Paper Info
Title
An adaptive genetic algorithm for the time dependent inventory routing problem
Abstract
In this paper we propose an adaptive genetic algorithm that produces good quality solutions to the time dependent inventory routing problem (TDIRP) in which inventory control and time dependent vehicle routing decisions for a set of retailers are made simultaneously over a specific planning horizon. This work is motivated by the effect of dynamic traffic conditions in an urban context and the resulting inventory and transportation costs. We provide a mixed integer programming formulation for TDIRP. Since finding the optimal solutions for TDIRP is a NP-hard problem, an adaptive genetic algorithm is applied. We develop new genetic representation and design suitable crossover and mutation operators for the improvement phase. We use adaptive genetic operator proposed by Yun and Gen (Fuzzy Optim Decis Mak 2(2):161---175, 2003 ) for the automatic setting of the genetic parameter values. The comparison of results shows the significance of the designed AGA and demonstrates the capability of reaching solutions within 0.5 % of the optimum on sets of test problems.
Year
DOI
Venue
2014
10.1007/s10845-012-0727-5
Journal of Intelligent Manufacturing
Keywords
Field
DocType
Time dependent inventory routing problem,Adaptive genetic algorithm,Mixed integer programming
Genetic operator,Vehicle routing problem,Mathematical optimization,Crossover,Time horizon,Inventory control,Integer programming,Genetic representation,Engineering,Genetic algorithm
Journal
Volume
Issue
ISSN
25
5
0956-5515
Citations 
PageRank 
References 
7
0.48
25
Authors
4
Name
Order
Citations
PageRank
Dong Won Cho1382.49
Young Hae Lee218314.03
Tae Youn Lee370.48
Mitsuo Gen41873130.43