methods in the collaboration were discussed in Section 4, namely the Petri net modeling technique, the constraint matrix, and the hierarchy structure of the task pool. Section 5 described the optimization algorithm and the calculating procedures. In Section 6, a demonstrated scenario is discussed to illustrate the implementation of the proposed ICM. The conclusions and future works were given in Section 7. 2. Literature review There are two steams of literature that are closely related to the advanced assembly systems, i.e. (1) the enabling technologies and applications of the advanced assembly systems; (2) the dynamic scheduling algorithms of assembly systems. 2.1. The enabling technologies and applications of the advanced assembly systems Lots of emerging theories and technologies have been applied in manufacturing over the last decades, e.g. IoT, Multi-Agent System (MAS), Petri net, big data analytics, etc. Featured with the capability of collaboration among discrete resources with high autonomy and interactivity, MAS provided an ideal architecture for ICM in assembly systems. Some interaction protocols were studied so that MAS can be applied in the management of the Flexible Manufacturing System (FMS) [9]. The MAS has been developed by Bellifemine et al. through the formation of a middle-ware in Java Agent Development Framework (JADE) [10]. The specific approaches for the negotiation and consultation to resolve contradictions and conflicts between agents were proposed by Wang et al. and a multi-agent negotiation strategy based on cost balance was given in [11]. Based on the MAS and cyber-physical system, Zhang et al. proposed the architecture for an intelligent shopfloor with self-organizing and self-adaptive capabilities [12]. The applications of MAS in industries can be found in many fields, such as contain online monitoring [13], production planning and control [14], etc. Petri net was generally used to model the process-level behaviors [15] and conduct fault diagnosis [16] in the FMS. Tian et al. applied the Petri net to the model the collaborative works, presenting a negotiation mechanism where the task can be distributed dynamically and the resources can be allocated according to the ability of a member [17]. As the theoretical foundation, the RFID-based colored Petri net modeling method and a hierarchical colored Petri net were also applied to monitor the production quality [18] and to model the priority-based distributed manufacturing process [19]. A system-level analysis in FMS was conducted by Baruwa et al. using the colored Petri net to identify patterns and improve a search-based scheduling algorithm [20]. Besides, Petri net also helped in developing the theories of MAS in that the time dimension among the interactions of agents was formally modeled

(1) It is important to analyze and improve collaboration among assembly stations or operators. Currently, the assembly process of complex products usually involves lots of manual operations. The robotic arms are also available in some stations to guarantee product quality and improve efficiency. However, the information exchange among the manual and/or automation systems is inconvenient, which may postpone critical decision-making processes and worsen the adverse effect from just a minor faulty operation. (2) A combination of the global resource configuration and the local rescheduling should be further studied to support a practical tool in the industry. Since the fixed-position assembly of complex products includes lots of tasks/sub-tasks and resources, it is rather difficult and time-consuming to reschedule on a global level of the manufacturing system. Instead, a combination of local adjustments and the globally-optimized initial plan may fit better for industrial production. (3) A comprehensive mechanism for task assignment in the assembly systems is necessary. The dynamic scheduling problem includes operators and robotic arms that have different capabilities. Also, their capability and availability (which are important constraints in the scheduling problem) may change over time. To address these questions, this research investigated an Intelligent Collaborative Mechanism (ICM) to improve the process-planning ability during the fixed-position assembly of complex products. ICM is designed to intervene in the process of resource configuration when the original scheduling plan cannot be followed due to system malfunctioning or order change. Specifically, ICM can monitor the processes under normal conditions. If a deviation from the initial plan is 2

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by Boukredera et al. [21]. Recently, the theories related to IoT and their applications have been developing rapidly due to the maturity of information/sensing technology, including the smart gateways, Radio Frequency Identification (RFID), ZigBee, Bluetooth, etc. [22]. Lots of advantages and potentials in RFID-based manufacturing systems were discussed by Günther et al. [23] and Luo et al. [24], etc. Apart from RFID, technologies such as smart gateways [25], Bluetooth [26], etc. were also studied and implemented to improve the industrial systems. Based on these technologies, a learning-by-demonstration technique was investigated by Duque et al. to help with the automatic programming of robots at assembly stations [15]. Besides, the training to operators of the advanced assembly systems can be conducted using virtual reality and data mining [27]. In addition, an assembly process monitoring system was implemented in the advanced assembly systems to prevent potential damages to robotics and processing parts caused by unexpected conditions of assemblies [28].

3. The overall architecture of the real-time data-driven ICM The aim of this research is to provide an intelligent collaborative mechanism for the assembly systems in the fixed-position layout, so that the exceptions resulted from lacking the real-time data and collaboration among assembly processes can be greatly reduced. Besides, the preplanned assembly schedule of the whole product will be improved at each assembly station (i.e. process-level, local optimization) according to the real-time progress and status of the stations, while not deviating too much from the preplanned schedule to ensure the global efficiency. The overall architecture of the proposed ICM is shown in Fig. 1. The hierarchical structure includes four layers from bottom to top, which are functionally divided according to their roles in the collaboration. The first layer is shown at the bottom of Fig. 1, which is responsible to collect the real-time data related to assembly and manage the information from multiple sensors or interfaces. The RFID readers and the Near Field Communication (NFC) chips can capture the arrival of assemblies, tools, operators, etc. Such data can be transmitted to the upper-level functional models and services through ethernet or industrial Internet. The TCP/UDP protocols, the RS-485, IEEE 802.11 standards, the Narrow Band IoT (NB-IoT) technologies, etc. can be selected in the solution according to the specific system requirements on reliability, realtimeness, type of the collected data. Based on the requirements of data collection and the monitoring targets, the selection of sensors is conducted aiming to provide sufficient and reliable information sources with a limited budget. Proper standards of data (i.e. the data formats) should be selected according to the data structure, requirements for instantaneity, etc. A library of drivers may also be necessary to access data from some hardware devices. The data flow among the sensors, interfaces, Petri-net-based workflow management models, databases, and other enterprise information systems is illustrated in the previous work [39]. The instantaneity of this layer can be decided by comprehensive research of the monitoring requirements and the implementation cost, as the ICM itself is driven by discrete events. Therefore, the concept of “real-time” mentioned in this research is a relative concept as compared to the daily or weekly production control at present. The second layer is responsible to monitor the assembly process by comparing the actual production data and the initial schedule. The hierarchical event structure including primary event, basic event, complex event, and critical event, is introduced in the second layer. Four examples of the corresponding level of events are the update of the raw data from sensors, the state change of a machine, the task assignment within multiple assembly stations, and an exceptional event that requires rescheduling. A higher-level event can be composed of many primary and basic events. The state monitoring module can identify any deviations from plan and trigger a “collaboration” among resources (i.e. rescheduling). The Petri net modeling technique and the hierarchical event structure are applied to classify the numerous assembly events and focus on the critical ones, which are generally exceptions and may lead to rescheduling of the tasks. These events combined with the possible solutions (provided alongside the initial plan) will be sent to the next layer and drive the autonomous operations of ICM. Details of the state monitoring based on Petri net can be found in [40]. The first two layers construct the data infrastructure and guarantee the proactive sensing, intercommunication, and interoperation of manufacturing resources, helping to realize the intelligent and autonomous decisionmaking during collaborations. The third layer contains the descriptive models of the tasks and resources. In the preparing stage of the task-resource matching, the status of resources (e.g. operators, tools, etc.) are examined to confirm their availability and recent performance. Also, the constraints on the sequencing of tasks are freed in the mechanism of dynamic matching. As a result, all the tasks for matching are not bound by any prerequisite processes. These tasks are called “the unconstrained tasks” in this research for simplicity. The pre-selection of the unconstrained tasks can

2.2. The dynamic scheduling algorithms of assembly systems A number of dynamic scheduling algorithms were proposed to solve the classical problems in the manufacturing shopfloor. A scheduling method based on Genetic Algorithms (GA) was developed to assist job shop scheduling under multiple criteria and dynamic environment [29]. GA was also combined with many other algorithms to solve engineering problems. E.g., Yang et al. used the grid method to obtain the shortest path for robotics by combining GA and dynamic programming [30]. As to the assembly processes, a mixed integer programming model solved by the Greedy Randomized Adaptive Search Procedure (GRASP) was introduced by Bard et al. to optimize the sequencing in assembly systems [31]. To shorten the total assembly time, Xie et al. employed optimization controlling algorithm of shortening idle time [32]. Fei et al. investigated the dynamic balancing problem based on the complexity of assembly relationships and solved the problem using GA [33]. Besides, Qin et al. adopted a two-level GA to obtain a near optimal solution [34]. To solve the increasingly complicated problems in practice, several categories of the dynamic scheduling algorithms were studied, e.g. the heuristics, meta-heuristics, multi-agent-based approaches, artificial intelligence algorithms, etc. [35]. A multi-agent scheduling system was discussed by Madureira et al., which realized the optimal or near-optimal global performances on the cooperation between multiple agents in manufacturing system [36]. When an assembly system becomes sufficiently large and complex, most approaches that lead to the global optimal solution are not computationally efficient. Except for the dynamic optimization algorithms discussed above, the artificial intelligent systems were also applied in industrial management systems to deal with the operational problems which contain uncertainty, vagueness and high-dimensional data [37]. The above-mentioned literature provided lots of theoretical models and algorithms for the flexible management of assembly systems. On top of these, when it comes to some complex products that were assembled in fixed-position layout and mainly by manual operations, Qin et al. designed a two-level genetic algorithm to obtain a near optimal solution to minimize the make-span [38], which was very helpful for the optimization of fixed-position assembly systems. However, the proposed method still had difficulty in dealing with the product which is rather complex or the scale of assembly jobs is large enough. In this research, a real-time optimization mechanism is discussed that divides the large-scale problem and improves the assembly schedule from the operators’ point of view in the fixed-position assembly systems. The integration of the local and global optimization (i.e. the calculated scheduling plan in real time and the preplanned production schedule from other algorithms) guarantees the practicability and efficiency of ICM. 3

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Fig. 1. The overall architecture of the collaborative mechanism for assembly systems.

greatly reduce the complexity of real-time rescheduling, as all the selected tasks under consideration can be taken right now if the resource has enough capacity. This mechanism has removed the complicated constraints of sequences among the tasks so that modifications to the original plan can be much easier when exceptions happen, as the unconstrained tasks are rather flexible. The original constraints of sequences are not violated because the unconstrained tasks will only be released for assignment when the prerequisite processes have been finished according to the workflow management module. Besides, a knowledge management module (KMM) is designed to realize the evolution of parameters in ICM, which can help to improve the longterm performance of the collaboration among tasks and resources. KMM takes the value of the operational time of processes from sensors and compares it with the optimal plan, training the parameters in ICM to improve the models gradually. As a standalone module, KMM will not directly participate in the task allocation processes, thus it will not affect the system performance greatly while the parameters (e.g. the weights for the dynamic priority) in ICM will have a greater chance to be more reliable. The fourth layer is responsible for the dynamic matching among tasks and resources. The priority of the task is jointly decided by the task urgency, process order, average assembly time, etc. Considering

the dynamic priority, the initial production plan (which may come from an algorithm for the global optimization) can be modified locally according to the real-time status of resources and the progress of related processes. The proposed collaborative mechanism in this architecture (i.e. the locally-adjusted plan in real time which is originated from the global optimization) could be very useful in practice, as the most important reason for the gaps between a perfect plan and a perfect result often lies in the unexpected events during execution, which is not quite rare. The locally-performed matching gives consideration to both the optimal plan at each station and an overall production plan that is “close enough” to the globally-optimized schedule. When an exception occurs, the corresponding production plans at the related stations will be adjusted to avoid the exceeding waiting time of troubleshooting or the potential quality deficiency in the product. Such collaborations may happen among many stations and the operators can follow the latest instructions rather than bothering the exceptional processes. 4. The key techniques in the modeling of assembly processes The real-time data-driven intelligent collaborative mechanism (ICM) for the fixed-position assembly system is mainly based on the 4

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following three models, i.e. the Petri net model of the assembly workflow, the constraint matrix of tasks, and the partitioned structure of the task pools. These models help ICM to monitor the assembly progresses and to select proper tasks for the further matching process.

M ’(Pi ) = M (Pi )

Post (Pi , Tk ) + Pre (Pi , Tj ), Pi

·

t

(1)

t·

The Petri-net model can be used to analyze the workflow of both the global assembly system and the local assembly station. In the dashedline box of Fig. 2, the process-level specification directly related to transition T6 (i.e. the final assembly process) is illustrated. The Petri net is firable and the transition Tj is enabled only when statement (2) is true.

4.1. The Petri net model of the assembly workflow Workflow management is essential for process monitoring and bottleneck analyses, especially in the assembly systems where lots of material, information, value, etc. integrate together. The Petri-netbased modeling technique has been widely studied and applied for its well-developed mathematical theory on modeling the Discrete Event Dynamic Systems (DEDS). The real-time status of the physical systems can be reflected in the dynamic Petri net model, whose structure and attributes can feed the optimization algorithms and contribute to the systemic improvement. The Petri net is defined by the tuple (P, T; Pre, Post, K, M0), where P = {P1, P2, …, Pm} is a finite set of places; T = {T1, T2, …, Tm} is a finite set of transitions; Pre(Pi, Tj): P × T → N denotes an application of precedence and contains the weight of the arcs from place Pi to transition Tj; Post(Pi, Tj): T × P → N denotes an application of incidence and contains the weight of the arcs from transition Tj to place Pi; K = {KP1, KP2, …, KPm} denotes the capacity of the places; M0 denotes the initial markings that specifies the tokens in all places and the initial state of the global system. In addition, M(Pi) denotes the markings (i.e. tokens) of Pi. The inputs and outputs of a transition are denoted as •t and t•, respectively. As illustrated in Fig. 2, the assembly scenario is shown in graphic representation, which includes two rounds of the subassembly and a final assembly. Each operation is marked with Ti (i = 1, 2, …, 6). In the graphical notation of the Petri net, a place is represented by a circle; A transition is denoted by a bar, connected with several ingoing arcs (i.e. inputs) and outgoing arcs (i.e. outputs) with arrows. The tokens are denoted by dots, representing the resource distribution in the current network. During the execution of assembly, attributes such as the statues of operation, the quantity of material, the result of the quality inspection, etc. in different processes can be updated in real time, which will modify the markings of the Petri-net model accordingly. When a transition fires, the tokens in the related places will be updated to M’(Pi), which can be obtained by (1).

Pi

·

t: M (Pi )

Pre (Pi , Tj )

Pi

t ·: M (Pi ) + Post (Pi, Tj )

KPi

(2)

We call it an exceptional situation when a process should have started according to the initial schedule while the corresponding transition is not firable. Through the Petri-net model, the production monitoring system can clearly identify the states of processes during assembly. The raw manufacturing data can be translated into progress and state information and can be directly used by many other modules. E.g., the estimation of assembly time can be more reliable according to the model. Besides, the results of the quality inspection will be reflected with M(PQuality_Cirtificate_Ready) and M(PDefeciency_Report_Ready), and will lead to different follow-ups. With the topology of the Petri net, even when exceptions occur at one assembly station during assembly, other stations can be notified and may adjust the schedule accordingly. 4.2. The constraint matrix of different processes Generally, the assembly of a complex product consists of dozens of processes. There are different relations among them, e.g., the serial, parallel, time-dependent relations, etc. As shown in Fig. 3, TM1−M2…−Mn denotes the task that assembles components M1, M2, …, Mn together. Suppose the task TE–G–F–A is assigned to the assembly station, in addition to preparing the proper material, tools and notify the operators, the prerequisite process should be completed first according to the production route. Therefore, TE–G–F–A is directly constrained by the status of TE–A. The constraints and the interrelation among tasks can be obtained by analyzing the Petri-net based workflow. The operations illustrated in Fig. 3 include 6 sub-processes (marked with the arrows in the dashed circles), where two types of relations are considered, i.e. the serial and parallel relations. The serial relation indicates a sequence of processes (T1, T2, …, Tn), such as (TE-A, TE–G–F–A) and (TB–A, TC–B). The parallel relation indicates that the processes [T1, T2, …, Tn] are

Fig. 2. The real-time data-driven Petri-net model of the assembly. 5

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Tm into consideration when deciding the latest schedule for assembly, which may alter the token in the Task_Active place in the Petri-net model. 4.3. The multi-level task pool structure for ICM Combining the task constraint matrix with the Petri-net model, all processes that are not bound by the status of other processes can be picked out. In order to realize the dynamic and accurate task allocation during assembly execution of complex products, it is also necessary to classify different tasks according to the real-time assembly progress and the states of resources. Therefore, a multi-level task pool structure is created in this section. The state set (TS) of process contains four elements, and is defined as TS = {uncompleted, on-going, completed, fault} (denoted as TS = {1, 2, 3, 4} for simplicity in Fig. 4). The initial state of a process is uncompleted. When a process is picked out and handed over to the operator, its state will turn to on-going and remain unchanged until the process is completed (i.e. the completed state) or some exceptional events happen (i.e. the fault state). Fault may indicate the breakdown of machine, absent of operator, shortage of material or tools, etc., which can be looked up in the hierarchical event structure and the Petri-net based workflow management system, as is shown in the second layer of Fig. 1. The multi-level task pool structure is built to present the changing states of assembly processes. In the dashed box of Fig. 4, the task pool (of an assembly station) is divided into four parts, corresponding to the four states mentioned above. Before the distribution of tasks, the multilevel task structure will scan the updated task pool and select the optimal process and allocate it to the operator. This selection of tasks starts with combining the constraint matrix with the uncompleted tasks. The processes that are not bound by others can be further considered. The unconstrained tasks are then delivered to the task-resource matching module mentioned in the fourth layer of Fig. 1. These tasks can be assigned to the operators and being processed at any moment from now. Considering the structure of the task pool, the scale of the matching problem among tasks and operators is relatively small and can be solved easily. Therefore, the functional module of task-resource matching can be frequently invoked without taking too many computational resources. The detailed matching mechanism is introduced in Section 5.

Fig 3. The breakdown illustration on the part assembly of a complex product.

independent with each other, such as [TE-A, TC–B] and [TB–A, TE–G–F–A]. The status of the parallel processes will not influence one another directly. According to the interrelations among processes, the constraint matrix TCn × n is obtained in (3), where Cij represents the relation between process i and j, and is defined as follows.

C12 TCn × n =

C21 Cn1 Cn2

C1n C2n (3)

For serial processes, the relation is defined according to the distance between them. In this way, the relations between neighboring processes i and j are defined as Cij = ± 1. When i belongs to the prerequisite processes of j (e.g., TE-A and TE-F-A), Cij = 1; otherwise, Cij = −1. The relations between serial processes i and k that do not adjoin each another are defined as Cik = ± 2 (which means there is/are other process (es) between i and k, e.g., TE-A and TG-F). Besides, the relations between parallel processes i and m are defined as Cim = 0; The constraints on the process relations are stored in the constraint matrix and can be accessed by the assembly collaboration system. During assembly, the matrix can help to determine whether the prerequisite processes have been completed, so as to decide which processes are ready to be picked up for assembly. E.g., to examine whether Tm can be processed at this moment, all the elements in the column corresponding to Tm should be checked (i.e. C1m, C2m, …, Cnm). If all elements equal zero, Tm is ready to be processed. Otherwise, if CXm = 1, process TX must be completed prior to Tm. When the assembly system identifies the completion of TX, the collaboration mechanism will take

5. The intelligent collaborative mechanism of tasks The general idea of resource configuration during exceptions in this research is described as follows. According to the initial plan, the uncompleted tasks will try to move to the section of “on-going task” at certain times. If their prerequisite processes have not been completed

Fig. 4. The dynamic filter of tasks for the collaboration among processes. 6

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Table 1 Notations in the dynamic scheduling problem.

Sjh + Tijh

Yijhkl )

(9)

Yij (h + 1) kl )

(10)

Skl + L (1

Notation

Description

Cjh

m n Jj Oi Pjh Pijh Tijh Sjh Cjh S’jh C’jh Dj cj Cmax Ti L Xijh

Total number of assembly tasks Total number of operators The jth assembly tasks (j = 1, 2, …, m) The ith operator (i = 1, 2, …, n) The hth process of Jj Process Pjh operated by Oi The operating time of Pijh The planned starting time of Pjh The planned completed time of Pjh The actual starting time of Pjh The actual completed time of Pjh The delivery time of Jj The completed time of Jj The maximum completed time of Jj The actual operating time of operator Oi, Ti=ΣjΣhTijh A sufficiently large positive value A Boolean value indicating whether Pjh is operated by Oi (X = 1) or not (X = 0) A Boolean value indicating whether Pijh has a higher priority than Pikl (Y = 1) or not (Y = 0) Weights of the two sub-objective functions

Constraints (7) and (8) ensure the predefined sequence of processes in each assembly jobs. (9) and (10) indicate that an operator only can handle one process at a time. This formulated optimization problem can be solved by the genetic algorithm to produce an initial scheduling plan. Related genetic algorithms can be found in lots of literature, and it is not introduced in this research.

Yijhkl a, b

5.2. The dynamic priority of the assembly processes During the assembly execution, the priority of processes may change due to the change of order or other situations caused by exceptions. In this section, the methods to calculate the dynamic priority is introduced. Three major factors are considered to obtain the dynamic priority, i.e. the urgency of tasks, the average processing time, and the static priority. AHP is used to get the relative weights of the three factors and GRA is adopted to achieve the dynamic priority. The urgency of tasks (i.e. Urg) is defined in (11) where T represents the current time. The closer the time approaches the planned starting time, the higher opportunity the process has to be assigned to an operator.

due to the exceptions, these tasks will be monitored and managed by the collaborative mechanism. For other tasks that have not been affected by the exceptions, they will follow their initial plan which can be obtained as introduced in Section 5.1. For the affected tasks, the methods of the Analytical Hierarchy Process (AHP) and Grey Relational Analysis (GRA) are applied to determine the dynamic priority of them. Therefore, the dynamic match among tasks and operators (or assembly stations) can be achieved based on the priorities.

minj Urg = minj (Sjh

The scheduling problem of the fixed-position assembly for complex products is described as follows. There are m assembly tasks (jobs) and n operators. Each job contains h processes. The production route (i.e. the sequence of processes) is predetermined and at least one operator can handle the process. The time consumption of each process varies with each operator. Other notations are defined in Table 1. The target of the dynamic scheduling is to minimize the assembly time and maximize the utilization rate of operators. The following constraints are applied in the optimization. Firstly, one operator can only handle one process at a time. Secondly, one process only can be taken by only one operator at a time. Thirdly, once a process begins, it shouldn't be interrupted (e.g. changing the operator, etc.) due to the scheduling plan. Processes of the different tasks are independent, which means there is no constraint among them. In order to obtain the initial scheduling plan, two sub-objective functions are formulated in (4) and (5), which aims to achieve the schedule with the minimized processing time and the maximized utilization rate of operators respectively. The objective function of this optimization problem is combined in (6), where a and b are the weights. (4)

Ti maxj (cj )

(5)

f2 = max

y = af1 + bf2 = a·min (max(cj )) + b· max j

Ti maxj (cj )

(11)

T)

The average processing time (i.e. Avetime) is defined in (12), where n represents the number of operators that can handle this task In general practice, processes (of similar importance) with shorter operating time tend to be arranged earlier, so as to initiate the subsequent process as early as possible. Since each task can be processed by at least one operator, their average processing time is used to estimate the standard processing time of the process.

5.1. The initial static schedule

f1 =min (maxj (cj ))

Sj (h + 1) + L (1

minj Avetime = minj (

i

Tijh

n

)

The static priority of the process indicates the inherent order from the production route. E.g., the production route of a product is illustrated in the tree model in Fig. 5. The six processes can be divided into three levels according to the assembly procedures. The process that is closer to the final product (i.e. the output of T6) receives a lower static priority. As shown in Fig. 5, the processes with the highest static priority are T1, T2, T3, and the lowest one is T6. After determining the three factors, AHP and GRA are adopted to obtain dynamic priority. Fig. 6 shows the hierarchical structure of the AHP model, where the time, cost, and quality of processing are chosen to be the criteria. In AHP, the paired comparison method is used to obtain the judgment matrix (A), which is notated in (13). The weight wi of each factor is determined by (14) and aij is the elements of A. According to AHP, aij usually takes the value of 1, 2, 3, …, 9 and their reciprocals.

(6)

Subject to:

Sjh + Xijh Tijh

Cjh

Sj (h + 1)

Cjh

(12)

(7) (8)

Fig. 5. The static priority based on the production route. 7

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(18)

LCR = LCI / LRI

Then, GRA is applied to obtain the dynamic priority of the assembly processes. The procedures are described as follows. Firstly, the reference and comparison sequences of processes that reflect the features of the dynamic priority are determined. Then, the sequences are normalized. The processed matrix is obtained in (18).

(X0T , X1T , Fig. 6. The hierarchical structure of the AHP model.

(13)

an1 an2 . . . ann

W = (w1, w2, …, wn )T , wi =

n n

a j ij a j ij

(

wi = 1)

i (k )

ri =

(14)

n

= i=1

n

Amax

LCI =

LRI =

(AW )i nWi

n

1 n

n

Xn (1) Xn (2)

X 0 (m ) X 1 (m )

Xn (m)

(18)

(16)

LCIn

(17)

1 m

mini mink |x 0 (k ) x 0 (k )| + max i max k |x 0 (k ) x 0 (k )| |x 0 (k ) x 0 (k )| + max i max k |x 0 (k ) x 0 (k )|

(19)

m

wk i (k ) k=1

(20)

5.3. The allocation and optimization of the processes In this section, a process-operator matching mechanism is introduced to allocate tasks for the assembly stations based on the dynamic priority of processes discussed above. As shown in Fig. 7, the six processes selected in Section 5.2 are sorted by priority and the first three ones are stored in the area of “the preselected tasks,” which will be assigned to some operators who are available at the starting time of the tasks. This assignment of tasks is much easier to handle as the solution space has been greatly reduced and some constraints are freed. The other three tasks are stored in the area of “the inactive tasks.” As long as there is vacant space in the “preselected tasks,” the inactive tasks will automatically fill in it and the Task_Active place in the Petri-net model in Fig. 2 will receive a token. Since the status of each task at different stations can be timely collected, the pre-allocation of tasks is possible during rescheduling. Operators that are to be available after a given time period can also be considered during matching. During execution, only those whose time to completion of their current task (Tc = Cjh-T) is less than α (depending on the specific assembly process) can join the pool of operators, and are called the available operators. The criteria for matching are jointly decided by the operators’ ability of assembly (which is represented by the operating time in this research) and the waiting time. The waiting

(15)

1

=

By comparing the values of ri, the dynamic priority of each assembly process is determined, and the top six processes sorted by dynamic priority are picked up for the subsequent task allocation introduced in Section 5.3.

The judgment matrix in AHP should be given by experts to provide a more reliable result. The subjective method AHP is applied in this research as a simple way to formulize human knowledge, which can provide relatively good values for the weights while not making the calculations too much complicated (e.g. those NP-hard problems that need the intelligent algorithms to solve). The knowledge management module in ICM applies some training models such as those based on Back-Propagation Neural Network, helping to provide better parameters of ICM and avoid the bias brought by the subjective judgment. The assembly time during exceptions and the corresponding weights given for priority determination will be recorded together. By comparing the actual assembly time and the planned one, the optimal set of weights can be achieved. Introduction to the detailed modeling process of a Back-Propagation Neural Network can be referred to in [41]. The consistency judgment formulas (15)–(18) are used in the AHP. LCI, LRI, and LCR are the primary indexes used in AHP, and they represent the Consistency Index, the Random consistency Index, and the Consistency Ratio, respectively. If LCR < 0.1, w = (w1, w2, .., wn) passes the consistency test, and W can be used as the relative weights to determine the dynamic priority of processes. max

X1 (1) X1 (2)

The grey correlation coefficient ζi between the reference and comparison sequences is achieved by calculating (19), and the correlation ri is obtained through (20).

a11 a12 . . . a1n a21 a22 . . . a2n

A = (aij )n×n =

XnT ) =

X 0 (1) X 0 (2)

Fig. 7. The process-operator matching mechanism driven by the dynamic priority. 8

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processes are listed in Table 2, where the dashed line indicates the operator is incapable to handle the corresponding process. According to the task constraints and the objective function established in Section 5.1, the static scheduling tables for the three assembly stations (Station Ⅰ, Ⅱ, and Ⅲ) are summarized in Table 3. For simplicity, TB–A, TE–A, TC–BA, TD–BA, TI–A, TF–E, TG–F, TH–E are called the 1st, 2nd, …, 8th task, and T(X−r) represents the rth task assigned to station X. During the assembly execution, 4 operators (O1, O2, O3, and O4) will complete the tasks that contain 24 processes. The setup time for switching between station I and II, I and III is Ts = 0.1, and the setup time for switching between station II and III is Ts=0.2. During execution, three exceptions are tested in this case. First, operator O1 is instructed to leave at T = 20 and will return at T = 40. Second, after T(Ⅲ-4) is completed, module F for the process T(Ⅱ-6) cannot be delivered to station Ⅱ on time, and will be delayed for a 20unit time period. Third, operator O1 leaves for another temporary task at T = 130 and returns to station Ⅲ at T = 155. In the Petri-net model, the transition corresponding to T(Ⅲ-2) is not firable according to (2), and the initial static schedule cannot be followed. At T = 20, operator O1 finishes T(Ⅰ-3) and requests a new task, and the ICM is activated then. The initial schedule is shown in Fig. 10(a) and the scheduling provided by ICM is shown in Fig. 10(c). Based on the constraint matrix and the task pool structure, the set of unconstrainted tasks at T = 22 can be obtained, i.e. Tfree= {T(Ⅰ-4), T(Ⅰ5), T(Ⅰ-6), T(Ⅱ-4), T(Ⅲ-4)}. Through the AHP method, the weights for urgency, processing time, and static priority can be obtained as W = (0.6, 0.2, 0.2)T. By applying GRA, the tasks in Tfree are sorted by dynamic priority in descending order as {T(Ⅱ-4), T(Ⅰ-4), T(Ⅰ-5), T(Ⅲ-4), T(Ⅰ-6)}. According to the matching mechanism, process T(Ⅱ-4), T(Ⅰ-4), and T(Ⅰ-5) will be matched with operator O2 at the first place. Next, by calculating S one by one, ICM will allocate T(Ⅱ-4) to operator O2 which leads to the maximized S. Since operator O1 is unavailable at this moment, the progress of T(III-2) deviates from the initial plan, and T(III-2) becomes the unconstrained task again. By repeating the calculations mentioned above, this task will be reassigned to operator O3. According to the calculations, T(I-5) and T(III-5) will be assigned to O1 and O3, which is consistent with the initial plan. Until the initial plan can be followed again, ICM is responsible to handle the task allocation and resource configuration. In this case, although three different exceptions happened independently, only two processes were reassigned (i.e. T(III2) and T(III-8)). The initial schedule in Fig. 10(a) indicates the maximum completed time is Cmax = 123.3. When the exceptions occur without the treatment of ICM, Cmax = 176, as shown in Fig. 10(b). After the intervention of ICM, the scheduling result is shown in Fig. 10(c), and Cmax = 148.1. A comparison of the assembly progress among the three schedules is shown in Fig. 11. When the timestamps range from 1 to 20, the assembly progress of the initial plan is slightly lower (less than 1%) than that of the ICM-based plan; and for other timestamps, the initial plan runs ahead of or parallel to the ICM-based plan. That is because the assembly progress was defined as the cumulative time consumption of all operators so far divided by the total time consumption for all processes. Since the total processing time may change after rescheduling, it is possible for the assembly progress to increase or decrease over time. In this case, the after-ICM plan reassigned the task TH-E to O2, which was initially planned to be done by O1 and O3, causing the total time consumption of all tasks to decrease. After the exceptional events starting from T = 20, the progress dropped behind the planned one. ICM has reduced the gap between plan and execution by 60.26% on average from T = 1 to T = 180 (with the standard deviation d = 42.90%). Therefore, the influences caused by exceptions and the modifications to the initial plan are greatly reduced. Except for keeping the assembly progress close to the initial plan, ICM can also minimize the loss in resource efficiency during exceptions. Actually, it may even provide a plan with better resource efficiency compared to the initial one. In this case, the utilization rate (Ui) of each operator is compared in Fig. 12. The results show that ICM has

Fig. 8. The procedures of the dynamic task allocation.

time includes the time to completion (Tc) and the time for setup (Ts). During the matching process, the three preselected processes and the available operators are considered. In this research, the general rule for matching is that the process with high priority should be completed sooner. The operator whose Tc = 0 will select the process by calculating (21). From objective function (21), the above-mentioned rule for matching is reflected that the total time consumption (including the waiting-to-start time Tc, the setup time Ts, and the processing time Tijh) for tasks with high priority should be finished as soon as possible, which is a commonly used principle in practice.

min S = min (TC + TS + Tijh )

(21)

i = 1,2,3

If the selected process is not the one with the highest priority, the collaboration mechanism will assign the process with the highest priority to an operator to the operators whose Tc ≤ α while S is minimized. The procedures are listed in Fig. 8. 6. Analysis of a case scenario In this section, a case is analyzed to demonstrate the presented ICM. In the case scenario, there are three fixed-position assembly stations, named Ⅰ, Ⅱ, and Ⅲ respectively. Each station has an initial task of assembly. The operators’ interface of one station is shown in Fig. 9. Nine alphabetical labeled components are displayed in the task instructions. Component E, F, and G need to be assembled and installed at the bottom of component A in order. Component B is attached to A and component C is installed in the inner slot of B. Component I and D are installed at the bottom of A. Finally, component H is assembled on the A as the door of this machine. The production route is defined in (22).

TB

TC8 × 8

TB A TE A TC BA = TD BA TI A TF E TG F TH E

A

TE

A

0 1 1 0 0 0 2

TC

TD

BA

0 0 0 1 1 2 2

1 0 0 0 0 0 1

BA

1 0 0 0 0 0 1

TI 0 1 0 0 0 0 1

TF

A

0 1 0 0 0 1 2

E

0 2 0 0 0 1 1

TG

F

TH

E

2 2 1 1 1 2 1 (22)

There are four operators in the shopfloor to handle the tasks. Each operator can independently complete one or more assembly processes. The processing time for each operator on the same task may be different. The relations of the processing time among operators and 9

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Fig. 9. The user interface of the prototype system at each assembly stations.

mainly lie in the following two aspects. First, the methods/models selected for ICM are rather simple and straightforward. They can be easily solved without using any intelligent algorithms. Therefore, it is practical to handle exceptions with ICM in assembly stations that have many tasks and resources. Second, ICM can deal with exceptions through resource collaboration, which means the solution focuses on solving local problems and the rescheduling does not necessarily need to spread to the global level. To conclude, ICM can reduce the impact of exceptions using limited computation resources, making it easier for industrial implementations.

Table 2 The processing time of different processes for different operators.

O1 O2 O3 O4

TB-A

TE-A

TC-BA

TD-BA

TI-E

TF-E

TG-F

TH-E

10 11 12 –

10 11 12 11

10 11 12 12

– 14 – 14

15 – 14 13

19 20 21 –

22 – 19 18

* 25 * 23

(*Specially, O1 and O3 can cooperate to complete TH-E, costing 21-unit time).

improved the utilization rate of O3 by 1.2% under exceptional situations, and decreased the impact of exceptions on O2 and O4 by 90.5% and 43.6% in terms of utilization rate, respectively. The differences of ICM compared to other scheduling systems

7. Conclusions and future works Manufacturing enterprises have to strive to improve their

Table 3 The initial static scheduling of each process. Task

Operator

Starting time

Completed time

Task

Operator

Starting time

Completed time

T(Ⅰ-1) T(Ⅰ-2) T(Ⅰ-3) T(Ⅰ-4) T(Ⅰ-5) T(Ⅰ-6) T(Ⅰ-7) T(Ⅰ-8) T(Ⅱ-1) T(Ⅱ-2) T(Ⅱ-3) T(Ⅱ-4)

O1 O4 O1 O4 O1 O1 O1 O2 O2 O4 O2 O2

0 0 10 22.2 30.2 45.2 71.2 98.2 0 11.1 11 22

10 11 20 36.2 45.2 64.2 93.2 123.2 11 22.1 22 36

T(Ⅱ−5) T(Ⅱ-6) T(Ⅱ-7) T(Ⅱ-8) T(Ⅲ-1) T(Ⅲ-2) T(Ⅲ-3) T(Ⅲ-4) T(Ⅲ-5) T(Ⅲ-6) T(Ⅲ-7) T(Ⅲ-8)

O4 O2 O4 O4 O3 O1 O3 O2 O3 O3 O3 O1, O3

38.1 50.4 70.4 88.4 0 20.1 12 36.2 30 45 70 95

51.1 70.4 88.4 111.4 12 30.1 24 50.2 44 66 89 116

10

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Fig. 10. The Gantt charts for the scheduling results before and after the intervention of ICM.

responsiveness to meet the requirements from the changing market and react quickly to the exceptions. As an important hub that connects lots of resources and the final product, the assembly systems for complex

products require special attention to stay competitive. In order to achieve collaborative and effective interaction among assembly stations and operators, a framework of ICM in fixed-position assembly systems 11

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Fig. 11. Comparison of the assembly progress using the different schedule.

federated computing) or applying some intelligent algorithms. Acknowledgments The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (51675441) and the 111 Project Grant of Northwestern Polytechnical University (B13044). We appreciate the opportunities to conduct multiply investigations in company HL brought by the Mechanical and Energy Engineering Department of Southern University of Science and Technology. We would also like to thank the editors and reviewers for their efforts and comments on this work. References Fig. 12. Comparison of the utilization rate of operators.

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is put forward in this research. Based on the automatic identification technologies and a flexible strategy of dynamic scheduling, the local collaboration among operators is combined with the global optimal schedule, so as to improve the system performance when exceptions occur during assembly. There are three main contributions of this research. Firstly, an overall architecture of ICM is built up that guarantees reliable information exchange among different assembly stations and operators. This architecture is fundamental to solve any dynamic optimization problems. Secondly, the dynamic priority of processes is defined and obtained using the Petri-net-based workflow analyses, AHP, and GRA. This offers a great tool to reduce the dimension of the scheduling problem for complex products with massive processes. Thirdly, ICM combines the global optimal schedule and the local adjustments to make the assembly system still effective when exceptions occur. According to the case analysis, the proposed ICM focusing on the tradeoff between the optimal rescheduling plan and the flexible task reassignment during exceptions can reduce the delays and improve the utilization of operators. The deficiency of the case analysis represents one of the main limitations of this research. Since the real-world assembly task for complex products involves too many resources and processes, more experiments need to be done and more cases need to be analyzed to test the strategies and to improve the flexibility and efficiency of more general systems. Besides, some of the technologies used in this research (i.e. AHP, GRA, etc.) are the very basic ones to stand out the procedures of ICM. Therefore, there is still room for achieving a better tradeoff between the effectiveness and efficiency of the dynamic resource allocation problem by adopting some latest computing techniques (e.g. 12

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