A TLBO and a Jaya heuristics for permutation flow shop scheduling to minimize the sum of inventory holding and batch delay costs. (October 2018)

• Record Type: Journal Article
• Authors: Mishra, Aseem; Shrivastava, Divya
• Is Part Of: Computers & industrial engineering. Volume 124(2018)
• Page Start: 509
• Page End: 522
• Publication Date: 2018-10
• Subjects: Permutation flow shop scheduling problem -- Inventory holding costs -- Batch delay costs -- Teaching-learning based optimization algorithm -- Jaya algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.07.049
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Joint WIP inventory and tardiness cost model in flow shop problem is proposed. We optimize the objective function, with two new metaheuristics: TLBO and Jaya. Computational results of various algorithms are compared. Jaya algorithm outperforms all other meta-heuristics. Abstract: This paper deals with permutation flow shop scheduling problem in which an integrated cost model consisting of work-in-process inventory carrying cost and penalty cost due to batch delay is proposed. The objective is to obtain an optimum production schedule which minimizes the expected total cost per unit time of scheduling. To optimize the objective function, we apply two new metaheuristic optimization techniques namely TLBO (teaching-learning based optimization) and Jaya and two traditional algorithms: PSO (particle swarm optimization) and SA (simulated annealing). The problem is solved for several instances ranging from 8 jobs and 5 machines to 500 jobs and 20 machines. Computational results show that for small instances, all algorithms performed equally good when compared with the exact solution (total enumeration method). However, for medium and large size problems, enumeration method was unable to give the results in a reasonable computation time period. Therefore the results of all four algorithms are compared among themselves and found that Jaya outperforms all algorithms. However, for a few large instances, SA yields better results in less computation time as against other heuristics. The overall performance of all algorithms reveals that TLBO and Jaya have considerable potential to solve discrete combinatorial problems such as permutation flow-shop scheduling problems.