Parallel flowshop scheduling using Tabu search

Cao, Dongxing; Chen, Ming‐Yuan

doi:10.1080/0020754031000106443

Cited by 25 publications

(7 citation statements)

References 17 publications

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“…They proposed a O(nP 3 )-time dynamic programming algorithm for optimally solving 2 flow shops in parallel with two machines. (Cao & Chen, 2010) developed a mathematical model and a Tabu Search algorithm for the PFSP with two machines. Al-Salem (2004) proposed a polynomial-time algorithm to minimize the makespan in two-machine parallel flow shops with proportional processing time.…”

Section: Introductionmentioning

confidence: 99%

Efficient heuristics for the parallel blocking flow shop scheduling problem

Ribas

Companys

Tort‐Martorell

2017

Expert Systems with Applications

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We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them –especially the IGA– perform considerably better than those algorithms adapted from the DPFSP literature.Peer ReviewedPostprint (author's final draft

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Section: Introductionmentioning

confidence: 99%

Efficient heuristics for the parallel blocking flow shop scheduling problem

Ribas

Companys

Tort‐Martorell

2017

Expert Systems with Applications

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“…In recent years, parallel machine scheduling problems have been studied by many researchers [1][2][3][4][5]. In a general parallel machine scheduling problem, a set of independent jobs are to be scheduled on a number of parallel machines to meet certain job completion time related objectives [1].…”

Section: Introductionmentioning

confidence: 99%

Parallel machine selection and job scheduling to minimize machine cost and job tardiness

Cao

Chen

Wan³

2005

Computers & Operations Research

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“…Vairaktarakis and Elhafsi (2000) concluded empirically, on the basis of computational experiments with several heuristics for both problems, that the parallel flow shop entails only a minor loss in throughput performance in comparison with the hybrid flow shop; accordingly, it is an attractive alternative to the hybrid flow shop, with its complicated routings. Other heuristics for the parallel flow shop problem have been presented by Cao and Chen (2003) and Al-Salem (2004).…”

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confidence: 99%

Approximation algorithms for the parallel flow shop problem

Zhang

Velde

2012

European Journal of Operational Research

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We consider the N P-hard problem of scheduling n jobs in m two-stage parallel flow shops so as to minimize the makespan. This problem decomposes into two subproblems:assigning the jobs to parallel flow shops; and scheduling the jobs assigned to the same flow shop by use of Johnson's rule. For m = 2, we present a 3 2 -approximation algorithm, and for m = 3, we present a 12 7 -approximation algorithm. Both these algorithms run in O(n log n) time. These are the first approximation algorithms with fixed worst-case performance guarantees for the parallel flow shop problem.

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Parallel flowshop scheduling using Tabu search

Cited by 25 publications

References 17 publications

Efficient heuristics for the parallel blocking flow shop scheduling problem

Efficient heuristics for the parallel blocking flow shop scheduling problem

Parallel machine selection and job scheduling to minimize machine cost and job tardiness

Approximation algorithms for the parallel flow shop problem

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