A multi-objective green supply chain network design with price-dependent demand
Abolfazl Kazemi,
Marzieh Karimi and
Mahsa Oroojeni Mohammad Javad
International Journal of Logistics Systems and Management, 2025, vol. 50, issue 2, 266-285
Abstract:
For many years, various businesses have used the supply chain management system to survive in a competitive market and have a more realistic vision for their business. Designing an optimal supply chain network determines supply chain efficiency and customer satisfaction. In this paper, a multi-objective model for designing a multi-product green supply chain network is proposed considering price-dependent demand conditions. The first objective function aims to maximum profit of the whole chain, including the difference between the revenue from the sale of products and the total operating cost, including fixed costs and variable costs. The second objective function minimises the supply chain's production and transportation pollution. For solving the NP-hard model, multi-objective algorithms based on Pareto, multi-objective simulated annealing algorithm (MOSA), and multi-objective variable neighbourhood search algorithm (MOVNS) is used. Finally, the statistical hypothesis test and the five standard indicators are applied to evaluate and compare algorithms.
Keywords: green supply chain network design; price-dependent demand; Pareto-based algorithms. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=144438 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijlsma:v:50:y:2025:i:2:p:266-285
Access Statistics for this article
More articles in International Journal of Logistics Systems and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().