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Validi, Sahar, Bhattacharya, Arijit 
ORCID: https://orcid.org/0000-0001-5698-297X and Byrne, P.J.
(2020)
Sustainable distribution system design: a two-phase DoE-guided meta-heuristic solution approach for a three-echelon bi-objective AHP-integrated location-routing model.
Annals of Operations Research, 290 (1-2).
191–222.
ISSN 0254-5330
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Abstract
This article introduces a sustainable integrated bi-objective location-routing model, its two-phase solution approach and an analysis procedure for the distribution side of three-echelon logistics networks. The mixed-integer programming model captures several real-world factors by introducing an additional objective function and a set of new constraints in the model that outbound logistics channels find difficult to reconcile. The sustainable model minimises CO2 emissions from transportation and total costs incurred in facilities and the transportation channels. Design of Experiment (DoE) is integrated to the meta-heuristic based optimiser to solve the model in two phases. The DoE-guided solution approach enables the optimiser to offer the best stable solution space by taking out solutions with poor design features from the space and refining the feasible solutions using a convergence algorithm thereby selecting the realistic results. Several alternative solution scenarios are obtained by prioritising and ranking the realistic solution sets through a multi-attribute decision analysis tool, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The robust model provides the decision maker the ability to take decisions on sustainable open alternative optimal routes. The outcomes of this research provide theoretical and methodological contributions, in terms of integrated bi-objective location-routing model and its two-phase DoE-guided meta-heuristic solution approach, for the distribution side of three-echelon logistics networks.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | sustainable logistics network,location-routing ,bi-objective programming,mixed-integer programming,multi-attribute decision analysis,doe-guided meta-heuristic solution |
| Faculty \ School: | Faculty of Social Sciences > Norwich Business School |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 21 May 2018 15:30 |
| Last Modified: | 21 Oct 2022 18:39 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/67148 |
| DOI: | 10.1007/s10479-018-2887-y |
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