Designing a reliable-sustainable supply chain network

adaptive m-objective ε-constraint method

Journal Article (2024)
Author(s)

A. Sepehri (TU Delft - Rivers, Ports, Waterways and Dredging Engineering)

Erfan Babaee Tirkolaee (Istinye University, Middle East University, Yuan Ze University)

Vladimir Simic (Korea University, University of Belgrade)

Sadia Samar Ali (King Abdulaziz University)

Research Group
Rivers, Ports, Waterways and Dredging Engineering
DOI related publication
https://doi.org/10.1007/s10479-024-05961-2
More Info
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Publication Year
2024
Language
English
Related content
Research Group
Rivers, Ports, Waterways and Dredging Engineering
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Abstract

In the current era emphasizing sustainability and circularity, supply chain network design is a critical challenge for making reliable decisions. The optimization of facility location-allocation inventory problems (FLAIPs) holds the key to achieving dependable product delivery with reduced costs and carbon emissions. Despite the importance of these challenges, a substantial research gap exists regarding economic, reliability, and sustainability criteria for FLAIPs. This paper aims to fill this gap by introducing a multi-objective mixed-integer linear programming model, focusing on configuring a reliable sustainable supply chain network. The model addresses three key objectives: minimizing costs, minimizing emissions, and maximizing reliability. A notable contribution of this research lies in elaborating on five levels of a supply chain network catering to the delivery of multiple products across various periods. Another novelty is the simultaneous incorporation of economic, environmental, and reliability objectives in the network design—a facet rarely addressed in prior research. Results highlight that varying demand levels for each facility lead to altered trade-offs between objectives, empowering practitioners to make diverse decisions in facility location allocation. The proposed mathematical model undergoes validation through numerical examples and sensitivity analysis of parameters. The paper concludes by presenting theoretical and managerial implications, contributing valuable insights to the field of sustainable supply chains.