Decentralized Decision System for Closed-Loop Supply Chain: A Bi-Level Multi-Objective Risk-Based Robust Optimization Approach

This paper proposes a novel risk-based robust mixed-integer linear programming to design a decentralized closed-loop supply chain. The model is formulated as an uncertain bi-level multi-objective programming with multiple suppliers, manufacturers, and distributors, as the leader, and recovery, recycling, and disposal centers as the follower. A Scenario-based Conditional Value-at-Risk is employed to capture the demand uncertainty. The Karush-Kuhn-Tucker approach, epsilon-constraint, and LP-metric are leveraged to deal with the complexity of the bi-level coordination, and the multi-objectivity of the leader and follower. The performance of the model is compared with the performance of the deterministic decentralized model and the corresponding multi-objective model designed for the centralized system in both the robust and deterministic modes. Results indicate better performance of robust approaches compared to deterministic approaches. The decentralized approach provides better performance for the cost-sensitive decision-maker, especially the optimistic one, and those who are sensitive to social parameters prefer the centralized approach. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper introduces a novel risk-based robust mixed-integer linear programming model for designing decentralized closed-loop supply chains, which shows better performance compared to deterministic methods. The decentralized approach is more suitable for cost-sensitive decision-makers, while the centralized approach is preferred by those sensitive to social parameters.