DocumentCode :
2804564
Title :
Optimal Design of Returned Logistics Network Based on Genetic Algorithm
Author :
Di, Weimin
Author_Institution :
Sch. of Manage. Eng., Zhengzhou Univ., Zhengzhou, China
Volume :
4
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
374
Lastpage :
378
Abstract :
To improve returned logistics management performance, an appropriate design approach for returned logistics network structures and operation schedules are proposed. Synthetically taking into account the location problem and inventory relationships of inter-related nodes, a nonlinear integer programming (NLIP) model is developed. With the help of the model, the following items can be computed, such as the optimal sites and numbers of both established processing factories and collection centers, the logistics flows in returned logistics network and the working days per period of collection centers, also the minimum fees in planning horizon can be obtained simultaneously. In order to acquire design result effectively and efficiently, the genetic algorithm for NLIP model is presented, and its realization steps are introduced. Besides, effectiveness of NLIP model and the given algorithm, as well as the influence of inventory carrying fees on design result are verified by an example.
Keywords :
genetic algorithms; logistics; nonlinear programming; optimal systems; appropriate design approach; genetic algorithm; inter related nodes; logistics network structures; nonlinear integer programming; operation schedules; optimal design; returned logistics management performance; returned logistics network; Algorithm design and analysis; Computer network management; Computer networks; Conference management; Costs; Genetic algorithms; Logistics; Mathematical model; Mathematical programming; Production facilities;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
Type :
conf
DOI :
10.1109/ICNC.2009.342
Filename :
5362638
Link To Document :
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