Issue |
RAIRO-Oper. Res.
Volume 58, Number 2, March-April 2024
|
|
---|---|---|
Page(s) | 1835 - 1869 | |
DOI | https://doi.org/10.1051/ro/2024049 | |
Published online | 16 April 2024 |
An optimisation model for sustainable multi-commodity transportation planning
1
Department of Operational Research, University of Delhi, New Delhi 110007, India
2
Queen’s Business School, Queen’s University Belfast, Belfast BT9 5EE, UK
* Corresponding author: c.vincent@qub.ac.uk
Received:
28
June
2022
Accepted:
21
February
2024
This paper aims to establish a supply chain model that significantly reduces economic and environmental costs. It comprises all activities related to procurement, production, and distribution planning. The proposed multi-objective multi-commodity optimisation model deals with the four conflicting objectives of reducing costs and emissions and choosing top-priority suppliers and the most efficient vehicles. We apply an integrated AHP (analytic hierarchy process) and TOPSIS (technique for order preference by similarity to an ideal solution) technique to determine the weights of suppliers, depending on three indices of criteria, alternatives, and raw material. This paper proposes a cross-efficiency evaluation method using data envelopment analysis (DEA) to ensure that the cross-evaluation of different types of vehicles for evaluating peers is as consistent as possible. The mutually contradictory objectives give rise to several Pareto-optimal solutions. The optimal compromise solutions are found using a lexicographic goal programming technique. We present a real-world case to demonstrate the effectiveness of the proposed methodology, followed by numerical comparisons and additional insights.
Mathematics Subject Classification: 90C29
Key words: Sustainable supply chain / multi-objective optimisation / multi-commodity transportation / mixed-integer decision problem / TOPSIS technique / DEA technique
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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