Title: Risk-averse joint facility location-inventory optimisation for green closed-loop supply chain network design under demand uncertainty

Authors: Ying Xu; Xiao Zhao; Pengcheng Dong; Guodong Yu

Addresses: College of Finance and Information, Ningbo University of Finance and Economics, No. 899, Xueyuan Road, 315175 Ningbo, Zhejiang, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430000, China ' School of Management, Shandong University, No. 27, Shanda South Road, 250100 Jinan, China ' School of Management, Shandong University, No. 27, Shanda South Road, 250100 Jinan, China

Abstract: This paper considers a joint facility location-inventory optimisation for green closed-loop supply chain network design under demand uncertainty. Under the uncoordinated inventory policy, we propose a chance-constrained risk-averse bi-objective 0-1 mixed-integer nonlinear stochastic programming to minimise the total expected cost and CO₂ emissions. To solve the model, we first present an equivalent reformulation with a single objective based on distributionally robust optimisation. Then, we provide a linear reformulation with some valid inequalities. We also provide a greedy heuristic decomposition searching rule to solve the large-scale problem. We finally present a numerical analysis to show the performance of our methods. Results illustrate that the risk-averse joint model can effectively improve service capability and reliability than independent and risk-neutral location and inventory problems. We also recommend that the incompletely uncoordinated strategy for the joint optimisation can be more cost-effective and generate fewer workloads. Besides, the proposed algorithm achieves a more desirable performance than CPLEX for large-scale problems. [Submitted: 10 December 2020; Accepted: 15 January 2022]

Keywords: green closed-loop supply chain; facility location; inventory; risk-averse; chance constraint; distributionally robust optimisation.

DOI: 10.1504/EJIE.2023.129444

European Journal of Industrial Engineering, 2023 Vol.17 No.2, pp.192 - 219

Accepted: 15 Jan 2022
Published online: 09 Mar 2023 *

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