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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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.
AbstractList	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. Issue Title: Special Issues: "Manufacturing Intelligence and Innovation for Digital Manufacturing and Operational Excellence" and "Information Control Problems in Manufacturing - INCOM 2012" 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.[PUBLICATION 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.
Author	Cho, Dong Won Gen, Mitsuo Lee, Young Hae Lee, Tae Youn
Author_xml	– sequence: 1 givenname: Dong Won surname: Cho fullname: Cho, Dong Won organization: Department of Industrial and Management Engineering, Hanyang University – sequence: 2 givenname: Young Hae surname: Lee fullname: Lee, Young Hae email: yhlee@hanyang.ac.kr organization: Department of Industrial and Management Engineering, Hanyang University – sequence: 3 givenname: Tae Youn surname: Lee fullname: Lee, Tae Youn organization: Department of Industrial and Management Engineering, Hanyang University – sequence: 4 givenname: Mitsuo surname: Gen fullname: Gen, Mitsuo organization: Fuzzy Logic Systems Institute
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CitedBy_id	crossref_primary_10_1016_j_cie_2019_04_026 crossref_primary_10_1016_j_eswa_2016_01_041 crossref_primary_10_1038_s41598_024_70781_0 crossref_primary_10_1016_j_cor_2019_02_014 crossref_primary_10_1007_s10845_015_1109_6 crossref_primary_10_1007_s10845_019_01518_4 crossref_primary_10_1007_s10845_015_1078_9 crossref_primary_10_1007_s12046_016_0538_z crossref_primary_10_3390_en11061437 crossref_primary_10_15446_dyna_v85n205_68282 crossref_primary_10_1007_s11277_018_5251_2 crossref_primary_10_1016_j_asoc_2023_110023 crossref_primary_10_3390_su11174628 crossref_primary_10_3390_math9182233 crossref_primary_10_1177_0037549715603481 crossref_primary_10_15446_dyna_v85n207_72546 crossref_primary_10_1007_s10845_015_1117_6 crossref_primary_10_1016_j_ijpe_2016_09_001 crossref_primary_10_1016_j_ejor_2021_09_025 crossref_primary_10_1088_1755_1315_440_4_042075
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Snippet	In this paper we propose an adaptive genetic algorithm that produces good quality solutions to the time dependent inventory routing problem (TDIRP) in which... Issue Title: Special Issues: "Manufacturing Intelligence and Innovation for Digital Manufacturing and Operational Excellence" and "Information Control Problems...
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SubjectTerms	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
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Title	An adaptive genetic algorithm for the time dependent inventory routing problem
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