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

Abstract

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.

KEYWORDS:

• Seru system; flexibility
• seru system formation; stochastic programming
• Gurobi solver

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated during and/or analysed during the current study are available in the github repository, https://github.com/Luckydragon96/FlexibleSERUSystem.git

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant numbers 71831003, 71420107028].

Notes on contributors

Yuhong Ren

Yuhong Ren is a Ph.D. candidate in Management Science and Engineering from Dongbei University of Finance and Economics, Dalian, China. She received her Master degree in Industrial Engineering from Qingdao University, China, in 2013. Her research focuses on Seru production.

Jiafu Tang

Jiafu Tang received his Ph.D. degree from Northeastern University in 1999. He is currently Cheung Kong Scholarship Chair Professor of MOE and Xinghai Chair Professor of Dongbei University of Finance and Economics (DUFE). He acted as Dean of Management Science & Engineering, DUFE in the periods of 2013–2022. Since 2002, he has awarded several honours, includes ‘The Award of Program of the National Innovative Research Teamwork of National Natural Science Foundation of China’, ‘The Awardee of the NSFC for Distinguished Young Scholars’, ‘The New Century Excellent Talents in University of MOE of China’ and ‘Excellent Younger Teachers of MOE’. His interests include fuzzy modelling and intelligent optimisation for complex industrial systems, Operations Management and Logistics Optimisation, Product Engineering and Quality optimisation for NPD. He authored more than 80 papers in peer-reviewed international journals.

Yang Yu

Yang Yu received the Ph.D. degree in Management Science and Engineering from the Dalian University of Technology, Dalian, in 2009. He is currently a professor of operational management with the Dalian University of Technology, China. His research interests include operational management, green logistics, and exact algorithms.

Xiaolong Li

Xiaolong Li is currently a Ph.D. student at the Department of Intelligent data and Systems Engineering, Northeastern University, Shenyang, China. His research interests include Seru production, parallel machine scheduling problems, multi-objective evolutionary algorithms and exact methods.