َA Developed Cuckoo Search Algorithm for Solving a new Model of the Machine and Vehicle Scheduling
Subject Areas : Industrial Management
1 - Faculty member of Islamic Azad university Saveh branch
Keywords: Cuckoo search algorithm, Machine scheduling, Vehicle Scheduling,
Abstract :
In this paper, a new machine and vehicle simultaneous scheduling model has been developed taking into account the feasibility of transport, and the dependence of transport time on the type of work, considering the stopping time of the machine and its repair time, which is compatible with the industry environment. To find the answer, the multi-objective cuckoo search algorithm has been developed and for comparing and testing its efficiency, two other algorithms with the same structure have been used. The results obtained by the developed multi-objective cuckoo search algorithm were compared with other algorithms and the results show the superior quality of the solutions of the developed multi-objective cuckoo search algorithm for solving this type of problem. Therefore, using this new problem with the proposed solution method in the industrial environment will simultaneously reduce processing costs and increase the level of product quality and increase the level of customer service.
References:
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Fan, X., He, Q., & Zhang, Y. (2015). Zone Design of Tandem Loop AGVs Path with Hybrid Algorithm. IFAC-PapersOnLine, 48(3), 869-874.
Hamed Fazlollahtabar, M. S.-M., Jaydeep Balakrishnan. (2015). Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms. In Robotics and Autonomous Systems. 72, 131-138.
Heger, J., & Voss, T. (2018). Optimal scheduling of AGVs in a reentrant blocking job-shop. Procedia CIRP, 67, 41-45.
Jolai, F., Asefi, H., Rabiee, M., & Ramezani, P. (2013). Bi-objective simulated annealing approaches for no-wait two-stage flexible flow shop scheduling problem. Scientia Iranica, 20(3), 861-872.
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Maghsoudlou, H., Afshar-Nadjafi, B., & Niaki, S. T. A. (2016). A multi-objective invasive weeds optimization algorithm for solving multi-skill multi-mode resource constrained project scheduling problem. Computers & Chemical Engineering, 88, 157-169. doi:http://dx.doi.org/10.1016/j.compchemeng.2016.02.018
Naderi, B., Fatemi Ghomi, S., Aminnayeri, M., & Zandieh, M. (2011). Scheduling open shops with parallel machines to minimize total completion time. Journal of Computational and Applied Mathematics, 235(5), 1275-1287.
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Rao, R., & Kalyankar, V. (2011). Parameters optimization of advanced machining processes using TLBO algorithm. EPPM, Singapore, 20, 21-31.
Rao, R. V., Savsani, V. J., & Vakharia, D. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1-15.
Umar, U. A., Ariffin, M. K. A., Ismail, N., & Tang, S. H. (2015). Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. The International Journal of Advanced Manufacturing Technology, 81(9), 2123-2141. doi:10.1007/s00170-015-7329-2.
Zeng, C., Tang, J., & Yan, C. (2014). Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles. Applied Soft Computing, 24, 1033-1046.
Zhao, X., Liu, H., Lin, S., & Chen, Y. (2020). Design And Implementation Of A Mu+6302ltiple Agv Scheduling Algorithm For A Job-Shop. International Journal of Simulation Modelling (IJSIMM), 19(1).
Zhong, M., Yang, Y., Dessouky, Y., & Postolache, O. (2020). Multi-AGV scheduling for conflict-free path planning in automated container terminals. Computers & Industrial Engineering, 142, 106371. doi:https://doi.org/10.1016/j.cie.2020.106371.
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Coello, C. A. C., Lamont, G. B., & Van Veldhuisen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems, (Vol. 5, pp. 79-104). New York: Springer.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. Evolutionary Computation, IEEE Transactions on, 8(3), 256-279.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, & H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, September 18–20, 2000 Proceedings (pp. 849-858). Berlin, Heidelberg: Springer Berlin Heidelberg.
Fan, X., He, Q., & Zhang, Y. (2015). Zone Design of Tandem Loop AGVs Path with Hybrid Algorithm. IFAC-PapersOnLine, 48(3), 869-874.
Hamed Fazlollahtabar, M. S.-M., Jaydeep Balakrishnan. (2015). Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms. In Robotics and Autonomous Systems. 72, 131-138.
Heger, J., & Voss, T. (2018). Optimal scheduling of AGVs in a reentrant blocking job-shop. Procedia CIRP, 67, 41-45.
Jolai, F., Asefi, H., Rabiee, M., & Ramezani, P. (2013). Bi-objective simulated annealing approaches for no-wait two-stage flexible flow shop scheduling problem. Scientia Iranica, 20(3), 861-872.
Karimi, N., Zandieh, M., & Karamooz, H. R. (2010). Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications, 37(6), 4024-4032. doi:http://dx.doi.org/10.1016/j.eswa.2009.09.005.
Liu, Y., Ji, S., Su, Z., & Guo, D. (2019). Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm. PloS one, 14(12), e0226161.
Maghsoudlou, H., Afshar-Nadjafi, B., & Niaki, S. T. A. (2016). A multi-objective invasive weeds optimization algorithm for solving multi-skill multi-mode resource constrained project scheduling problem. Computers & Chemical Engineering, 88, 157-169. doi:http://dx.doi.org/10.1016/j.compchemeng.2016.02.018
Naderi, B., Fatemi Ghomi, S., Aminnayeri, M., & Zandieh, M. (2011). Scheduling open shops with parallel machines to minimize total completion time. Journal of Computational and Applied Mathematics, 235(5), 1275-1287.
Nouri, H. E., Driss, O. B., & Ghédira, K. (2016). Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment. Applied Intelligence, 1-21. doi:10.1007/s10489-016-0786-y.
Rahman, H. F., & Nielsen, I. (2019). Scheduling automated transport vehicles for material distribution systems. Applied Soft Computing, 82, 105552.
Rahman Humyun, F., Janardhanan Mukund, N., & Nielsen, P. (2020). An integrated approach for line balancing and AGV scheduling towards smart assembly systems. Assembly Automation, 40(2), 219-234. doi:10.1108/AA-03-2019-0057.
Rao, R., & Kalyankar, V. (2011). Parameters optimization of advanced machining processes using TLBO algorithm. EPPM, Singapore, 20, 21-31.
Rao, R. V., Savsani, V. J., & Vakharia, D. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1-15.
Umar, U. A., Ariffin, M. K. A., Ismail, N., & Tang, S. H. (2015). Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. The International Journal of Advanced Manufacturing Technology, 81(9), 2123-2141. doi:10.1007/s00170-015-7329-2.
Zeng, C., Tang, J., & Yan, C. (2014). Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles. Applied Soft Computing, 24, 1033-1046.
Zhao, X., Liu, H., Lin, S., & Chen, Y. (2020). Design And Implementation Of A Mu+6302ltiple Agv Scheduling Algorithm For A Job-Shop. International Journal of Simulation Modelling (IJSIMM), 19(1).
Zhong, M., Yang, Y., Dessouky, Y., & Postolache, O. (2020). Multi-AGV scheduling for conflict-free path planning in automated container terminals. Computers & Industrial Engineering, 142, 106371. doi:https://doi.org/10.1016/j.cie.2020.106371.
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