Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm

Abstract: Use of Automated guided vehicles (AGVs) is highly significant in Flexible Manufacturing Sys-tem (FMS) in which material handling in form of jobs is performed from one work center to an-other work center. A multifold increase in through put of FMS can be observed by application of multi load AGVs. In this paper, Particle Swarm Optimization (PSO) integrated with Memetic Algorithm (MA) named as Modified Memetic Particle Swarm Optimization Algorithm (MMP-SO) is applied to yield initial feasible solutions for scheduling of multi load AGVs for minimum travel and waiting time in the FMS. The proposed MMPSO algorithm exhibits balanced explora-tion and exploitation for global search method of standard Particle Swarm Optimization (PSO) algorithm and local search method of Memetic Algorithm (MA) which further results into yield of efficient and effective initial feasible solutions for the multi load AGVs scheduling problem.

How to cite this paper

 Chawla, V., Chanda, A & Angra, S. (2018). Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm.Journal of Project Management, 3(1), 39-54.

Refrences

Akturk, M. S., & Yilmaz, H. (1996). Scheduling of automated guided vehicles in a decision making hierarchy. International Journal of Production Research, 34(2), 577-591.
Egbelu, P. J., & Tanchoco, J. M. A. (1986). Potentials for bi-directional guide-path for automated guided vehicle based systems. International Journal of Production Research, 24(5), 1075-1097.
Erol, R., Sahin, C., Baykasoglu, A., & Kaplanoglu, V. (2012). A multi-agent based approach to dy-namic scheduling of machines and automated guided vehicles in manufacturing systems. Applied soft computing, 12(6), 1720-1732.
Fazlollahtabar, H., Saidi-Mehrabad, M., & Balakrishnan, J. (2015). Mathematical optimization for ear-liness/tardiness minimization in a multiple automated guided vehicle manufacturing system via in-tegrated heuristic algorithms. Robotics and Autonomous Systems, 72, 131-138.
Fleischmann, B., Gnutzmann, S., & Sandvoß, E. (2004). Dynamic vehicle routing based on online traffic information. Transportation science, 38(4), 420-433.
Gaskins, R. J., & Tanchoco, J. M. (1987). Flow path design for automated guided vehicle sys-tems. International Journal of Production Research, 25(5), 667-676.
Gaskins, R. J., Tanchoco, J. M. A., & Taghaboni, F. (1989). Virtual flow paths for free-ranging auto-mated guided vehicle systems. The International Journal of Production Research 27(1), 91-100.
Grunow, M., Günther, H. O., & Lehmann, M. (2005). Dispatching multi-load AGVs in highly auto-mated seaport container terminals. Container Terminals and Automated Transport Systems Part I, 231-255.
Ho, Y. C., & Liao, T. W. (2009). Zone design and control for vehicle collision prevention and load balancing in a zone control AGV system. Computers & Industrial Engineering, 56(1), 417-432.
Jerald, J., Asokan, P., Saravanan, R., & Rani, A. D. C. (2006). Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm. The Interna-tional Journal of Advanced Manufacturing Technology, 29(5), 584-589.
Kumar, N. S., & Sridharan, R. (2010). Simulation-based metamodels for the analysis of scheduling decisions in a flexible manufacturing system operating in a tool-sharing environment. The Interna-tional Journal of Advanced Manufacturing Technology, 51(1-4), 341-355.
Levitin, G., & Abezgaouz, R. (2003). Optimal routing of multiple-load AGV subject to LIFO loading constraints. Computers & Operations Research, 30(3), 397-410.
Mantel, R. J., & Landeweerd, H. R. (1995). Design and operational control of an AGV sys-tem. International Journal of Production Economics, 41(1-3), 257-266.
Meersmans, P. J. M. (2002). Optimization of container handling systems.
Nayyar, P., & Khator, S. K. (1993). Operational control of multi-load vehicles in an automated guid-ed vehicle system. Computers & industrial engineering, 25(1-4), 503-506.
Powell, W. B., Towns, M. T., & Marar, A. (2000). On the value of optimal myopic solutions for dy-namic routing and scheduling problems in the presence of user noncompliance. Transportation Sci-ence, 34(1), 67-85.
Qiu, L., Hsu, W. J., Huang, S. Y., & Wang, H. (2002). Scheduling and routing algorithms for AGVs: a survey. International Journal of Production Research, 40(3), 745-760.
Rashidi, H. (2010). Scheduling in container terminals using Network Simplex Algorithm. Journal of Optimization in Industrial Engineering, 9-16.
Rashidi, H., & Tsang, E. (2015). Vehicle Scheduling in Port Automation: Advanced Algorithms for Minimum Cost Flow Problems. CRC Press.
Sadaghiani, J., Boroujerdi, S., Mirhabibi, M., & Sadaghiani, P. (2014). A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem. Decision Sci-ence Letters, 3(2), 157-168.
Ulusoy, G., Sivrikaya-Şerifoǧlu, F., & Bilge, Ü. (1997). A genetic algorithm approach to the simulta-neous scheduling of machines and automated guided vehicles. Computers & Operations Re-search, 24(4), 335-351.
Umar, U. A., Ariffin, M. K. A., Ismail, N., & Tang, S. H. (2015). Hybrid multiobjective genetic algo-rithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. The International Journal of Ad-vanced Manufacturing Technology, 81(9-12), 2123-2141.
Van der Meer, R. (2000). Operational control of internal transport(No. TTS; T2000/5).
Veeravalli, B., Rajesh, G., & Viswanadham, N. (2002). Design and analysis of optimal material distri-bution policies in flexible manufacturing systems using a single AGV. International journal of pro-duction research, 40(12), 2937-2954.
Yang, C., Choi, Y., & Ha, T. (2004). Simulation-based performance evaluation of transport vehicles at automated container terminals. OR spectrum, 26(2), 149-170.