How to cite this paper
APA: Chawla, V., Chanda, A & Angra, S. (2019). The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm. Journal of Project Management, 4(1), 19-30.
Chicago/Turabian: Chawla, V., Chanda, A & Angra, S. 2019. "The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm." Journal of Project Management 4, no. 1 (2019): 19-30.
AMA: Chawla, V., Chanda, A & Angra, S. The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm. Journal of Project Management. 2019;4(1):19-30.
References
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Angra, S., Chanda, A., & Chawla, V. (2018). 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. Management Science Letters, 8(4), 187-200.
Bozorg-Haddad, O. (2017). Advanced Optimization by Nature-Inspired Algorithms.
Chanda, A., Angra, S., & Chawla, V. (2018). A Modified Memetic Particle Swarm Optimization Al-gorithm for Sustainable Multi-objective Scheduling of Automatic Guided Vehicles in a Flexible Manufacturing System. International Journal of Computer Aided Manufacturing, 4(1), 33-47.
Chawla, V.K., Chanda, A., & Angra, S. (2018a). Scheduling of multi-load AGVs in FMS by modi-fied memetic particle swarm optimization algorithm. Journal of Project Management, 3(1), 39-54.
Chawla, V.K., Chanda, A., & Angra, S. (2018b). Automatic guided vehicles fleet size optimization for flexible manufacturing system by grey wolf optimization algorithm. Management Science Let-ters, 8(2), 79-90.
Chawla, V., Chanda, A., Angra, S., & Chawla, G. (2018 c). The sustainable project management: A review and future possibilities. Journal of Project Management, 3(3), 157-170.
Chawla, V.K., Chanda, A., & Angra, S. (2018d). A clonal selection algorithm for minimizing distance travel & back-tracking of automatic guided vehicles in a flexible manufacturing system. Journal of The Institution of Engineers (India): Series C, DOI: 10.1007/s40032-018-0447-5.
Chawla, V.K., Chanda, A., & Angra, S. (2018e). Sustainable multi-objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm. Interna-tional Journal of Data and Network Science. DOI: 10.5267/j.ijdns.2018.6.001
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.
Fleischmann, B., Gnutzmann, S., & Sandvoß, E. (2004). Dynamic vehicle routing based on online traffic information. Transportation Science, 38(4), 420-433.
Grunow, M., Günther, H. O., & Lehmann, M. (2005). Dispatching multi-load AGVs to highly auto-mated seaport container terminals. Container Terminals and Automated Transport Systems Part I, 231-255.
Jerald, J., Asokan, P., Saravanan, R., & Rani, A. D. C. (2006). Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using the adaptive genetic algorithm. The In-ternational Journal of Advanced Manufacturing Technology, 29(5), 584-589.
Kashyap, S. K., & Thakkar, J. (2012). Job-Shop Scheduling in a Make-to-Order Company: An appli-cation of ‘Palmer’s Heuristic Approach’ and ‘Two Machine Fictitious Rule’. Journal of The Institu-tion of Engineers (India): Series C, 93(1), 103-109.
Komaki, G. M., & Kayvanfar, V. (2015). Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. Journal of Computational Science, 8, 109-120.
Kumar, N. S., & Sridharan, R. (2010). Simulation-based meta-models 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.
Lu, C., Gao, L., Li, X., & Xiao, S. (2017). A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Engineering Applications of Artificial Intelligence, 57, 61-79.
Meersmans, P. J. M. (2002). Optimization of container handling systems.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Soft-ware, 69, 46-61.
Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimiz-er: a novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119.
Moghadam, B. F., Sadjadi, S. J., & Seyedhosseini, S. M. (2010). An empirical analysis on robust ve-hicle routing problem: a case study on drug industry. International Journal of Logistics Systems and Management, 7(4), 507-518.
Moghaddam, B. F., Ruiz, R., & Sadjadi, S. J. (2012). Vehicle routing problem with uncertain de-mands: An advanced particle swarm algorithm. Computers & Industrial Engineering, 62(1), 306-317.
Nawaz, M., Enscore Jr, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95.
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.
Saad, A., Biswas, G., Kawamura, K., & Johnson, E. M. (1997a). The effectiveness of dynamic re-scheduling in agent-based flexible manufacturing systems. In Architectures, Networks, and Intelli-gent Systems for Manufacturing Integration (Vol. 3203, pp. 88-100). International Society for Optics and Photonics.
Saad, A., Kawamura, K., & Biswas, G. (1997b). Performance evaluation of contract net-based heter-archical scheduling for flexible manufacturing systems. Intelligent Automation & Soft Computing, 3(3), 229-247.
Sadaghiani, J., Boroujerdi, S., Mirhabibi, M., & Sadaghiani, P. (2014). A Pareto archive floating search procedure for solving the multi-objective flexible job shop scheduling problem. Decision Science Letters, 3(2), 157-168.
Sadjadi, S. J., & Makui, A. (2002). An Algorithm to Compute the Complexity of a Static Production Planning (RESEARCH NOTE). International Journal of Engineering-Transactions A: Basics, 16(1), 57-60.
Sadrabadi, M. R., & Sadjadi, S. J. (2009). A new approach to solve multiple objective programming problems. International Journal of Industrial Engineering & Production Research, 20(1), 41-51.
Sen, K., Ghosh, S., & Sarkar, B. (2017). Comparison of Customer Preference for Bulk Material Han-dling Equipment through Fuzzy-AHP Approach. Journal of The Institution of Engineers (India): Series C, 98(3), 367-377.
Singh, R., & Khan, B. (2016). Meta-hierarchical-heuristic-mathematical-model of loading problems in a flexible manufacturing system for development of an intelligent approach. International Journal of Industrial Engineering Computations, 7(2), 177-190
Singh, S. K., & Singh, M. K. (2012). Evaluation of productivity, quality, and flexibility of an ad-vanced manufacturing system. Journal of The Institution of Engineers (India): Series C, 93(1), 93-101.
Udhayakumar, P., & Kumanan, S. (2010). Task scheduling of AGV in FMS using non-traditional op-timization techniques. International Journal of Simulation Modelling, 9(1), 28-39.
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.
Yang, C., Choi, Y., & Ha, T. (2004). Simulation-based performance evaluation of transport vehicles at automated container terminals. OR Spectrum, 26(2), 149-170.
Angra, S., Chanda, A., & Chawla, V. (2018). 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. Management Science Letters, 8(4), 187-200.
Bozorg-Haddad, O. (2017). Advanced Optimization by Nature-Inspired Algorithms.
Chanda, A., Angra, S., & Chawla, V. (2018). A Modified Memetic Particle Swarm Optimization Al-gorithm for Sustainable Multi-objective Scheduling of Automatic Guided Vehicles in a Flexible Manufacturing System. International Journal of Computer Aided Manufacturing, 4(1), 33-47.
Chawla, V.K., Chanda, A., & Angra, S. (2018a). Scheduling of multi-load AGVs in FMS by modi-fied memetic particle swarm optimization algorithm. Journal of Project Management, 3(1), 39-54.
Chawla, V.K., Chanda, A., & Angra, S. (2018b). Automatic guided vehicles fleet size optimization for flexible manufacturing system by grey wolf optimization algorithm. Management Science Let-ters, 8(2), 79-90.
Chawla, V., Chanda, A., Angra, S., & Chawla, G. (2018 c). The sustainable project management: A review and future possibilities. Journal of Project Management, 3(3), 157-170.
Chawla, V.K., Chanda, A., & Angra, S. (2018d). A clonal selection algorithm for minimizing distance travel & back-tracking of automatic guided vehicles in a flexible manufacturing system. Journal of The Institution of Engineers (India): Series C, DOI: 10.1007/s40032-018-0447-5.
Chawla, V.K., Chanda, A., & Angra, S. (2018e). Sustainable multi-objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm. Interna-tional Journal of Data and Network Science. DOI: 10.5267/j.ijdns.2018.6.001
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.
Fleischmann, B., Gnutzmann, S., & Sandvoß, E. (2004). Dynamic vehicle routing based on online traffic information. Transportation Science, 38(4), 420-433.
Grunow, M., Günther, H. O., & Lehmann, M. (2005). Dispatching multi-load AGVs to highly auto-mated seaport container terminals. Container Terminals and Automated Transport Systems Part I, 231-255.
Jerald, J., Asokan, P., Saravanan, R., & Rani, A. D. C. (2006). Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using the adaptive genetic algorithm. The In-ternational Journal of Advanced Manufacturing Technology, 29(5), 584-589.
Kashyap, S. K., & Thakkar, J. (2012). Job-Shop Scheduling in a Make-to-Order Company: An appli-cation of ‘Palmer’s Heuristic Approach’ and ‘Two Machine Fictitious Rule’. Journal of The Institu-tion of Engineers (India): Series C, 93(1), 103-109.
Komaki, G. M., & Kayvanfar, V. (2015). Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. Journal of Computational Science, 8, 109-120.
Kumar, N. S., & Sridharan, R. (2010). Simulation-based meta-models 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.
Lu, C., Gao, L., Li, X., & Xiao, S. (2017). A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Engineering Applications of Artificial Intelligence, 57, 61-79.
Meersmans, P. J. M. (2002). Optimization of container handling systems.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Soft-ware, 69, 46-61.
Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimiz-er: a novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119.
Moghadam, B. F., Sadjadi, S. J., & Seyedhosseini, S. M. (2010). An empirical analysis on robust ve-hicle routing problem: a case study on drug industry. International Journal of Logistics Systems and Management, 7(4), 507-518.
Moghaddam, B. F., Ruiz, R., & Sadjadi, S. J. (2012). Vehicle routing problem with uncertain de-mands: An advanced particle swarm algorithm. Computers & Industrial Engineering, 62(1), 306-317.
Nawaz, M., Enscore Jr, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95.
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.
Saad, A., Biswas, G., Kawamura, K., & Johnson, E. M. (1997a). The effectiveness of dynamic re-scheduling in agent-based flexible manufacturing systems. In Architectures, Networks, and Intelli-gent Systems for Manufacturing Integration (Vol. 3203, pp. 88-100). International Society for Optics and Photonics.
Saad, A., Kawamura, K., & Biswas, G. (1997b). Performance evaluation of contract net-based heter-archical scheduling for flexible manufacturing systems. Intelligent Automation & Soft Computing, 3(3), 229-247.
Sadaghiani, J., Boroujerdi, S., Mirhabibi, M., & Sadaghiani, P. (2014). A Pareto archive floating search procedure for solving the multi-objective flexible job shop scheduling problem. Decision Science Letters, 3(2), 157-168.
Sadjadi, S. J., & Makui, A. (2002). An Algorithm to Compute the Complexity of a Static Production Planning (RESEARCH NOTE). International Journal of Engineering-Transactions A: Basics, 16(1), 57-60.
Sadrabadi, M. R., & Sadjadi, S. J. (2009). A new approach to solve multiple objective programming problems. International Journal of Industrial Engineering & Production Research, 20(1), 41-51.
Sen, K., Ghosh, S., & Sarkar, B. (2017). Comparison of Customer Preference for Bulk Material Han-dling Equipment through Fuzzy-AHP Approach. Journal of The Institution of Engineers (India): Series C, 98(3), 367-377.
Singh, R., & Khan, B. (2016). Meta-hierarchical-heuristic-mathematical-model of loading problems in a flexible manufacturing system for development of an intelligent approach. International Journal of Industrial Engineering Computations, 7(2), 177-190
Singh, S. K., & Singh, M. K. (2012). Evaluation of productivity, quality, and flexibility of an ad-vanced manufacturing system. Journal of The Institution of Engineers (India): Series C, 93(1), 93-101.
Udhayakumar, P., & Kumanan, S. (2010). Task scheduling of AGV in FMS using non-traditional op-timization techniques. International Journal of Simulation Modelling, 9(1), 28-39.
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.
Yang, C., Choi, Y., & Ha, T. (2004). Simulation-based performance evaluation of transport vehicles at automated container terminals. OR Spectrum, 26(2), 149-170.