A differential evolution algorithm with double-mode crossover for supply chain scheduling in cold rolling

2016 ◽  
Author(s):  
Shengnan Zhao ◽  
Yang Yang ◽  
Qingxin Guo ◽  
Lixin Tang
Keyword(s):  
Supply Chain ◽  
Cold Rolling ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Supply Chain Scheduling ◽  
Evolution Algorithm

scholarly journals Optimizing the Joint Replenishment and Channel Coordination Problem under Supply Chain Environment Using a Simple and Effective Differential Evolution Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Lin Wang ◽  
Hui Qu ◽  
Shan Liu ◽  
Can Chen
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Cost Savings ◽  
Channel Coordination ◽  
Coordination Problem ◽  
Joint Replenishment ◽  
Np Hard Problem ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

This paper presents a useful and practical procurement approach using the joint replenishment and channel coordination (JR-CC) policy in a two-echelon supply chain considering the coordination cost. The objective is to determine a basic replenishment cycle time and the replenishment interval to minimize the total cost of the supply chain. To solve this NP-hard problem, a simple and improved differential evolution algorithm (IDE) is developed. The performance of the IDE is verified by benchmark functions. Moreover, results of comparative numerical example show the effectiveness of the proposed IDE. IDE can be used as a good candidate for the JR-CC model. Results of numerical examples also indicate that the JR-CC policy can result in considerable cost saving, and enhance the efficiency of a supply chain. But not all members in the supply chain can benefit a lot using this policy. Moreover, results of sensitivity analysis show that retailers have more willingness to adopt the JR-CC policy than the manufacturers because of the different cost savings.

scholarly journals An Effective Hybrid Self-Adapting Differential Evolution Algorithm for the Joint Replenishment and Location-Inventory Problem in a Three-Level Supply Chain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Lin Wang ◽  
Hui Qu ◽  
Tao Chen ◽  
Fang-Ping Yan
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Complex Problem ◽  
Inventory Problem ◽  
Distribution Centers ◽  
Joint Replenishment ◽  
Evolution Algorithm ◽  
Comparative Results

The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP’s difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem.

Modified differential evolution algorithm for semi-supervised fuzzy clustering

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO
Keyword(s):  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm
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