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Lookup NU author(s): Dr Arijit DeORCiD, Professor Matthew GortonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Food Supply Chains (FSCs) have become increasingly complex with the average distance between producers and consumers rising considerably in the past two decades. Consequently, FSCs are a major source of carbon emissions and reducing transportation costs a major challenge for businesses. To address this, we present a mathematical model to promote the three core dimensions of sustainability (economic, environmental, and social), based on the Mixed-Integer Linear Programming (MILP) method. The model addresses the environmental dimension by intending to decrease the carbon emissions of different transport modes involved in the logistics network. Several supply chain network characteristics are incorporated and evaluated, with a consideration of social sustainability (job generation from operating various facilities). The mathematical model's robustness is demonstrated by testing and deploying it to a variety of problem instances. A real-life case study (Norwegian salmon supply chain) helps to comprehend the model's applicability. To understand the importance of optimizing food supply networks holistically, the paper investigates the impact of multiple supply chain permutations on total cost, demand fluctuations and carbon emissions. To address fluctuations in retail demand, we undertook sensitivity analysis for variations in demand, enabling the proposed model to revamp Norway's salmon supply chain network. Subsequently, the results are thoroughly examined to identify managerial implications.
Author(s): De A, Kalavagunta A, Gorton M, Goswami M
Publication type: Article
Publication status: Published
Journal: Journal of Environmental Management
Year: 2024
Volume: 366
Pages: 121914
Print publication date: 01/08/2024
Online publication date: 22/07/2024
Acceptance date: 15/07/2024
Date deposited: 26/07/2024
ISSN (electronic): 1095-8630
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.jenvman.2024.121914
DOI: 10.1016/j.jenvman.2024.121914
Data Access Statement: Data will be made available on request.
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