A two-machine no-wait flow shop problem with two competing agents

Azerine, Abdennour; Boudhar, Mourad; Rebaïne, Djamal

doi:10.1007/s10878-021-00755-9

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…The results are summarized in Table 4, which shows the average RPD. Overall, the proposed IG and SA outperformed by far TS from [26], except for only one case of (n = 50, h = 0.5, α = 0.9).…”

Section: Computational Experimentsmentioning

confidence: 92%

“…Additionally, to examine the performance of the proposed algorithms, IG and SA were compared with a tabu search (TS) based metaheuristic algorithm suggested in the latest study [26], which is the most similar one to our study. They considered a two-machine no-wait flow shop with two agents.…”

Section: Computational Experimentsmentioning

confidence: 98%

“…Jeong et al [25] presented a two-machine flowshop problem considering urgent jobs and developed metaheuristic algorithms and a branch and bound algorithm. Azerine et al [26] considered a two-machine no-wait flow shop problem with two competing agents and proposed a branch and bound algorithm for small size problems and tabu search metaheuristics for large sized problems. Now, a survey on flow shop problems with limited waiting times is provided.…”

Section: Previous Studiesmentioning

confidence: 99%

“…The TS was also coded in Java language and ran on the same computer for comparison. The same parameters used in [26] were set, but the TS used the same initial sequences that were used in IG and SA, because the flow shop considered in [26] is different from this study. These comparison tests were conducted on medium and large size problems.…”

Section: Computational Experimentsmentioning

confidence: 99%

See 3 more Smart Citations

Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

2021

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This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms.

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Section: Computational Experimentsmentioning

confidence: 92%

Section: Computational Experimentsmentioning

confidence: 98%

Section: Previous Studiesmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

2021

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“…Following this, the proposed improvement scatter search algorithm for fuzzy due dates is applied to solve a benchmark NWFSSP that includes setup times. Azerine et al 44 investigated the two-machine NWFSSP with two competing agents, aiming to minimize the overall completion time of one agent while adhering to an upper bound on the makespan of the second agent. Polynomial-time algorithms were demonstrated for certain restricted scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

Khurshid,

Maqsood,

Khurshid

et al. 2024

Sci Rep

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This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing algorithms helps different algorithms mitigate their weaknesses and leverage their respective strengths. The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. The selection process employs (µ + λ)-ES, allowing excellent parent solutions to survive multiple generations until a better offspring surpasses them. The IG algorithm’s straightforward search mechanism aids in further improving the solution and avoiding local minima. The destruction operator randomly removes d-jobs, which are then inserted one by one using a construction operator. The local search operator employs a single insertion approach, while the acceptance–rejection criteria are based on a constant temperature. Parameters of both ES and IG algorithms are calibrated using the Multifactor analysis of variance technique. The performance of the HES-IG algorithm is calibrated with other algorithms using the Wilcoxon signed test. The HES-IG algorithm is tested on 21 Nos. Reeves and 30 Nos. Taillard benchmark problems. The HES-IG algorithm has found 15 lower bound values for Reeves benchmark problems. Similarly, the HES-IG algorithm has found 30 lower bound values for the Taillard benchmark problems. Computational results indicate that the HES-IG algorithm outperforms other available techniques in the literature for all problem sizes.

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Algorithms for a two-machine no-wait flow shop scheduling problem with two competing agents

Yang,

Liu,

Huang

et al. 2024

J Comb Optim

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A two-machine no-wait flow shop problem with two competing agents

Cited by 8 publications

References 28 publications

Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

Algorithms for a two-machine no-wait flow shop scheduling problem with two competing agents

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