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

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Kai Kang ◽  
Xiaoyu Wang ◽  
Yanfang Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Closed Loop ◽  
Particle Swarm ◽  
Distribution Center ◽  
Manufacturing Cost ◽  
Swarm Optimization ◽  
Beer Industry ◽  
Neighbor Particle ◽  
Fuzzy Random

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.

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

2016 ◽  
Author(s):  
Kang Kai ◽  
Xiaoyu Wang ◽  
Yanfang Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Closed Loop ◽  
Distribution Center ◽  
Allocation Problem ◽  
Swarm Optimization ◽  
Manufacturing Costs ◽  
Multi Objective ◽  
Beer Industry ◽  
Neighbor Particle

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.

scholarly journals Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

2018 ◽  
Vol 10 (10) ◽  
pp. 3791 ◽  
Author(s):  
Daqing Wu ◽  
Jiazhen Huo ◽  
Gefu Zhang ◽  
Weihua Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Carbon Emissions ◽  
Time Window ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Transport Cost ◽  
Distribution Centers ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will be solved by the proposed quantum-particle swarm optimization algorithm. The three-echelon supply chain, consisting of suppliers, distribution centers, and retailers, is established based on the number and location of suppliers, the transport method from suppliers to distribution centers, and the quantity of products to be transported from suppliers to distribution centers and from these centers to retailers. Then, a quantum-particle swarm optimization is described as its performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to balance the economic benefit and environmental effect.

A PSO Weighted Estimating the Mean Downstream Demand to Mitigate the Bullwhip Effect in a Supply Chain

2011 ◽  
Vol 383-390 ◽  
pp. 4125-4129
Author(s):  
Ling Tzu Tseng
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Particle Swarm ◽  
Bullwhip Effect ◽  
Pso Algorithm ◽  
Inventory Level ◽  
Inventory Cost ◽  
Swarm Optimization ◽  
Demand Information ◽  
Batch Ordering

Bullwhip Effect, Particle Swarm Optimization, Supply Chain, Demand Information Abstract. A deformation phenomenon occurring in business activity, called the bullwhip effect which comes from the demand information is not fully shared among the members of a supply chain, conducts the upstream manufacturer to excessively anticipate the demand capacity of the downstream retailer. The manufacturer improperly decides the amount of the products not only to raise the inventory cost on the way of poorly handling the actually downstream demand, but also to lose the chance of business deals due to its backordering. To cope with the bullwhip effect by taking into account the holding and backorder costs, an evolutionary method based on the Particle Swarm Optimization (PSO) algorithm to estimate the critical parameter, mean downstream demand, is proposed and computer validated in this paper such that the estimated inventory level could be close the really batch ordering of the manufacturer.

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