A Clonal Selection Algorithm for Minimizing Distance Travel and Back Tracking of Automatic Guided Vehicles in Flexible Manufacturing System
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Abstract
The flexible manufacturing system (FMS) constitute of several programmable production work centers, material handling systems (MHSs), assembly stations and automatic storage and retrieval systems. In FMS, the automatic guided vehicles (AGVs) play a vital role in material handling operations and enhance the performance of the FMS in its overall operations. To achieve low makespan and high throughput yield in the FMS operations, it is highly imperative to integrate the production work centers schedules with the AGVs schedules. The Production schedule for work centers is generated by application of the Giffler and Thompson algorithm under four kind of priority hybrid dispatching rules. Then the clonal selection algorithm (CSA) is applied for the simultaneous scheduling to reduce backtracking as well as distance travel of AGVs within the FMS facility. The proposed procedure is computationally tested on the benchmark FMS configuration from the literature and findings from the investigations clearly indicates that the CSA yields best results in comparison of other applied methods from the literature.
Keywords
Automatic guided vehicles Clonal selection algorithm Flexible manufacturing system Priority hybrid dispatching rulesNotes
Acknowledgement
The author is thankful to the reviewers for their constructive comments for an earlier version of this research paper.
References
- 1.V.K. Chawla, A. Chanda, S. Angra, The sustainable project management: a review and future possibilities. J. Proj. Manag. (2018). https://doi.org/10.5267/j.jpm.2018.2.001 Google Scholar
- 2.S.K. Kashyap, J. Thakkar, Job-shop scheduling in a make-to-order company: an application of ‘Palmer’s Heuristic Approach’ and ‘Two Machine Fictitious Rule’. J. Inst. Eng. (India) Ser. C 93(1), 103–109 (2012)CrossRefGoogle Scholar
- 3.P. Udhayakumar, S. Kumanan, Integrated scheduling of flexible manufacturing system using evolutionary algorithms. Int. J. Adv. Manuf. Technol. 61(5), 621–635 (2012)CrossRefGoogle Scholar
- 4.K. Sen, S. Ghosh, B. Sarkar, Comparison of customer preference for bulk material handling equipment through fuzzy-AHP approach. J. Inst. Eng. (India): Ser. C 98(3), 367–377 (2017)Google Scholar
- 5.S. Rajotia, K. Shanker, J.L. Batra, A semi-dynamic time window constrained routing strategy in an AGV system. Int. J. Prod. Res. 36(1), 35–50 (1998)CrossRefMATHGoogle Scholar
- 6.F. Taghaboni-Dutta, J.M.A. Tanchoco, Comparison of dynamic routing techniques for automated guided vehicle system. Int. J. Prod. Res. 33(10), 2653–2669 (1995)CrossRefMATHGoogle Scholar
- 7.B. Giffler, G.L. Thompson, Algorithms for solving production-scheduling problems. Oper. Res. 8(4), 487–503 (1960)MathSciNetCrossRefMATHGoogle Scholar
- 8.S.K. Singh, M.K. Singh, Evaluation of productivity, quality, and flexibility of an advanced manufacturing system. J. Inst. Eng. (India): Ser. C 93(1), 93–101 (2012)Google Scholar
- 9.K.E. Stecke, Design, planning, scheduling, and control problems of flexible manufacturing systems. Ann. Oper. Res. 3(1), 1–12 (1985)MathSciNetCrossRefGoogle Scholar
- 10.I. Sabuncuoglu, D.L. Hommertzheim, Dynamic dispatching algorithm for scheduling machines and automated guided vehicles in a flexible manufacturing system. Int. J. Prod. Res. 30(5), 1059–1079 (1992)CrossRefGoogle Scholar
- 11.K. Suleyman, S. Ihsan, Beam search based algorithm for scheduling machines and AGVs in an FMS, in Proceedings of the Industrial Engineering Research Conference, pp. 308–312. Publ by IIE, Norcross, GA, United States (1993)Google Scholar
- 12.D.Y. Lee, F. Di Cesare, Integrated scheduling of flexible manufacturing systems employing automated guided vehicles. IEEE Trans. Ind. Electron. 41(6), 602–610 (1994)CrossRefGoogle Scholar
- 13.G. Ulusoy, Ü. Bilge, Simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 31(12), 2857–2873 (1993)CrossRefGoogle Scholar
- 14.A. Saad, G. Biswas, K. Kawamura, E.M. Johnson, The effectiveness of dynamic rescheduling in agent-based flexible manufacturing systems, in Architectures, Networks, and Intelligent Systems for Manufacturing Integration, vol 3203 (International Society for Optics and Photonics), pp. 88–100Google Scholar
- 15.A. Saad, K. Kawamura, G. Biswas, Performance evaluation of contract net-based heterarchical scheduling for flexible manufacturing systems. Intell. Autom. Soft Comput. 3(3), 229–247 (1997)CrossRefGoogle Scholar
- 16.S.H. Kim, H. Hwang, An adaptive dispatching algorithm for automated guided vehicles based on an evolutionary process. Int. J. Prod. Econ. 60, 465–472 (1999)CrossRefGoogle Scholar
- 17.A.N. Haq, T. Karthikeyan, M. Dinesh, Scheduling decisions in FMS using a heuristic approach. Int. J. Adv. Manuf. Technol. 22(5–6), 374–379 (2003)CrossRefGoogle Scholar
- 18.T.F. Abdelmaguid, A.O. Nassef, B.A. Kamal, M.F. Hassan, A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 42(2), 267–281 (2004)CrossRefMATHGoogle Scholar
- 19.J. Jerald, P. Asokan, G. Prabaharan, R. Saravanan, Scheduling optimization of flexible manufacturing systems using particle swarm optimization algorithm. Int. J. Adv. Manuf. Technol. 25(9), 964–971 (2005)CrossRefGoogle Scholar
- 20.Y.C. Ho, H.C. Liu, The performance of load-selection rules and pickup-dispatching rules for multiple-load AGVs. J. Manuf. Syst. 28(1), 1–10 (2009)CrossRefGoogle Scholar
- 21.W.J. Xia, Z.M. Wu, A hybrid particle swarm optimization approach for the job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 29(3), 360–363 (2006)CrossRefGoogle Scholar
- 22.K.L. Huang, C.J. Liao, Ant colony optimization combined with the taboo search for the job shop scheduling problem. Comput. Oper. Res. 35(4), 1030–1046 (2008)MathSciNetCrossRefMATHGoogle Scholar
- 23.V.K. Chawla, A. Chanda, S. Angra, Automatic guided vehicles fleet size optimization for flexible manufacturing system by grey wolf optimization algorithm. Manag. Sci. Lett. 8(2), 79–90 (2018)CrossRefGoogle Scholar
- 24.S.G. Ponnambalam, L.S. Kiat, Solving machine loading problem in flexible manufacturing systems using particle swarm optimization. World Acad. Sci. Eng. Technol. 39, 14–19 (2008)Google Scholar
- 25.A. Gnanavelbabu, J. Jerald, A. Noorul Haq, P. Asokan, Multi-objective scheduling of jobs, AGVs and AS/RS in FMS using the artificial immune system. In Proceedings of National Conference on Emerging trends in Engineering and Sciences, pp. 229–239 (2009)Google Scholar
- 26.A.H. Kashan, B. Karimi, A discrete particle swarm optimization algorithm for scheduling parallel machines. Comput. Ind. Eng. 56(1), 216–223 (2009)CrossRefGoogle Scholar
- 27.B.F. Moghaddam, R. Ruiz, S.J. Sadjadi, Vehicle routing problem with uncertain demands: an advanced particle swarm algorithm. Comput. Ind. Eng. 62(1), 306–317 (2012)CrossRefGoogle Scholar
- 28.Y.C. Wang, T. Chen, H. Chiang, H.C. Pan, A simulation analysis of part launching and order collection decisions for a flexible manufacturing system. Simul. Model. Pract. Theory 69, 80–91 (2016)CrossRefGoogle Scholar
- 29.V.K. Chawla, A. K. Chanda, S. Angra, Evaluation of Dispatching Rules for Integrated Scheduling of AGVs in FMS, in National Conference on Recent Advances in Mechanical Engineering (NCRAME), pp. 37–41, ISBN: 978-93-86256-89-8, NIT, Kurukshetra, Haryana, India (2017)Google Scholar
- 30.V.K. Kumar, A. Chanda, S. Angra, Evaluation of hybrid dispatching rules for simultaneous scheduling of AGVs in FMS, in 1st International Conference on New Frontiers in Engineering, Science and Technology, New Delhi, India, January 8–12, 2018, pp. 105–112 (2018)Google Scholar
- 31.V.K. Chawla, A. Chanda, S. Angra, Integrated scheduling of multi-load AGVs by priority hybrid dispatching rules in FMS-a simulation study, in INCOM18: Proceedings of the 1st International Conference on Mechanical Engineering, Jadavpur University, Kolkata, India (2018)Google Scholar
- 32.V.K. Chawla, A. Chanda, S. Angra, Scheduling of multi-load AGVs in FMS by modified memetic particle swarm optimization algorithm. J. Proj. Manag. 3(1), 39–54 (2018)Google Scholar
- 33.A.K. Kaban, Z. Othman, D.S. Rohmah, Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study. Int. J. Simul. Model. 11(3), 129–140 (2012)CrossRefGoogle Scholar
- 34.K. Deb, Multi-objective optimization using evolutionary algorithms, vol 16 (Wiley, 2001)Google Scholar
- 35.N. Shukla, P.K.S. Prakash, Multiple Fault Diagnosis Using Psycho—Clonal Algorithms. Evolutionary Computing in Advanced Manufacturing, pp. 235–258 (2011)Google Scholar
- 36.V. Cutello, G. Nicosia, An immunological approach to combinatorial optimization problems. Advances in Artificial Intelligence—IBERAMIA 2002, pp. 361–370 (2002)Google Scholar
- 37.A. Król, The application of the artificial intelligence methods for the planning of the development of the transportation network. Transportation Research Procedia 14, 4532–4541 (2016)CrossRefGoogle Scholar
- 38.D. Laha (Ed.). Handbook of Computational Intelligence in Manufacturing and Production Management. IGI Global (2007)Google Scholar
- 39.J. Brownlee, Clever Algorithms: Nature-Inspired Programming Recipes. (2011)Google Scholar
- 40.M. Chandrasekaran, P. Asokan, S. Kumanan, T. Balamurugan, S. Nickolas, Solving job shop scheduling problems using artificial immune system. Int. J. Adv. Manuf. Technol. 31(5–6), 580–593 (2006)CrossRefGoogle Scholar
- 41.D. Nam, C.H. Park, Multiobjective simulated annealing: a comparative study to evolutionary algorithms. Int. J. Fuzzy Syst. 2(2), 87–97 (2000)Google Scholar