A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times

Chia-Lun Hsu, Win-Chin Lin, Lini Duan, Jan-Ray Liao, Chin-Chia Wu and Juin-Han Chen

Discrete Dynamics in Nature and Society, 2020, vol. 2020, 1-16

Abstract:

In many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, machine breakdowns, tool quality variations and unavailability, and so on. In light of the phenomenon of scenario-dependent processing times existing in many applications, this paper proposes to incorporate scenario-dependent processing times into a two-machine flow-shop environment with the objective of minimizing the total completion time. The problem under consideration is never explored. To solve it, we first derive a lower bound and two optimality properties to enhance the searching efficiency of a branch-and-bound method. Then, we propose 12 simple heuristics and their corresponding counterparts improved by a pairwise interchange method. Furthermore, we set proposed 12 simple heuristics as the 12 initial seeds to design 12 variants of a cloud theory-based simulated annealing (CSA) algorithm. Finally, we conduct simulations and report the performances of the proposed branch-and-bound method, the 12 heuristics, and the 12 variants of CSA algorithm.

Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:3530701

DOI: 10.1155/2020/3530701

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