A Collection‐Distribution Center Location and Allocation Optimization Model in Closed‐Loop Supply Chain for Chinese Beer Industry

Abstract

Recycling waste products is an environmental-friendly activity that can result in manufacturing cost saving and economic efficiency improving. In the beer industry, recycling bottles can reduce manufacturing cost and the industry’s carbon footprint. This paper presents a model for a collection-distribution center location and allocation problem in a closed-loop supply chain for the beer industry under a fuzzy random environment, in which the objectives are to minimize total costs and transportation pollution. Both random and fuzzy uncertainties, for which return rate and disposal rate are considered fuzzy random variables, are jointly handled in this paper to ensure a more practical problem solution. 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 beer company case study is given to illustrate the application of the proposed model and to demonstrate the priority-based global-local-neighbor particle swarm optimization.

Transportation Risk Control of Waste Disposal in the Healthcare System with Two-Echelon Waste Collection Network

“…Heuristic algorithms are usually used to solve VRP and obtain near-optimal solutions in a reasonable time. Global search algorithms like genetic algorithm and PSO are applicable for finding the global optimum [24,25]. For local search heuristics, some metaheuristics are applied to accelerate convergence while avoid trapping in local optimum, such as Squeaky Wheel Optimization [26,27], Critical-Shaking Neighbourhood Search [28,29], and column generationbased heuristic [30].…”

Evaluating Supply Chain Network Designs: An Approach Based on SNA Metrics and Random Forest Feature Selection

“…Several studies have focused on developing mathematical models for small and medium-sized CLSC Networks with integrated or separate forward-reverse logistics (de Rosa et al, 2013;El-Sayed et al, 2010;Ghadge et al, 2016;Kang et al, 2017;Lee et al, 2013;Pishvaee et al, 2009;Ponce-Cueto & Molenat, 2015;Yavari & Zaker, 2019). In the real world, supply chains are subject to disruptions caused by equipment breakdown, human error, and natural disasters (Jabbarzadeh et al, 2012).…”

Literature review: Strategic network optimization models in waste reverse supply chains

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