A two-stage stochastic programming model and parallel Master-Slave adaptive GA for flexible Seru system formation

High flexibility is an important feature of seru system that has received less attention. In this paper, we discuss how to do such flexible seru system formation, especially focusing on the strategic decision phase. We formulate the flexible seru system formation problem (FSFP) as a nonlinear progra...

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Vydáno v:International journal of production research Ročník 62; číslo 4; s. 1144 - 1161
Hlavní autoři: Ren, Yuhong, Tang, Jiafu, Yu, Yang, Li, Xiaolong
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 16.02.2024
Taylor & Francis LLC
Témata:
Adaptive algorithms
Computing time
Flexibility
Genetic algorithms
Gurobi solver
Nonlinear programming
Performance evaluation
seru system formation; stochastic programming
Seru system; flexibility
Stochastic models
Stochastic programming
ISSN:0020-7543, 1366-588X
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Shrnutí:High flexibility is an important feature of seru system that has received less attention. In this paper, we discuss how to do such flexible seru system formation, especially focusing on the strategic decision phase. We formulate the flexible seru system formation problem (FSFP) as a nonlinear programming model to evaluate flexibility performance in terms of flexibility-investment cost and flexibility-loss cost. To exactly obtain the optimal solution of the FSFP, we transform the nonlinear model into a linear one and solve it with Gurobi solver. For the large-scale problem, we proposed a parallel Master-Slave adaptive genetic algorithm (PMSA-GA) by transforming it into a two-stage stochastic programming model. The adaptive selection is used to improve the quality of solutions in PMSA-GA. To reduce the computational time, multiple populations of seru formation evolve in parallel with the assistance of the Master-Slave mechanism. Extensive experiments are tested to evaluate the performance of the proposed model and algorithm, and the effect of cost parameters on the system performance is discussed. The results show that the FSFP model takes the property of dynamic demand into account and is more suitable for dynamic demand environments than the task-oriented seru formation (TOSF) strategy from the previous literature.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2023.2177087