Efficient solutions to the m-machine robust flow shop under budgeted uncertainty

Mario Levorato (), David Sotelo (), Rosa Figueiredo () and Yuri Frota ()
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Mario Levorato: Universidade Federal Fluminense
David Sotelo: Petrobras
Rosa Figueiredo: Avignon Université
Yuri Frota: Universidade Federal Fluminense

Annals of Operations Research, 2024, vol. 338, issue 1, No 27, 765-799

Abstract: Abstract This work presents two solution methods for the m-machine robust permutation flow shop problem with processing time uncertainty. The goal is to minimize the makespan of the worst-case scenario by utilizing an approach based on budgeted uncertainty, in which only a subset of operations will reach their worst-case processing time values. To obtain efficient solutions to this problem, we first extend an existing two-machine worst-case procedure, based on dynamic programming, generalizing it to m machines. The worst-case calculation is then incorporated into two proposed solution methods: an exact column-and-constraint generation algorithm and a GRASP metaheuristic. Based on experiments with four sets of literature-based instances, empirical results demonstrate the ability of the GRASP to efficiently produce an optimal or near-optimal solution in most cases.

Keywords: GRASP; Robust optimization; Permutation flow shop; Budget of uncertainty; Column-and-constraint generation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05661-3

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