A New Approach to Solving Multi-Objective Flow-Shop Scheduling Problems: A MultiMoora-Based Genetic Algorithm
Flow-shop scheduling problems constitute a type of problem that is frequently discussed in the literature where a wide variety of methods are developed for their solution. Although the problem used to be set as a single purpose, it became necessary to expect more than one objective to be evaluated together with increasing customer expectation and competition, after which studies were started to be carried out under the title of multi-objective flow-shop scheduling. With the increase in the number of workbenches and jobs, the difficulty level of the problem increases in a nonlinear way, and the solution becomes more difficult. This study proposes a new hybrid algorithm by combining genetic algorithms, which are metaheuristic methods, and the MultiMoora method, which is a multi-criterion decision-making method, for the solution of multi-objective flow-shop scheduling problems. The study evaluates and tries to optimize the performance criteria of maximum completion time, average flow time, maximum late finishing, average tardiness and the number of late (tardy) jobs. The proposed algorithm is compared to the standard multi-objective genetic algorithm (MOGA) and the MultiMoora-based genetic algorithm (MBGA) shows better results.
Basseur, M., Seynhaeve, F., & Talbi, E. G. 2002. Design of multi-objective evolutionary algorithms: Application to the flow-shop scheduling problem. In Congress on Evolutionary Computation CEC'02, 2, 1151-1156.
Braglia, M., & Grassi, A. 2009. A new heuristic for the flowshop scheduling problem to minimize makespan and maximum tardiness. International Journal of Production Research, 47(1), 273-288.
Brauers, W. K. M., & Zavadskas, E. K. 2006. The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 445-469.
Brauers, W. K. M., & Zavadskas, E. K. 2010. Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5-24.
Brauers, W. K. M., & Zavadskas, E. K. 2012. Robustness of MULTIMOORA: a method for multi-objective optimization. Informatica, 23(1), 1-25.
Chang, P. C., Hsieh, J. C., & Lin, S. G. 2002. The development of gradual-priority weighting approach for the multi-objective flowshop scheduling problem. International Journal of Production Economics, 79(3), 171-183.
Chen, C. L., Vempati, V. S., & Aljaber, N. 1995. An application of genetic algorithms for flow shop problems. European Journal of Operational Research, 80(2), 389-396.
Das, S. R., Gupta, J. N., & Khumawala, B. M. 1995. A savings index heuristic algorithm for flowshop scheduling with sequence dependent set-up times. Journal of the Operational Research Society, 46(11), 1365-1373.
Datta, S., Sahu, N., & Mahapatra, S. 2013. Robot selection based on grey-MULTIMOORA approach. Grey Systems: Theory and Application, 3(2), 201-232.
Gonçalves, J. F., de Magalhães Mendes, J. J., & Resende, M. G. 2005. A hybrid genetic algorithm for the job shop scheduling problem. European journal of operational research, 167(1), 77-95.
Horn, J., Nafpliotis, N., & Goldberg, D. E. 1994. A niched Pareto genetic algorithm for multiobjective optimization. In Evolutionary Computation. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on, 82-87.
Ishibuchi, H., & Murata, T. 1996. Multi-objective genetic local search algorithm. In Evolutionary Computation, Proceedings of IEEE International Conference on, 119-124.
Ishibuchi, H., & Murata, T. 1998. A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 392-403.
Ishibuchi, H., Yoshida, T., & Murata, T. 2003. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE transactions on evolutionary computation, 7(2), 204-223.
Johnson, S. M. 1954. Optimal two‐and three‐stage production schedules with setup times included. Naval Research Logistics (NRL), 1(1), 61-68.
Keskin, K. 2010. Beklemesiz akış tipi çizelgeleme problemlerinin çok amaçlı melez genetik algoritma ile çözümü. Doctoral dissertation, Selçuk Üniversitesi Fen Bilimleri Enstitüsü.
Kundakcı, N. 2016. “Combined multi-criteria decision makingdecision-making approach based on MACBETH and MULTI-MOORA methods.,” Alphanumeric Journal, 4(1), 17-26, .2016.
Li, B. B., & Wang, L. 2007. A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(3), 576-591.
Li, B. B., Wang, L., & Liu, B. 2008. An effective PSO-based hybrid algorithm for multiobjective permutation flow shop scheduling. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 38(4), 818-831.
Lin, D., Lee, C. K. M., & Wu, Z. 2011. Integrated GA and AHP for re-entrant flow shop scheduling problem. International Conference on Quality and Reliability, 496-500.
Lin, D., Lee, C. K. M., & Wu, Z. 2012. Integrating analytical hierarchy process to genetic algorithm for re-entrant flow shop scheduling problem. International Journal of Production Research, 50(7), 1813-1824.
Marichelvam, M. K., Prabaharan, T., & Yang, X. S. 2014. A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE transactions on evolutionary computation, 18(2), 301-305.
Mokhtari, H., Abadi, I. N. K., & Cheraghalikhani, A. 2011. A multi-objective flow shop scheduling with resource-dependent processing times: trade-off between makespan and cost of resources. International Journal of Production Research, 49(19), 5851-5875.
Murata, T., Ishibuchi, H., & Tanaka, H. 1996a. Genetic algorithms for flowshop scheduling problems. Computers & Industrial Engineering, 30(4), 1061-1071.
Murata, T., Ishibuchi, H., & Tanaka, H. 1996b. Multi-objective genetic algorithm and its applications to flowshop scheduling. Computers & Industrial Engineering, 30(4), 957-968.
Pan, Q. K., Wang, L., & Qian, B. 2009. A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems. Computers & Operations Research, 36(8), 2498-2511.
Pasupathy, T., Rajendran, C., & Suresh, R. K. 2006. A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs. The International Journal of Advanced Manufacturing Technology, 27(7), 804-815.
Ponnambalam, S. G., Jagannathan, H., Kataria, M., & Gadicherla, A. 2004. A TSP-GA multi-objective algorithm for flow-shop scheduling. The International Journal of Advanced Manufacturing Technology, 23(11-12), 909-915.
Poon, P. W., & Carter, J. N. 1995. Genetic algorithm crossover operators for ordering applications. Computers & Operations Research, 22(1), 135-147.
Pour, N., Tavakkoli-Moghaddam, R., & Asadi, H. 2013. 5. Optimizing a multi-objectives flow shop scheduling problem by a novel genetic algorithm. International Journal of Industrial Engineering Computations, 4(3), 345-354.
Rajendran, C., & Chaudhuri, D. 1990. Heuristic algorithms for continuous flow‐shop problem. Naval Research Logistics (NRL), 37(5), 695-705.
Veeraiah, T., Pratapa Reddy, Y., V S Mohan Kumar, P., & W D S Milton, P. 2017. Optimization of Flow Shop Scheduling by MATLAB. SSRG International Journal of Mechanical Engineering (SSRG-IJME) – Special Issue May – 2017, 222-226.