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Jing XL, Pan QK, Gao L. Local search-based metaheuristics for the robust distributed permutation flowshop problem. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107247] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Two-Machine Job-Shop Scheduling Problem to Minimize the Makespan with Uncertain Job Durations. ALGORITHMS 2019. [DOI: 10.3390/a13010004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We study two-machine shop-scheduling problems provided that lower and upper bounds on durations of n jobs are given before scheduling. An exact value of the job duration remains unknown until completing the job. The objective is to minimize the makespan (schedule length). We address the issue of how to best execute a schedule if the job duration may take any real value from the given segment. Scheduling decisions may consist of two phases: an off-line phase and an on-line phase. Using information on the lower and upper bounds for each job duration available at the off-line phase, a scheduler can determine a minimal dominant set of schedules (DS) based on sufficient conditions for schedule domination. The DS optimally covers all possible realizations (scenarios) of the uncertain job durations in the sense that, for each possible scenario, there exists at least one schedule in the DS which is optimal. The DS enables a scheduler to quickly make an on-line scheduling decision whenever additional information on completing jobs is available. A scheduler can choose a schedule which is optimal for the most possible scenarios. We developed algorithms for testing a set of conditions for a schedule dominance. These algorithms are polynomial in the number of jobs. Their time complexity does not exceed O ( n 2 ) . Computational experiments have shown the effectiveness of the developed algorithms. If there were no more than 600 jobs, then all 1000 instances in each tested series were solved in one second at most. An instance with 10,000 jobs was solved in 0.4 s on average. The most instances from nine tested classes were optimally solved. If the maximum relative error of the job duration was not greater than 20 % , then more than 80 % of the tested instances were optimally solved. If the maximum relative error was equal to 50 % , then 45 % of the tested instances from the nine classes were optimally solved.
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Palacín C, Pitarch J, Jasch C, Méndez C, de Prada C. Robust integrated production-maintenance scheduling for an evaporation network. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2017.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Wang B, Wang X, Lan F, Pan Q. A hybrid local-search algorithm for robust job-shop scheduling under scenarios. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.10.020] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Geyik F, Elibal K. A linguistic approach to non-identical parallel processor scheduling with fuzzy processing times. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.12.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Shao W, Pi D, Shao Z. A hybrid discrete optimization algorithm based on teaching–probabilistic learning mechanism for no-wait flow shop scheduling. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.06.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Sotskov Y, Egorova N, Lai TC. Minimizing total weighted flow time of a set of jobs with interval processing times. ACTA ACUST UNITED AC 2009. [DOI: 10.1016/j.mcm.2009.03.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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WANG B, HE S. Robust Optimization Model and Algorithm for Logistics Center Location and Allocation under Uncertain Environment. ACTA ACUST UNITED AC 2009. [DOI: 10.1016/s1570-6672(08)60056-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Matsveichuk N, Sotskov Y, Egorova N, Lai TC. Schedule execution for two-machine flow-shop with interval processing times. ACTA ACUST UNITED AC 2009. [DOI: 10.1016/j.mcm.2008.02.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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