Skip to main content
Kent Academic Repository

Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach

Hamdan, s., Cheaitou, Ali (2017) Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach. Computers & Operations Research, 81 . pp. 282-304. ISSN 0305-0548. (doi:10.1016/j.cor.2016.11.005) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91468)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://dx.doi.org/10.1016/j.cor.2016.11.005

Abstract

This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem. The tool contains three integrated components. First, fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) is used to assign two preference weights to each potential supplier according to two sets of criteria taken separately: traditional and green. Second, top management uses an analytic hierarchy process (AHP) to assign a global importance weight to each of the two sets of criteria based on the strategy of the company and independently of the potential suppliers. Third, for each supplier, the preference weight obtained from fuzzy TOPSIS regarding the traditional criteria is then multiplied by the global importance weight of the set of traditional criteria. The same is done for the green criteria. The two combined preference weights obtained for each supplier are then used in addition to total cost to select the best suppliers and to allocate orders using multi-period bi-objective and multi-objective optimization. The mathematical models are solved using the weighted comprehensive criterion method and the branch-and-cut algorithm. The approach of this research has a major advantage: it provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of the number of criteria on the preference weight of the suppliers. Contrary to the case in which each supplier is evaluated on the basis of all criteria at the same time, the proposed approach would not necessarily result in a supplier with poor green performance being ranked among the best for a situation in which the number of green criteria is smaller than the number of traditional criteria. In this case, the final ranking would mainly depend on the global weight of the green criteria set given by the top management using AHP as well as on the ranking of the supplier in terms of green criteria obtained from fuzzy TOPSIS. Extensive numerical experiments are conducted in which the bi-objective and multi-objective models are compared and the effect of the separation between green and traditional criteria is investigated. The results show that the two optimization approaches provide very close solutions, which leads to a preference for the bi-objective approach because of its lower computation time. Moreover, the results confirm the flexibility of the proposed approach and show that combining all criteria in one set is a special case. Finally, a time study is performed, which shows that the bi-objective integer linear programming model has a polynomial computation time.

Item Type: Article
DOI/Identification number: 10.1016/j.cor.2016.11.005
Uncontrolled keywords: Supplier selection, Planning, Order allocation, Green criteria, Multi-criteria decision making, Optimization
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Sadeque Hamdan
Date Deposited: 10 Nov 2021 11:47 UTC
Last Modified: 04 Mar 2024 17:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91468 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.