Exact and metaheuristic approaches for identical parallel machine scheduling with a common server and sequence-dependent setup times

Silva, João Marcos Pereira; Teixeira, Ewerton; Subramanian, Anand

doi:10.1080/01605682.2019.1671153

Cited by 8 publications

(1 citation statement)

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“…To the best of our knowledge, there are no publicly available benchmark instances from the literature regarding the problem P, S2|s j , t j |C max with a regular job set, so we decided to generate a new set of instances. This set was created by generalizing the scheme previously proposed by Silva et al [34] and Benmansour and Sifaleras [8]. Indeed, to generate a regular job set, first we generate a general job set, where the processing time p j , loading time s j , and unloading time t j of each job j were generated from the uniform distributions U [10,100], U [5,25] and U [5,25],…”

Section: Benchmark Instancesmentioning

confidence: 99%

General variable neighborhood search for the parallel machine scheduling problem with two common servers

2022

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We address in this paper the parallel machine scheduling problem with a shared loading server and a shared unloading server. Each job has to be loaded by the loading server before being processed on one of the available machines and unloaded immediately by the unloading server after its processing. The objective function involves the minimization of the overall completion time, known as the makespan. This important problem raises in flexible manufacturing systems, automated material handling, healthcare, and many other industrial fields, and has been little studied up to now. To date, research on it has focused on the case of two machines. The regular case of this problem is considered. A mixed integer programming formulation based on completion time variables is suggested to solve small-sized instances of the problem. Due to its $$\mathcal{NP}\mathcal{}$$ NP -hardness, we propose two greedy heuristics based on the minimization of the loading, respectively unloading, server waiting time, and an efficient General Variable Neighborhood Search (GVNS) algorithm. In the computational experiments, the proposed methods are compared using 120 new and publicly available instances. It turns out that, the proposed GVNS with an initial solution-finding mechanism based on the unloading server waiting time minimization significantly outperforms the other approaches.

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