A Multi-Objective Approach to Modelling the Integrated Resource Selection and Operation Sequences Problem in a Production System
A Multi-Objective Approach to Modelling the Integrated Resource Selection and Operation Sequences Problem in a Production System |
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© 2022 by IJETT Journal | ||
Volume-70 Issue-8 |
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Year of Publication : 2022 | ||
Authors : Miguel Fernández, Avid Roman-Gonzalez |
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DOI : 10.14445/22315381/IJETT-V70I8P205 |
How to Cite?
Miguel Fernández, Avid Roman-Gonzalez, "A Multi-Objective Approach to Modelling the Integrated Resource Selection and Operation Sequences Problem in a Production System," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 51-56, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P205
Abstract
This paper addresses the integrated resource selection and operation sequences problem. This production scheduling problem is an extension of the flow shop, job shop and flexible job shop problems, its main characteristics being the precedence relationship between operations that are part of customer orders, the lot size of orders and the flexibility of the machines. A mixed-integer programming model is proposed to solve the problem, simultaneously optimising two objectives. This class of problems with more than one objective is known as multi-objective optimization, which consists of obtaining the non-dominated solutions that are part of the Pareto frontier. The problem's first objective is to minimize the makespan or the shortest time to complete all the orders. The second objective of the problem is to balance the workload of the machines, which aims to prevent specific machines from having a low workload and other machines from having an excessive workload. The computational results show that the mathematical model could satisfactorily solve the cases or instances.
Keywords
Integrated resource selection operation sequences problem, Mixed-integer programing model, Non-dominated solutions, Pareto frontier.
Reference
[1] R. W. Buzzo, and J. V. Moccellin, “Production Scheduling in Flow Shop Systems Using a Hybrid Genetic Algorithm-Simulated Annealing Heuristic Method, Management & Production,” vol. 7, no. 3, pp. 364-377, 2000.
[2] M. Pinedo, “Scheduling Theory Algorithms and System,” New York: Editorial Prentice Hall, 2008.
[3] A. K.Agarwal, and R. Garg, “Advanced Modelistic Approach of Flowshop Scheduling Problem for 10-Jobs, 10-Machines by Heuristics Models Using Makespan Criterion,” International Journal of Engineering Trends and Technology, vol. 4, no. 5, pp. 1742- 1748, 2013.
[4] G. U. Sankar, and D. Saravanan, “Single Objective Evolutionary Algorithm for Job Shop Scheduling,” International Journal of Mathematics Trends and Technology, vol. 3, no. 1, pp. 38-42, 2012.
[5] M. Sophia, “Review on Scheduling-Job Scheduling,” International Journal of Engineering Trends and Technology, vol. 60, no. 1, pp. 41-44, 2018.
[6] I. Kacem, S. Hammadi, and P. Borne, “Approach by Localization and Multiobjective Evolutionary Optimization for Flexible Job-Shop Scheduling,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 32, no. 1, pp. 1-13, 2002a.
[7] I. Kacem, S. Hammadi, and P. Borne, “Pareto-Optimality Approach for Flexible Job-Shop Scheduling Problems: Hybridization of Evolutionary Algorithms and Fuzzy Logic,” Mathematics and Computers in Simulation, vol. 60, no. 3-5, pp. 245-276, 2002b.
[8] M. Nagamani, E. Chandrasekaran, and D. Saravanan, “Single Objective Evolutionary Algorithm for Flexible Job-Shop Scheduling Problem,” International Journal of Mathematics Trends and Technology, vol. 3, no. 2, pp. 78-81, 2012.
[9] H. Zhang, M. Gen, and Y. Seo, “An Effective Coding Approach for Multiobjective Integrated Resource Selection and Operation Sequences Problem,” Journal of Intelligent Manufacturing, vol. 17, no. 4, pp. 385-397, 2006.
[10] J. Du, andJ. Y.-T Leung, “Minimizing Total Tardiness on One Machine is NP-Hard,” Mathematics of Operations Research, vol. 15, no. 3, pp. 483-495, 1990.
[11] M. R. Garey, D. S. Johnson, and R. Sethi, “The Complexity of Flowshop and Jobshop Scheduling,” Mathematics of Operations Research, vol. 1, no. 2, pp. 117-129, 1976.
[12] C. Moon, M. Lee, Y. Seo, andY. H. Lee, “Integrated Machine Tool Selection and Operation Sequencing with Capacity and Precedence Constraints Using Genetic Algorithm,” Computers & Industrial Engineering, vol. 43, no. 3, pp. 605-621, 2002.
[13] Y. H. Lee, C. S. Yeong, and C. Moon, “Advanced Planning and Scheduling with Outsourcing in Manufacturing Supply Chain,” Computers & Industrial Engineering, vol. 43, no. 1-2, pp. 351-374, 2002.
[14] P. Yan, D. Liu, D. Yuan, and J. Yu, “Genetic Algorithm with Local Search for Advanced Planning and Scheduling,” Third International Conference on Natural Computation, vol. 3, pp. 781-785, 2007.
[15] X. Shao, X. Li, L. Gao, and Ch. Zhang, “Integration of Process Planning and Scheduling – A Modified Genetic Algorithm-Based Approach,” Computers & Operations Research, vol. 36, pp. 2082-2096, 2009.
[16] S. Kafashi, “Integrated Setup Planning and Operation Sequencing (ISOS) Using Genetic Algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 56, no. 5-8, pp. 589-600, 2011.
[17] G. Al Aqel, M. F. Ausaf, X. Li, and L. Gao, “A New Priority-Sort Based Optimization Algorithm for Integrated Process Planning and Scheduling,” International Journal of Modeling and Optimization, vol. 3, no. 2, pp. 226-231, 2013.
[18] M. Saidi, and S. Zarghami, “Exact Mixed Integer Programming for Integrated Scheduling and Process Planning in Flexible Environment,” Journal of Optimization in Industrial Engineering, vol. 7, no. 15, pp. 47-53, 2014.
[19] R. Barzanji, B. Naderi, and M. A. Begen, “Decomposition Algorithms for the Integrated Process Planning and Scheduling Problem,” Omega, vol. 93, pp. 102025, 2020.
[20] J. Yang, andW. Tang, “Preference-Based Adaptive Genetic Algorithm for Multiobjective Advanced Planning and Scheduling Problem,” IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1935-1940, 2009.
[21] L. Dayou, Y. Pu, and Y. Ji, “Development of a Multiobjective GA for Advanced Planning and Scheduling Problem,” The International Journal of Advanced Manufacturing Technology, vol. 42, no. 9-10, pp. 974-992, 2009.
[22] A. Konak, D. Coit, andA. Smith, “Multi-Objective Optimization Using Genetic Algorithms: A Tutorial,” Reliability Engineering & System Safety, vol. 91, no. 9, pp. 992-1007, 2006.
[23] H. Ishibuchi, and T. Murata, “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), vol. 28, no. 3, pp. 392-403, 1998.
[24] T. Murata, H. Ishibuchi, and H. Tanaka, “Multi-Objective Genetic Algorithm and its Applications to Flowshop Scheduling,” Computers & Industrial Engineering, vol. 30, no. 4, pp. 957-968, 1996.
[25] S. G. Ponnambalam, H. Jagannathan, M. Kataria, and A. Gadicherla, “A TSP-GA Multi-Objective Algorithm for Flow-Shop Scheduling,” The International Journal of Advanced Manufacturing Technology, vol. 23, no. 11-12, pp. 909-915, 2004.