An Iterated Greedy Heuristic for Mixed No-Wait Flowshop Problems

Wang, Yamin; Li, Xiaoping; Ruíz, Rubén; Sui, Shaochun

doi:10.1109/tcyb.2017.2707067

Cited by 36 publications

(12 citation statements)

References 49 publications

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“…Besides, [69] simultaneously considered precedence constraints and setup times. The mixed-no-wait situation was examined by [70]. References [71,72] considered a makespan constraint while minimizing total completion time.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

et al. 2020

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Production management of perishable goods is highly complex and requires well-informed decisions in corresponding stages. In such production environments, scheduling problems with time constraints are of high relevance to ensure the timely flow of the work-in-process material and goods. This study introduces the no-wait flowshop scheduling problem with release times (NWFSP-RT) to help advance decision support systems in the food production industry. For this purpose, an original mixed-integer linear programming (MILP) formulation is proposed for minimizing the makespan. Since Beam Search (BS) algorithm has been successfully applied to solve various scheduling problems, a BS algorithm, and an improved variant, the local search-based Beam Search (BSLS) algorithms are developed to solve the NWFSP-RT problem. Extensive numerical analysis is conducted to analyze the performance of the algorithms in solving this highly intractable extension of the scheduling problems. We showed that BSLS effectively avoids early convergence and local optimality while dismissing non-promising search directions within a partial enumeration solution approach. The statistical analysis confirmed that the improved BS algorithm performs better in terms of solution quality. Applications of the developed heuristic are worthwhile research topics to pursue in solving other complex optimization problems. INDEX TERMS Flowshop scheduling, no-wait, release time, Beam Search.

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Section: Literature Reviewmentioning

confidence: 99%

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

et al. 2020

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“…, where is a nonincreasing function describing the learning effect (similarly for and ) or job based nonincreasing learning curve, = ( ) or = − min{ − 1, }. Readers may refer to Rudek [66][67][68] and Wang et al [60,61] for further details.…”

Section: Conclusion and Future Studiesmentioning

confidence: 99%

“…The proposed LS1, LS2, LS3, and LS4 are very fast; even their complexities are not easily found. As for other neighborhood structures and local search methods, one can refer to Li et al[59] and Wang et al[60,61], among others.…”

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confidence: 99%

Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

Lin

2018

Discrete Dynamics in Nature and Society

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Two-stage production process and its applications appear in many production environments. Job processing times are usually assumed to be constant throughout the process. In fact, the learning effect accrued from repetitive work experiences, which leads to the reduction of actual job processing times, indeed exists in many production environments. However, the issue of learning effect is rarely addressed in solving a two-stage assembly scheduling problem. Motivated by this observation, the author studies a two-stage three-machine assembly flow shop problem with a learning effect based on sum of the processing times of already processed jobs to minimize the makespan criterion. Because this problem is proved to be NP-hard, a branch-and-bound method embedded with some developed dominance propositions and a lower bound is employed to search for optimal solutions. A cloud theory-based simulated annealing (CSA) algorithm and an iterated greedy (IG) algorithm with four different local search methods are used to find near-optimal solutions for small and large number of jobs. The performances of adopted algorithms are subsequently compared through computational experiments and nonparametric statistical analyses, including the Kruskal–Wallis test and a multiple comparison procedure.

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“…Shao et al 21) presented a Pareto-based estimation of distribution algorithm to solve a distributed problem with sequencedependent setup times so as to minimize the objective of makespan and total weighted tardiness. Considering both wait and no-wait constraints, Wang et al 22) and Cheng et al 23) presented iterative greedy algorithms to minimize makespan.…”

Section: Introductionmentioning

confidence: 99%

A Novel Genetic Simulated Annealing Algorithm for No-wait Hybrid Flowshop Problem with Unrelated Parallel Machines

Xuan

Zheng

et al. 2021

ISIJ Int.

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This paper studies the problem of scheduling N jobs in a hybrid flowshop with unrelated parallel machines at each stage. Considering the practical application of the presented problem, no-wait constraints and the objective function of total flowtime are included in the scheduling problem. A mathematical model is constructed and a novel genetic simulated annealing algorithm so-called GSAA are developed to solve this problem. In the algorithm, firstly a modified NEH algorithm is proposed to obtain the initial population. A two-dimensional matrix encoding scheme for scheduling solutions is designed and an insertion-translation approach are employed for decoding in order to meet no-wait constraints. Afterwards, to avoid GA premature and enhance search ability, an adaptive adjustment strategy is imposed on the crossover and mutation operators. In addition, a SA procedure is implemented for some better individuals from the GA solutions to complete re-optimization, where five neighborhood search structures are constructed including job based exchange, gene based exchange, gene based insertion, job based mutation, and gene based mutation. Finally, various simulation experiments in two scales of small-medium and large are established. Computational results show that the presented algorithm performs much more effectively compared with several heuristic algorithms reported in the literature. KEY WORDS: hybrid flowshop; unrelated parallel machines; no-wait constraints; total flowtime; genetic simulated annealing algorithm.

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An Iterated Greedy Heuristic for Mixed No-Wait Flowshop Problems

Cited by 36 publications

References 49 publications

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

A Novel Genetic Simulated Annealing Algorithm for No-wait Hybrid Flowshop Problem with Unrelated Parallel Machines

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