PMID- 24453822
OWN - NLM
STAT- MEDLINE
DCOM- 20140919
LR  - 20211021
IS  - 1537-744X (Electronic)
IS  - 2356-6140 (Print)
IS  - 1537-744X (Linking)
VI  - 2013
DP  - 2013
TI  - An effective hybrid self-adapting differential evolution algorithm for the joint 
      replenishment and location-inventory problem in a three-level supply chain.
PG  - 270249
LID - 10.1155/2013/270249 [doi]
LID - 270249
AB  - 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.
FAU - Wang, Lin
AU  - Wang L
AD  - School of Management, Huazhong University of Science and Technology, Wuhan 
      430074, China.
FAU - Qu, Hui
AU  - Qu H
AD  - School of Management, Huazhong University of Science and Technology, Wuhan 
      430074, China.
FAU - Chen, Tao
AU  - Chen T
AD  - College of Public Administration, Huazhong University of Science and Technology, 
      Wuhan 430074, China.
FAU - Yan, Fang-Ping
AU  - Yan FP
AD  - School of Management, Huazhong University of Science and Technology, Wuhan 
      430074, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20131217
PL  - United States
TA  - ScientificWorldJournal
JT  - TheScientificWorldJournal
JID - 101131163
SB  - IM
MH  - *Algorithms
MH  - *Models, Theoretical
PMC - PMC3878286
EDAT- 2014/01/24 06:00
MHDA- 2014/09/23 06:00
PMCR- 2013/12/17
CRDT- 2014/01/24 06:00
PHST- 2013/09/29 00:00 [received]
PHST- 2013/10/27 00:00 [accepted]
PHST- 2014/01/24 06:00 [entrez]
PHST- 2014/01/24 06:00 [pubmed]
PHST- 2014/09/23 06:00 [medline]
PHST- 2013/12/17 00:00 [pmc-release]
AID - 10.1155/2013/270249 [doi]
PST - epublish
SO  - ScientificWorldJournal. 2013 Dec 17;2013:270249. doi: 10.1155/2013/270249. 
      eCollection 2013.