DocumentCode :
2230601
Title :
Partial chain based GA for joint inventory and delivery scheduling with vehicle rent way
Author :
Tang, Jiafu ; Luo, Xinggang ; Zhang, Jun
Author_Institution :
Dept. of Syst. Eng., Northeastern Univ., Shenyang, China
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
1920
Lastpage :
1924
Abstract :
A partial chain based Genetic Algorithm (PCGA) is developed to solve the integrated inventory and delivery scheduling in a distribution network where multiproducts at one supplier are distributed to several retailers. The shipments of products to the retailers are subcontracted to professional transportation enterprises via vehicle-rent way. Distinguished from many other distribution models, the amount of vehicles is viewed upon as an operational decision simultaneously with the delivery schedule in the planning horizon so as to cater for quick changes in market demand. The jointly inventory and delivery scheduling problem addressed tries to determine not only the schedule of delivery, but also the number of vehicles required towards minimum total costs of inventory, transportation and rental costs in vehicles over the planning horizon. An example is introduced to test the efficiency of the method and sensitivity analysis is conducted also in the paper.
Keywords :
genetic algorithms; goods distribution; inventory management; scheduling; transportation; delivery schedule; delivery scheduling problem; distribution models; distribution network; integrated inventory; inventory scheduling problem; joint inventory; market demand; operational decision; partial chain based GA; planning horizon; retailers; transportation enterprises; vehicle rent way; vehicle-rent way; Automotive engineering; Costs; Genetic algorithms; Genetic engineering; Mathematical model; Production planning; Routing; Subcontracting; Transportation; Vehicle dynamics; 0-1mixed integer programming; Distribution network; Genetic Algorithm; flexible vehicle capacity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2629-4
Electronic_ISBN :
978-1-4244-2630-0
Type :
conf
DOI :
10.1109/IEEM.2008.4738206
Filename :
4738206
Link To Document :
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