An adaptive genetic algorithm for the time dependent inventory routing problem

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....

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Vydáno v:Journal of intelligent manufacturing Ročník 25; číslo 5; s. 1025 - 1042
Hlavní autoři: Cho, Dong Won, Lee, Young Hae, Lee, Tae Youn, Gen, Mitsuo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.10.2014
Springer Nature B.V
Témata:
Adaptive algorithms
Algorithms
Analysis
Business and Management
Control
Costs
Customers
Fuzzy
Genetic algorithms
Genetics
Integer programming
Intelligent systems
Inventories
Inventory
Inventory control
Inventory management
Machines
Manufacturing
Materials handling
Mathematical programming
Mechatronics
Operators
Optimization
Processes
Production
Retail stores
Robotics
Routing
Stockpiling
Studies
Suppliers
Supply chains
Vehicles
Adaptive genetic algorithm
Mixed integer programming
Time dependent inventory routing problem
ISSN:0956-5515, 1572-8145
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Popis
Shrnutí: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.
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-012-0727-5