An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

Ding, Jian-Ya; Song, Shiji; Gupta, Jatinder N. D.; Zhang, Rui; Chiong, Raymond; Wu, Cheng

doi:10.1016/j.asoc.2015.02.006

Cited by 123 publications

(79 citation statements)

References 43 publications

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“…The performance evaluation of the proposed algorithms is tested against the NEH under no-wait and the MNEH proposed by Ding et al (2015) on the Taillard's instances. The instances of Taillard (1993) 20×10 50×20 1 2044 2090 2061 1 6129 6321 6287 2 2166 2256 2184 2 5725 5953 5873 3 1940 2019 1980 3 5862 6236 6204 4 1811 1879 1868 4 5788 6052 6041 5 1933 2038 1985 5 5886 6257 6087 6 1892 1951 1941 6 5863 6179 6161 7 1963 2035 2035 7 5962 6211 6124 8 2057 2218 2204 8 5926 6294 6167 9 1973 2103 2010 9 5876 6169 6113 10 2051 2139 2139 10 5958 6249 6160 Average 1983 2072.8 2040.7 Average 5897.5 6192.1 6121.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Makespan values on Taillard's instances For the values of NEH under no-wait, MNEH heuristic algorithm and the best known solutions can be referred to Ding et al (2015) for the Taillard's instances. Out of the 120 Taillard's problem instances the proposed heuristic improves the results of NEH, MNEH and the PCH by 27.85%, MNEH by 22.56% and that of the proposed constructive heuristic algorithm by 24.68% by applying simple heuristic technique with little more computational efforts used by the phase II of the algorithm.…”

Section: Tablementioning

confidence: 99%

“…Chaudhry and Munem Khan (2012) presented a spreadsheet based genetic algorithm (GA) approach to minimize makespan under no-wait situation for scheduling n-jobs through m-machines. Some other important metaheuristics including Pan et al (2008a), Tseng and Lin (2010) and Ding et al (2015). Ding et al (2015) recently proposed constructive modified NEH (MNEH) as initial solution for metaheuristics with better performance than MNEH.…”

Section: Introductionmentioning

confidence: 99%

“…Some other important metaheuristics including Pan et al (2008a), Tseng and Lin (2010) and Ding et al (2015). Ding et al (2015) recently proposed constructive modified NEH (MNEH) as initial solution for metaheuristics with better performance than MNEH. Riyanto and Santosa (2015) proposed solution to no-wait flow shop scheduling by hybridization of ant colony optimization (ACO) algorithm with local search (LS).…”

Section: Introductionmentioning

confidence: 99%

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Heuristics for no-wait flow shop scheduling problem

Nailwal¹,

Gupta²,

Jeet³

2016

10.5267/j.ijiec

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No-wait flow shop scheduling refers to continuous flow of jobs through different machines. The job once started should have the continuous processing through the machines without wait. This situation occurs when there is a lack of an intermediate storage between the processing of jobs on two consecutive machines. The problem of no-wait with the objective of minimizing makespan in flow shop scheduling is NP-hard; therefore the heuristic algorithms are the key to solve the problem with optimal solution or to approach nearer to optimal solution in simple manner. The paper describes two heuristics, one constructive and an improvement heuristic algorithm obtained by modifying the constructive one for sequencing n-jobs through m-machines in a flow shop under no-wait constraint with the objective of minimizing makespan. The efficiency of the proposed heuristic algorithms is tested on 120 Taillard's benchmark problems found in the literature against the NEH under no-wait and the MNEH heuristic for no-wait flow shop problem. The improvement heuristic outperforms all heuristics on the Taillard's instances by improving the results of NEH by 27.85%, MNEH by 22.56% and that of the proposed constructive heuristic algorithm by 24.68%. To explain the computational process of the proposed algorithm, numerical illustrations are also given in the paper. Statistical tests of significance are done in order to draw the conclusions.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Tablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heuristics for no-wait flow shop scheduling problem

Nailwal¹,

Gupta²,

Jeet³

2016

10.5267/j.ijiec

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“…Like the GCP, the ECP is NP-hard [12] and thus computationally challenging. In addition to its theoretical significance, the ECP has a variety of applications, like garbage collection [28], partitioning and load balancing [4], scheduling [8,18,26], etc. For a review of possible applications of the ECP, the reader is referred to recent papers like [2,24].…”

Section: Introductionmentioning

confidence: 99%

Backtracking based iterated tabu search for equitable coloring

Lai

Hao

Glover

2015

Engineering Applications of Artificial Intelligence

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An equitable k-coloring of an undirected graph G = (V, E) is a partition of its vertices into k disjoint independent sets, such that the cardinalities of any two independent sets differ by at most one. As a variant of the graph coloring problem (GCP), the equitable coloring problem (ECP) concerns finding a minimum k for which an equitable k-coloring exists. In this work, we propose a backtracking based iterated tabu search (BITS) algorithm for solving the ECP approximately. BITS uses a backtracking scheme to define different k-ECP instances, an iterated tabu search approach to solve each particular k-ECP instance for a fixed k, and a binary search approach to find a suitable initial value of k. We assess the algorithm's performance on a set of commonly used benchmarks. Computational results show that BITS is very competitive in terms of solution quality and computing efficiency compared to the state-of-the-art algorithm in the literature. Specifically, BITS obtains new upper bounds for 21 benchmark instances, while matching the previous best upper bound for the remaining instances. Finally, to better understand the proposed algorithm, we study how its key ingredients impact its performance.

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A No-Wait Flow Shop Scheduling Problem with Setup Time in Fuzzy Environment

Başar

Engin

2021

Lecture Notes in Networks and Systems

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An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

Cited by 123 publications

References 43 publications

Heuristics for no-wait flow shop scheduling problem

Heuristics for no-wait flow shop scheduling problem

Backtracking based iterated tabu search for equitable coloring

A No-Wait Flow Shop Scheduling Problem with Setup Time in Fuzzy Environment

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