scholarly journals A Pseudo-Parallel Genetic Algorithm Integrating Simulated Annealing for Stochastic Location-Inventory-Routing Problem with Consideration of Returns in E-Commerce

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Bailing Liu ◽  
Hui Chen ◽  
Yanhui Li ◽  
Xiang Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Computing Time ◽  
Single Type ◽  
Inventory Routing ◽  
Parallel Genetic Algorithm ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Stochastic Location ◽  
Pseudo Parallel Genetic Algorithm

Facility location, inventory control, and vehicle routes scheduling are three key issues to be settled in the design of logistics system for e-commerce. Due to the online shopping features of e-commerce, customer returns are becoming much more than traditional commerce. This paper studies a three-phase supply chain distribution system consisting of one supplier, a set of retailers, and a single type of product with continuous review (Q, r) inventory policy. We formulate a stochastic location-inventory-routing problem (LIRP) model with no quality defects returns. To solve the NP-hand problem, a pseudo-parallel genetic algorithm integrating simulated annealing (PPGASA) is proposed. The computational results show that PPGASA outperforms GA on optimal solution, computing time, and computing stability.

scholarly journals A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yanhui Li ◽  
Hao Guo ◽  
Lin Wang ◽  
Jing Fu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Inventory Routing ◽  
Logistics System ◽  
Routing Problem ◽  
Problem Model ◽  
Inventory Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

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