A Genetic Algorithm to Minimize the Total Tardiness for M-Machine Permutation Flowshop Problems

Chia-Shin Chung (), James Flynn (), Walter Rom () and Piotr Staliński ()
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Chia-Shin Chung: Department of Operations and Supply Chain Management, Cleveland State Univesity
James Flynn: Department of Operations and Supply Chain Management, Cleveland State Univesity
Walter Rom: Department of Operations and Supply Chain Management, Cleveland State Univesity
Piotr Staliński: Department of Quantitative Methods in Management, Wyższa Szkoła Biznesu-National Louis University

Journal of Entrepreneurship, Management and Innovation, 2012, vol. 8, issue 2, 26-43

Abstract: The m-machine, n-job, permutation flowshop problem with the total tardiness objective is a common scheduling problem, known to be NP-hard. Branch and bound, the usual approach to finding an optimal solution, experiences difficulty when n exceeds 20. Here, we develop a genetic algorithm, GA, which can handle problems with larger n. We also undertake a numerical study comparing GA with an optimal branch and bound algorithm, and various heuristic algorithms including the well known NEH algorithm and a local search heuristic LH. Extensive computational experiments indicate that LH is an effective heuristic and GA can produce noticeable improvements over LH.

Keywords: genetic algorithm; scheduling; permutation flowshop; tardiness (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:aae:journl:v:8:y:2012:i:2:p:26-43

DOI: 10.7341/2012822

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