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A Modified Memetic Particle Swarm Optimization Algorithm for Sustainable Multi-objective Scheduling of Automatic Guided Vehicles in a Flexible Manufacturing System

Arindam Kumar Chanda, V. K. Chawla, S. K. Angra

Abstract


In the present work, a modified memetic particle swarm optimization (MMPSO) algorithm was applied for a sustainable multi-objective scheduling of automatic guided vehicles AGVs in a flexible manufacturing system (FMS). To achieve sustainable scheduling results for AGVs in FMS, it is imperative to integrate the work center schedule with the AGV schedule, and thereafter, minimize their distance travel and back-tracking in the FMS facility. The work center schedule in the FMS is developed by the application of the Giffler and Thompson algorithm under four kinds of priority hybrid dispatching rules. Then a novel MMPSO algorithm is applied for multi-objective integrated scheduling to decrease AGV’s distance travel and the back-tracking. The resulting yield of MMPSO algorithm is compared and validated from the benchmark problem found in the literature.

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References


Angra S, Chanda A, Chawla V. Comparison and evaluation of job selection dispatching rules for integrated scheduling of multi-load automatic guided vehicles serving in variable sized flexible manufacturing system layouts: a simulation study. Manag Sci Lett. 2018; 8(4): 187–200p.

Chawla VK, Chanda A, Angra S. Scheduling of multi-load AGVs in FMS by modified memetic particle swarm optimization algorithm. J Proj Manag. 2018a; 3(1): 39–54.

Chawla VK, Chanda A, Angra S. The sustainable project management: A review and future possibilities. J Proj Manag. 2018b. DOI: 10.5267/j.jpm.2018.2.001

Chawla VK, Chanda A, Angra S. Automatic guided vehicles fleet size optimization for flexible manufacturing system by grey wolf optimization algorithm. Manag Sci Lett. 2018c; 8(2): 79–90p.

Chawla VK, Chanda A, Angra S. A clonal selection algorithm for minimizing distance travel & back tracking of automatic guided vehicles in flexible manufacturing system. J Inst Eng (India) C. 2018d. DOI: 10.1007/s40032-018-0447-5.

Chawla VK, Chanda A, Angra S. Sustainable multi objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm. International Journal of Data and Network Science 2018 e. DOI: 10.5267/j.ijdns.2018.6.001

Moghaddam BF, Ruiz R, Sadjadi SJ. Vehicle routing problem with uncertain demands: an advanced particle swarm algorithm. Comput Ind Engg. 2012; 62(1): 306–317p.

Pierson RA. Adapting horizontal material handling systems to flexible manufacturing set-ups. Ind Engg. 1984; 16: 62–64p.

Ponnambalam SG, Kiat LS. Solving machine loading problem in flexible manufacturing systems using particle swarm optimization. World Acad Sci Engg Technol. 2008; 39: 14–19p.

Rajotia S, Shanker K, Batra JL. A semi-dynamic time window constrained routeing strategy in an AGV system. Int J Prod Res. 1998; 36(1): 35–50p.

Sabuncuoglu I, Hommertzheim DL. Dynamic dispatching algorithm for scheduling machines and automated guided vehicles in a flexible manufacturing system. Int J Prod Res. 1992; 30(5): 1059–1079p.

Sadrabadi MR, Sadjadi SJ. A new approach to solve multiple objective programming problems. Int J Ind Engg Prod Res. 2009; 20(1): 41–51p.

Haq AN, Karthikeyan T, Dinesh M. Scheduling decisions in FMS using a heuristic approach. Int J Adv Manuf Technol. 2003; 22(5–6): 374–379p.

Suresh Kumar N, Sridharan R. Simulation-based metamodels for the analysis of scheduling decisions in a flexible manufacturing system operating in a tool-sharing environment. Int J Adv Manuf Technol. 2010; 51(1): 341–355p.

Eynan A, Rosenblatt MJ. An interleaving policy in automated storage/retrieval systems. Int J Prod Res. 1993; 31(1): 1–18p.

Giffler B, Thompson GL. Algorithms for solving production-scheduling problems. Oper Res. 1960; 8(4): 487–503p.

Kimemia J, Gershwin SB. Flow optimization in flexible manufacturing systems. Int J Prod Res. 1985; 23(1): 81–96p

Deb K. Multi-objective Optimization Using Evolutionary Algorithms, Vol. 16. Hoboken, NJ: John Wiley & Sons; 2001.

Taghaboni-Dutta F, Tanchoco JMA. Comparison of dynamic routing techniques for automated guided vehicle system. Int J Prod Res. 1995; 33(10): 2653–2669.

Ulusoy, G., & Bilge, Ü. (1993). Simultaneous scheduling of machines and automated guided vehicles. The International Journal of Production Research, 31(12), 2857-2873.

Lee DY, DiCesare F. Integrated scheduling of flexible manufacturing systems employing automated guided vehicles. IEEE Trans Ind Electron. 1994; 41(6): 602–610p.

Kim SH, Hwang H. An adaptive dispatching algorithm for automated guided vehicles based on an evolutionary process. Int J Prod Econ. 1999; 60: 465–472p.

Haq AN, Karthikeyan T, Dinesh M. Scheduling decisions in FMS using a heuristic approach. Int J Adv Manuf Technol. 2003; 22(5–6): 374–379p

Abdelmaguid TF, Nassef AO, Kamal BA, Hassan MF. A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int J Prod Res. 2004; 42(2): 267–281p.

Jerald J, Asokan P, Prabaharan G, Saravanan R. Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm. Int J Adv Manuf Technol. 2005; 25(9): 964–971p.

Huang KL, Liao CJ. Ant colony optimization combined with taboo search for the job shop scheduling problem. Comput Oper Res. 2008; 35(4): 1030–1046p.

Gnanavelbabu A, Jerald J, Noorul Haq A, Asokan P. Multi-objective scheduling of jobs, AGVs and AS/RS in FMS using artificial immune system. In: Proceedings of National conference on Emerging trends in Engineering and Sciences. 2009. pp. 229-239.

Ho YC, Liu HC. The performance of load-selection rules and pickup-dispatching rules for multiple-load AGVs. J Manuf Syst. 2009; 28(1): 1–10p.

Um I, Cheon H, Lee H. The simulation design and analysis of a flexible manufacturing system with automated guided vehicle system. J Manuf Syst. 2009; 28(4): 115–122p.

Azimi P, Haleh H, Alidoost M. The selection of the best control rule for a multiple-load AGV system using simulation and fuzzy MADM in a flexible manufacturing system. Model Simul Engg. 2010; 7.

Wang YC, Chen T, Chiang H, Pan HC. A simulation analysis of part launching and order collection decisions for a flexible manufacturing system. Simul Model Pract Theory. 2016; 69: 80–91p.

Kaban AK, Othman Z, Rohmah DS. Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study. Int J Simul Model. 2012; 11(3): 129–140p.

Sharma, S. Issues concerning optimization of non-conventional parameters in manufacturing. International Journal on Interactive Design and Manufacturing (IJIDeM), 2008; 2(4): 219.

Legarretaetxebarria, A., Quartulli, M., Olaizola, I. and Serrano, M. Optimal scheduling of manufacturing processes across multiple production lines by polynomial optimization and bagged bounded binary knapsack. International Journal on Interactive Design and Manufacturing (IJIDeM), 2017; 11(1): 83-91p.




DOI: https://doi.org/10.37628/jcam.v4i1.644

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