# FMS Scheduling Integration for Mass Customization

Yumin He, Milton Smith

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Yumin He, Milton Smith. FMS Scheduling Integration for Mass Customization. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.507-515, 10.1007/978-3-030-85902-2_54. hal-04117683

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# FMS Scheduling Integration for Mass Customization

Yumin He¹* and Milton Smith²

¹ Beihang University, Beijing 100191, P. R. China

² Texas Tech University, Lubbock, TX 79409, United States

heyumin@buaa.edu.cn

**Abstract.** In today's manufacturing and supply chain environments, many companies face challenge in responding to customers' requirements quickly and providing customized products quickly at low cost. Mass customization can help companies in providing customized products and services quickly and at a low price. Integrated decision-making has been found effective in many situations. This paper reviews the scheduling research of flexible manufacturing systems (FMSs). The FMS scheduling problem is part of the FMS production and operation management problem. Because the production management of FMSs is very difficult, the FMS scheduling problem is very complicated. Many researchers have investigated the FMS scheduling problem. The paper summarizes the FMS scheduling research with recent development. In addition, a framework of FMS scheduling integration for mass customization is developed based on the literature survey. A control flow of FMS part processing is designed as part of the framework. Further development of the FMS scheduling integration is suggested.

**Keywords:** Mass Customization, Flexible Manufacturing System, FMS Scheduling, Scheduling Integration.

## 1 Introduction

Contemporary manufacturing and supply chain environments are dynamic and changing. It often occurs that customers' requirements need to be satisfied quickly and accurately at a low price [1]. Mass customization (MC) is aimed to provide customized products and services with low cost and high quality in changing environments [2]. Integrated decision-making has been found effective to adequately manage various conflicting objectives and to make coordination control [3-5].

Flexible manufacturing systems (FMSs) can be used to automate mass customization [2]. The production management of FMSs is more difficult than that of mass production lines and job shops [6]. Orders sent to a capacity constrained FMS might not be processed on time and excess-capacity parts have to be sent to a job shop [7]. He, Stecke, and Smith (2016) investigated simultaneous robot and machine scheduling with part input sequencing in FMSs for mass customization [8]. Interactions between FMS robot scheduling and machine scheduling with part input sequencing are found. He and Stecke (2021) investigated the problem of simultaneous
FMS part input sequencing and robot scheduling and suggested the integration of
simultaneous FMS part input sequencing and robot scheduling with operation
scheduling [9].

This paper reviews the FMS scheduling research in the area of FMS part input
sequencing, robot scheduling, and machine scheduling. The FMS scheduling research
with recent development is presented. Based on the literature survey, a framework of
FMS scheduling integration for mass customization is developed. A control flow of
FMS part processing is designed as part of the framework.

## 2 FMS Scheduling

Many researchers have investigated the FMS scheduling problem. The FMS
scheduling research is summarized in the following as FMS part input sequencing,
sequencing and scheduling in FMSs with robot material handling, and machine
scheduling with FMS material handling.

### 2.1 FMS Part Input Sequencing

FMSs can be classified as flexible flow systems (FFSs) and general flexible
machining systems [10]. FFSs include flexible assembly systems and flexible transfer
lines. General flexible machining systems include both dedicated and nondedicated
flexible machining systems. A flexible machining cell (FMC) is a single machine and
its associated equipment [11].

FMS part input sequencing has been studied with the FMS production planning
problems in earlier studies. For example, Stecke and Kim (1991) developed a
modified Johnson's algorithm for FFSs [12]. Stecke (1992) developed several
approaches to solve the FMS part input sequencing problem [13]. Research on FMS
part input sequencing is summarized in Table 1.

### 2.2 Sequencing and Scheduling in FMS with Robot Material Handling

Researchers have studied sequencing and scheduling in FMS with robot material
handling. For example, Sethi *et al.* (1992) studied a real FMS with a robot, two or
three machine tools, and a single part type to maximize throughput [27].
Sriskandarajah *et al.* (2004) scheduled a bufferless dual-gripper robot handling
multiple part types to maximize throughput [28].

Dawande *et al.* (2005) surveyed robot move sequencing and part scheduling in
FMSs with robot material handling [30]. Research on sequencing and scheduling in
FMS with robot material handling is summarized in Table 2.

### 2.3 Machine Scheduling with FMS Material Handling

Researchers have studied machine scheduling with FMS material handling. In earlier
studies, Blazewicz et al. (1991) proposed a pseudo-polynomial dynamic programming
approach to schedule machine and vehicle [36]. Sabuncuoglu and Hommertzheim
<table><caption>**Table 1.** Summary of research on FMS part input sequencing.</caption><thead><tr><th>No.</th><th>Author</th><th>Subject</th><th>Objective</th><th>Year</th></tr></thead><tbody><tr><td>12</td><td>Stecke, Kim</td><td>Part type selection, part input</td><td>Over & under load</td><td>1991</td></tr><tr><td>13</td><td>Stecke</td><td>Part input sequencing</td><td>Over & under load</td><td>1992</td></tr><tr><td>14</td><td>O'Keefe, Rao</td><td>Part input</td><td>Makespan, throughput</td><td>1992</td></tr><tr><td>15</td><td>Kim, Yano</td><td>Part type selection</td><td>Total tardiness</td><td>1994</td></tr><tr><td>16</td><td>Smith, Stecke</td><td>Part input sequencing</td><td>Balancing workload</td><td>1996</td></tr><tr><td>17</td><td>Leu</td><td>Order-input sequencing</td><td>Set up time</td><td>1999</td></tr><tr><td>18</td><td>Sawik</td><td>Sequential loading</td><td>Production time</td><td>2000</td></tr><tr><td>19</td><td>Sawik</td><td>Blocking scheduling</td><td>Completion time</td><td>2001</td></tr><tr><td>20</td><td>Kim, Lee, Yoon</td><td>Part input sequencing</td><td>Makespan</td><td>2001</td></tr><tr><td>21</td><td>Sawik</td><td>Simultaneous balancing and scheduling</td><td>Completion time</td><td>2002</td></tr><tr><td>22</td><td>Lacomme, Moukrim, Tchernev</td><td>Job-input sequencing</td><td>Makespan</td><td>2005</td></tr><tr><td>23</td><td>He, Smith</td><td>Part input sequencing</td><td>Production</td><td>2007</td></tr><tr><td>24</td><td>Gusikhin, Caprihan, Stecke</td><td>Input sequencing</td><td>In-sequence parts, buffer size</td><td>2008</td></tr><tr><td>25</td><td>Sawik</td><td>Batching scheduling, cyclic scheduling</td><td>Completion time</td><td>2012</td></tr><tr><td>26</td><td>He et al.</td><td>Part input sequencing</td><td>Production</td><td>2015</td></tr></tbody></table>

<table><caption>**Table 2.** Summary of research on sequencing and scheduling in FMS with robot material handling.</caption><thead><tr><th>No.</th><th>Author</th><th>Subject</th><th>Objective</th><th>Year</th></tr></thead><tbody><tr><td>27</td><td>Sethi et al.</td><td>Sequencing parts and robot moves</td><td>Long run average throughput</td><td>1992</td></tr><tr><td>28</td><td>Sriskandarajah et al.</td><td>Robot move sequencing, part sequencing</td><td>Throughput rate</td><td>2004</td></tr><tr><td>29</td><td>Geismar et al.</td><td>Cyclic scheduling</td><td>Throughput</td><td>2005</td></tr><tr><td>31</td><td>Dawande, Pinedo, Sriskandarajah</td><td>Cyclic scheduling</td><td>Throughput</td><td>2009</td></tr><tr><td>32</td><td>Yildiz, Akturk, Karasan</td><td>Cyclic scheduling</td><td>Cycle time</td><td>2011</td></tr><tr><td>33</td><td>Zahrouni, Kamoun</td><td>Sequencing parts and robot activities</td><td>Cycle time</td><td>2012</td></tr><tr><td>34</td><td>Che, Kats, Levner</td><td>Robotic flow shop scheduling</td><td>Cycle time, stability radius</td><td>2017</td></tr><tr><td>35</td><td>Gultekin, Coban, Akhlaghi</td><td>Cyclic scheduling</td><td>Throughput rate</td><td>2018</td></tr><tr><td>36</td><td>Foumani, Razeghi, Smith-Miles</td><td>Cyclic scheduling</td><td>Partial cycle time</td><td>2020</td></tr><tr><td>9</td><td>He, Stecke</td><td>Simultaneous part input sequencing and robot scheduling</td><td>Production</td><td>2021</td></tr></tbody></table>
(1992) proposed a dynamic dispatching algorithm to schedule machines and AGVs [37]. Research on machine scheduling with FMS material handling is summarized in Table 3.

<table><caption>**Table 3.** Summary of research on machine scheduling with FMS material handling.</caption><thead><tr><td>No.</td><td>Author</td><td>Subject</td><td>Objective</td><td>Year</td></tr></thead><tbody><tr><td>37</td><td>Blazewicz et al.</td><td>Production scheduling, vehicle scheduling</td><td>Completion time</td><td>1991</td></tr><tr><td>38</td><td>Sabuncuoglu, Hommertzheim</td><td>Scheduling machines and AGVs</td><td>Mean flowtime, mean tardiness</td><td>1992</td></tr><tr><td>39</td><td>Ulusoy, Bilge</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>1993</td></tr><tr><td>40</td><td>Bilge, Ulusoy</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>1995</td></tr><tr><td>41</td><td>Abdelmaguid et al.</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>2004</td></tr><tr><td>42</td><td>Deroussi, Gourgand, Tchernev</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>2008</td></tr><tr><td>43</td><td>Babu et al.</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>2010</td></tr><tr><td>44</td><td>Lacomme, Larabi, Tchernev</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>2013</td></tr><tr><td>45</td><td>Zheng, Xiao, Seo</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>2014</td></tr><tr><td>46</td><td>Baruwa, Piera</td><td>Simultaneous scheduling of machines and AGVs</td><td>Makespan</td><td>2016</td></tr><tr><td>8</td><td>He, Stecke, Smith</td><td>Part input sequencing, machine and robot scheduling</td><td>Total parts produced, robot utilization, mean flowtime</td><td>2016</td></tr><tr><td>47</td><td>Nouri, Driss, Ghedira</td><td>Simultaneous scheduling of machines and transport robot</td><td>Makespan</td><td>2016</td></tr></tbody></table>

## 3 Framework of FMS Scheduling Integration

A framework of FMS scheduling integration for mass customization is developed based on the literature survey. The framework is illustrated in Fig. 1. The FMS is composed of CNC machines and a robot for material handling. The framework includes an information processing center that is composed of computers, servers, and tools to process information. The center can process data and information exchanged through internet, intranet, and extranet. It can also process data and information obtained from RFID. RFID technology is the significant advance in managing dynamic systems [48].
<figure><img src="image_3.png" /><figcaption>Fig. 1. Illustration of FMS Scheduling Integration.</figcaption></figure>

The framework also includes a scheduler for FMS scheduling integration. The scheduler is composed of an FMS part input scheduler, an FMS robot scheduler, and an FMS machine scheduler. Scheduling algorithms found in the literature can be used as the schedulers. The algorithm for FMS part input sequencing developed in [23, 26] can be used as the FMS part input scheduler. The algorithm for FMS robot scheduling developed in [49] can be used as the FMS robot scheduler.

The combination of the FMS part input scheduler and the FMS robot scheduler can result in the simultaneous scheduler. The algorithm for simultaneous FMS part input sequencing and robot scheduling has been developed in [9]. The integrated scheduler for the FMS scheduling integration can be developed by integrating these individual schedulers. It can also be developed by combining the simultaneous scheduler with the FMS machine scheduler.

A control flow of FMS part processing is designed. The control flow is aimed to illustrate part flow and control decision making in the FMS. The control flow is explained in the following. Inputted parts are waiting for loading and unloading (L/U). If the L/U is available, a part is loaded to the FMS. Parts are waiting for the robot for moving. If the robot is available, a part is moved by the robot to a machine for next operation. Parts are waiting for machines for processing. If a machine is available, a part is processed by the machine. After an operation is finished, the part is checked. If the part does not finish all operations, the part is waiting for the robot for
moving. Otherwise, the part is waiting for the L/U to be unloaded. The diagram of the control flow is illustrated in Fig. 2.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Control flow of part processing.</figcaption></figure>

## 4 Conclusion

In contemporary manufacturing and supply chain environments, companies often face challenge in satisfying customers' requirements quickly and accurately at a low price in dynamic and changing environments. Mass customization and integrated decision-making can provide help to companies to overcome the difficulty.

The FMS scheduling problem is part of the FMS production and operation management problem. Because the production management of FMSs is very difficult, the FMS scheduling problem is very complicated. Many researchers have investigated the FMS scheduling problem.

In this paper, the FMS scheduling research with recent development is reviewed. It is summarized as FMS part input sequencing, sequencing and scheduling in FMSs with robot material handling, and machine scheduling with FMS material handling. It is hoped that the review can provide researchers with a reference of the FMS scheduling.

This paper also develops a framework for FMS scheduling integration for mass customization. A control flow of FMS part processing is designed. The integrated scheduler for the FMS scheduling integration can be developed by integrating the
FMS part input scheduler, the FMS robot scheduler, and the FMS machine scheduler. It can also be developed by combining the simultaneous scheduler with the FMS machine scheduler.

## Acknowledgment

The authors would like to thank the session chair and the referees.

## References

1. Shapiro, B.P., Rangan, V.K., Sviokla J.J.: Staple yourself to an order. Harvard Business Review 7-8 162-171 (2004).

2. Pine II, B.J., Victor, B., Boynton, A.C.: Making mass customization work. Harvard Business Review 9-10 108-119 (1993).

3. Sawik, T.: Single vs. multiple objective supplier selection in a make to order environment. Omega: The International Journal of Management Science 38 203-212 (2010).

4. Sawik, T.: Integrated supply, production and distribution scheduling under disruption risks. Omega: The International Journal of Management Science 62 131-144 (2016).

5. Ivanov, D., Dolgui, A., Sokolov, B.: Robust dynamic schedule coordination control in the supply chain. Computers & Industrial Engineering 94 18-31 (2016).

6. Stecke, K.E.: Formulation and solution of nonlinear integer production planning problems for flexible manufacturing systems. Management Science 29 273-288 (1983).

7. Tetzlaff, U.A.W., Pesch, E.: Optimal workload allocation between a job shop and an FMS. IEEE Transactions on Robotics and Automation 15(1) 20-32 (1999).

8. He, Y., Stecke, K.E., Smith, M.L.: Robot and machine scheduling with state-dependent part input sequencing in flexible manufacturing systems. International Journal of Production Research 54 6736-6746 (2016).

9. He, Y., Stecke, K.E.: Simultaneous part input sequencing and robot scheduling for mass customization. International Journal of Production Research online https://doi.org/10.1080/00207543.2021.1894369 (2021).

10. Rachamadugu, R., Stecke, K.E.: Classification and review of FMS scheduling procedures. Production Planning and Control 5(1) 2-20 (1994).

11. Browne, J., Dubois, D., Rathmill, K., Sethi, S.P., Stecke, K.E.: Classification of flexible manufacturing systems. The FMS Magazine April 114-117 (1984).

12. Stecke, K.E., Kim, I.: A flexible approach to part type selection in flexible flow systems using part mix ratios. International Journal of Production Research 29 53-75 (1991).

13. Stecke, K.E.: Procedures to determine part mix ratios for independent demands in flexible manufacturing systems. IEEE Transaction on Engineering Management 39(4) 359-369 (1992).

14. O'Keefe, R.M., Rao, R.: Part input into a flexible flow system: an evaluation of look-ahead simulation and a fuzzy rule base. International Journal of Flexible Manufacturing Systems 4 113-127 (1992).

15. Kim, Y.-D., Yano, C.-A.: A due date-based approach to part type selection in flexible manufacturing systems. International Journal of Production Research 32 1027-1043 (1994).
16. Smith, T.M., Stecke, K.E.: On the robustness of using balanced part mix ratios to determine cyclic part input sequences into flexible flow systems. International Journal of Production Research 34 2925-2941 (1996).

17. Leu, B.-Y.: Comparative analysis of order-input sequencing heuristics in a cellular flexible assembly system for large products. International Journal of Production Research 37 2861-2873 (1999).

18. Sawik, T.: Simultaneous versus sequential loading and scheduling of flexible assembly systems. International Journal of Production Research 38(14) 3267-3282 (2000).

19. Sawik, T.: Mixed integer programming for scheduling surface mount technology lines. International Journal of Production Research 39(14) 3219-3235 (2001).

20. Kim, Y.-D., Lee, D.-H., Yoon, C.-M.: Two stage heuristic algorithms for part input sequencing in flexible manufacturing systems. European Journal of Operational Research 133 625-634 (2001).

21. Sawik, T.: Balancing and scheduling of surface mount technology lines. International Journal of Production Research 40(9) 1973-1991 (2002).

22. Lacomme, P., Moukrim, A., Tchernev, N.: Simultaneous job input sequencing and vehicle dispatching in a single vehicle automated guided vehicle system: a heuristic branch-and-bound approach coupled with a discrete events simulation model. International Journal of Production Research 43 1911-1942 (2005).

23. He, Y., Smith, M.L.: A dynamic heuristic-based algorithm to part input sequencing in flexible manufacturing systems for mass customization capability. International Journal of Flexible Manufacturing Systems 19 392-409 (2007).

24. Gusikhin, O., Caprihan, R., Stecke, K.E.: Least in-sequence probability heuristic for mixed-volume production lines. International Journal of Production Research 46 647-673 (2008).

25. Sawik, T.: Batch versus cyclic scheduling of flexible flow shops by mixed integer programming. International Journal of Production Research 50(18) 5017-5034 (2012).

26. He, Y., Rachamadugu, R., Smith, M.L., Stecke, K.E.: Segment set-based part input sequencing in flexible manufacturing systems. International Journal of Production Research 53 5106-5117 (2015).

27. Sethi, S.P., Sriskandarajah, C., Sorger, G., Blazewicz, J., Kubiak, W.: Sequencing of parts and robot moves in a robotic cell. International Journal of Flexible Manufacturing Systems 4 331-358 (1992).

28. Sriskandarajah, C., Drobouchevitch, I., Sethi, S.P., Chandrasekaran, R.: Scheduling multiple parts in a robotic cell served by a dual-gripper robot. Operations Research 52(1) 65-82 (2004).

29. Geismar, H.N., Sethi, S.P., Sidney, J.B., Sriskandarajah, C.: A note on productivity gains in flexible robotic cells. International Journal of Flexible Manufacturing Systems 17 5-21 (2005).

30. Dawande, M., Geismar, H.N., Sethi, S.P., Sriskandarajah, C.: Sequencing and scheduling in robotics cells: recent developments. Journal of Scheduling 8 387-426 (2005).

31. Dawande, M., Pinedo, M., Sriskandarajah, C.: Multiple part-type production in robotic cells: equivalence of two real world models. Manufacturing & Service Operations Management 11(2) 210-228 (2009).

32. Yildiz, S., Akturk, M.S., Karasan, O.E.: Bicriteria robotic cell scheduling with controllable processing times. International Journal of Production Research 49 569-583 (2011).

33. Zahrouni, W., Kamoun, H.: Sequencing and scheduling in a three-machine robotic cell. International Journal of Production Research 50(10) 2823-2835 (2012).
34. Che, A., Kats, V., Levner. E.: An efficient bicriteria algorithm for stable robotic flow shop scheduling. European Journal of Operational Research 260 964-971 (2017).

35. Gultekin, H., Coban, B., Akhlaghi, V.E.: Cyclic scheduling of parts and robot moves in m- machine robotic cells. Computers & Operations Research 90 161-172 (2018).

36. Foumani, M., Razeghi, A., Smith-Miles, K.: Stochastic optimization of two-machine flow shop robotic cells with controllable inspection times: From theory toward practice. Robotics and Computer-Integrated Manufacturing 61 1-20 (2020).

37. Blazewicz, J., Eiselt, H.A., Finke, G., Laporte, G., Weglarz, J.: Scheduling tasks and vehicles in a flexible manufacturing system. International Journal of Flexible Manufacturing Systems 4 5-16 (1991).

38. Sabuncuoglu, I., Hommertzheim, D.L.: Dynamic dispatching algorithm for scheduling machines and automated guided vehicles in a flexible manufacturing system. International Journal of Production Research 30(5) 1059-1079 (1992).

39. Ulusoy, G., Bilge, U.: Simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research 31 2857-2873 (1993).

40. Bilge, U., Ulusoy, G.: A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Operations Research 43(6) 1058-1070 (1995).

41. Abdelmaguid, T.F., Nassef, A.O., Kamal, B.A., Hassan, M.F.: A hybrid GA/ heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research 42 267-281 (2004).

42. Deroussi, L., Gourgand, M., Tchernev, N.: A simple metaheuristic approach to the simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research 46(8) 2143-2164 (2008).

43. Babu, A.G., Jerald, J., Haq, A.N., Luxmi, V.M., Vigneswaralu, T.P.: Scheduling of machines and automated guided vehicles in FMS using differential evolution. International Journal of Production Research 48(16) 4683-4699 (2010).

44. Lacomme, P., Larabi, M., Tchernev, N.: Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics 143 24-34 (2013).

45. Zheng, Y., Xiao, Y., Seo, Y.: A tabu search algorithm for simultaneous machine/AGV scheduling problem. International Journal of Production Research 52(19) 5748-5763 (2014).

46. Baruwa, O.T., Piera, M.A.: A coloured petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research 54 4773-4792 (2016).

47. Nouri, H.E., Driss, O.B., Ghedira, K.: Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model. Computers & Industrial Engineering 102 488-501 (2016).

48. Dolgui, A., Proth, J.-M.: Special section on radio frequency identification, IEEE Transactions on Industrial Informatics 8 688 (2012).

49. He, Y., Smith, M.L., Dudek, R.A.: Robotic material handler scheduling in flexible manufacturing systems for mass customization. Robotics and Computer-Integrated Manufacturing 26 671-676 (2010).