Preprint Article Version 1 NOT YET PEER-REVIEWED

A Multi-Objective Collection-Distribution Center Location and Allocation Problem in a Closed-Loop Supply Chain for the Chinese Beer Industry

  1. School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
Version 1 : Received: 11 October 2016 / Approved: 11 October 2016 / Online: 11 October 2016 (11:02:47 CEST)

How to cite: Kai, K.; Wang, X.; Ma, Y. A Multi-Objective Collection-Distribution Center Location and Allocation Problem in a Closed-Loop Supply Chain for the Chinese Beer Industry. Preprints 2016, 2016100037 (doi: 10.20944/preprints201610.0037.v1). Kai, K.; Wang, X.; Ma, Y. A Multi-Objective Collection-Distribution Center Location and Allocation Problem in a Closed-Loop Supply Chain for the Chinese Beer Industry. Preprints 2016, 2016100037 (doi: 10.20944/preprints201610.0037.v1).

Abstract

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

Subject Areas

collection-distribution center; closed loop supply chain; fuzzy random variable; particle swarm optimization

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