A metaheuristic based on simulation for stochastic Job-shop optimization

Kemmoé-Tchomté, S.; Lamy, Damien; Tchernev, Nikolay

doi:10.1109/iesm.2015.7380144

Cited by 3 publications

(2 citation statements)

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“…Obviously, infinite sampling of the above problem model can make the objective values of equations (1) and (3) stable, but in fact it is practically impossible. In the actual calculation, the value of equations (1) or ( 3) is usually approximated by the average of finite sampling values (e.g., Monte Carlo simulation) [8][9][10][11][12][13][14][15][16][17][18]. us, E(T max (π, ξ)) can be estimated with…”

Section: Stochastic Rjsspmentioning

confidence: 99%

“…At present, some uncertain JSSPs have been studied. Most of them are the JSSP with stochastic processing times [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], and the rest are the JSSP with probabilistic machine availability [23], the JSSP with fuzzy processing times [24], and the JSSP with fuzzy machine availability [25]. In these studies, evolutionary algorithms are the main methodology, and makespan or tardiness/earliness is the main optimization criterion.…”

Section: Introductionmentioning

confidence: 99%

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Differential Evolution Algorithm Combined with Uncertainty Handling Techniques for Stochastic Reentrant Job Shop Scheduling Problem

Qian

Mao

et al. 2022

Complexity

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This paper considers two kinds of stochastic reentrant job shop scheduling problems (SRJSSP), i.e., the SRJSSP with the maximum tardiness criterion and the SRJSSP with the makespan criterion. Owing to the NP-complete complexity of the considered RJSSPs, an effective differential evolutionary algorithm (DEA) combined with two uncertainty handling techniques, namely, DEA_UHT, is proposed to address these problems. Firstly, to reasonably control the computation cost, the optimal computing budget allocation technique (OCBAT) is applied for allocating limited computation budgets to assure reliable evaluation and identification for excellent solutions or individuals, and the hypothesis test technique (HTT) is added to execute a statistical comparison to reduce some unnecessary repeated evaluation. Secondly, a reentrant-largest-order-value rule is designed to convert the DEA’s individual (i.e., a continuous vector) to the SRJSSP’s solution (i.e., an operation permutation). Thirdly, a conventional active decoding scheme for the job shop scheduling problem is extended to decode the solution for obtaining the criterion value. Fourthly, an Insert-based exploitation strategy and an Interchange-based exploration strategy are devised to enhance DEA’s exploitation ability and exploration ability, respectively. Finally, the test results and comparisons manifest the effectiveness and robustness of the proposed DEA_UHT.

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