IMPROVED TEACHING– LEARNING-BASED AND JAYA OPTIMIZATIONALGORITHMS FOR SOLVING FLEXIBLE FLOW SHOP SCHEDULING PROBLEMS

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BUDDALA RAVITEJA; MAHAPATRA SIBA SANKAR

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Journal: JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL Year:2018 | Volume:14 | Issue:3 Page(s): 555-570

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Title

IMPROVED TEACHING– LEARNING-BASED AND JAYA OPTIMIZATIONALGORITHMS FOR SOLVING FLEXIBLE FLOW SHOP SCHEDULING PROBLEMS

Author(s)

BUDDALA RAVITEJA | MAHAPATRA SIBA SANKAR | Issue Writer Certificate

Keywords

FLEXIBLE FLOW SHOP

JAYA ALGORITHM

MAKESPAN

META-HEURISTICS

TEACHING–

LEARNING-BASEDOPTIMIZATION

Abstract

Flexible flow shop (or a hybrid flow shop)scheduling problem is an extension of classical flow shopscheduling problem. In a simple flow shop configuration, ajob having ‘ g’ operations is performed on ‘ g’ operationcentres (stages) with each stage having only one machine. If any stage contains more than one machine for providingalternate processing facility, then the problem becomes aflexible flow shop problem (FFSP). FFSP which containsall the complexities involved in a simple flow shop andparallel machine scheduling problems is a well-known NPhard(Non-deterministic polynomial time) problem. Owingto high computational complexity involved in solving theseproblems, it is not always possible to obtain an optimalsolution in a reasonable computation time. To obtain nearoptimalsolutions in a reasonable computation time, a largevariety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solvingFFSP is rather tricky and time consuming. To address thislimitation, TEACHING–; learning-based optimization (TLBO)and JAYA algorithm are chosen for the study because theseare not only recent meta-heuristics but they do not requiretuning of algorithm-specific parameters. Although thesealgorithms seem to be elegant, they lose solution diversityafter few iterations and get trapped at the local optima. Toalleviate such drawback, a new local search procedure isproposed in this paper to improve the solution quality. Further, mutation strategy (inspired from geneticalgorithm) is incorporated in the basic algorithm to maintainsolution diversity in the population. Computationalexperiments have been conducted on standard benchmarkproblems to calculate makespan and computational time. Itis found that the rate of convergence of TLBO is superiorto JAYA. From the results, it is found that TLBO andJAYA outperform many algorithms reported in the literatureand can be treated as efficient methods for solving theFFSP.

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APA: Copy

BUDDALA, RAVITEJA, & MAHAPATRA, SIBA SANKAR. (2018). IMPROVED TEACHING– LEARNING-BASED AND JAYA OPTIMIZATIONALGORITHMS FOR SOLVING FLEXIBLE FLOW SHOP SCHEDULING PROBLEMS. JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL, 14(3), 555-570. SID. https://sid.ir/paper/310145/en

Vancouver: Copy

BUDDALA RAVITEJA, MAHAPATRA SIBA SANKAR. IMPROVED TEACHING– LEARNING-BASED AND JAYA OPTIMIZATIONALGORITHMS FOR SOLVING FLEXIBLE FLOW SHOP SCHEDULING PROBLEMS. JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL[Internet]. 2018;14(3):555-570. Available from: https://sid.ir/paper/310145/en

IEEE: Copy

RAVITEJA BUDDALA, and SIBA SANKAR MAHAPATRA, “IMPROVED TEACHING– LEARNING-BASED AND JAYA OPTIMIZATIONALGORITHMS FOR SOLVING FLEXIBLE FLOW SHOP SCHEDULING PROBLEMS,” JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL, vol. 14, no. 3, pp. 555–570, 2018, [Online]. Available: https://sid.ir/paper/310145/en

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