Stochastic optimization of two-stage multi-item inventory system with hybrid genetic algorithm

Yuli Zhang; Shiji Song; Cheng Wu; Wenjun Yin

doi:10.1007/978-3-642-15597-0_53

Stochastic optimization of two-stage multi-item inventory system with hybrid genetic algorithm

Yuli Zhang^*
, Shiji Song
, Cheng Wu
, Wenjun Yin

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

This paper considers a two-stage, multi-item inventory system with stochastic demand. First we propose two types of exact stochastic optimization models to minimize the long-run average system cost under installation and echelon (r, nQ) policy. Second we provide an effective hybrid genetic algorithm (HGA) based on the property of the optimization problem. In the proposed HGA, a heuristic search technique, based on the tradeoff between inventory cost and setup cost, is introduced. The long-run average cost of each solution in the model is estimated by Monte Carlo method. At last, computation tests indicate that when variance of stochastic demand increases, echelon policy outperforms installation policy and the proposed heuristic search technique greatly enhances the search capacity of HGA.

Original language	English
Pages (from-to)	484-492
Number of pages	9
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6329 LNCS
Issue number	PART 2
DOIs	https://doi.org/10.1007/978-3-642-15597-0_53
Publication status	Published - 2010
Externally published	Yes
Event	2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010 - Wuxi, China Duration: 17 Sept 2010 → 20 Sept 2010

Keywords

Heuristic search
Hybrid Genetic Algorithm
Monte Carlo method
Stochastic optimization
Two-stage inventory

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10.1007/978-3-642-15597-0_53

Cite this

@article{13a382aec10f4ac293e56c1c1592728b,

title = "Stochastic optimization of two-stage multi-item inventory system with hybrid genetic algorithm",

abstract = "This paper considers a two-stage, multi-item inventory system with stochastic demand. First we propose two types of exact stochastic optimization models to minimize the long-run average system cost under installation and echelon (r, nQ) policy. Second we provide an effective hybrid genetic algorithm (HGA) based on the property of the optimization problem. In the proposed HGA, a heuristic search technique, based on the tradeoff between inventory cost and setup cost, is introduced. The long-run average cost of each solution in the model is estimated by Monte Carlo method. At last, computation tests indicate that when variance of stochastic demand increases, echelon policy outperforms installation policy and the proposed heuristic search technique greatly enhances the search capacity of HGA.",

keywords = "Heuristic search, Hybrid Genetic Algorithm, Monte Carlo method, Stochastic optimization, Two-stage inventory",

author = "Yuli Zhang and Shiji Song and Cheng Wu and Wenjun Yin",

year = "2010",

doi = "10.1007/978-3-642-15597-0\_53",

language = "English",

volume = "6329 LNCS",

pages = "484--492",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

number = "PART 2",

note = "2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010 ; Conference date: 17-09-2010 Through 20-09-2010",

}

Stochastic optimization of two-stage multi-item inventory system with hybrid genetic algorithm. / Zhang, Yuli; Song, Shiji; Wu, Cheng et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6329 LNCS, No. PART 2, 2010, p. 484-492.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - Stochastic optimization of two-stage multi-item inventory system with hybrid genetic algorithm

AU - Zhang, Yuli

AU - Song, Shiji

AU - Wu, Cheng

AU - Yin, Wenjun

PY - 2010

Y1 - 2010

N2 - This paper considers a two-stage, multi-item inventory system with stochastic demand. First we propose two types of exact stochastic optimization models to minimize the long-run average system cost under installation and echelon (r, nQ) policy. Second we provide an effective hybrid genetic algorithm (HGA) based on the property of the optimization problem. In the proposed HGA, a heuristic search technique, based on the tradeoff between inventory cost and setup cost, is introduced. The long-run average cost of each solution in the model is estimated by Monte Carlo method. At last, computation tests indicate that when variance of stochastic demand increases, echelon policy outperforms installation policy and the proposed heuristic search technique greatly enhances the search capacity of HGA.

AB - This paper considers a two-stage, multi-item inventory system with stochastic demand. First we propose two types of exact stochastic optimization models to minimize the long-run average system cost under installation and echelon (r, nQ) policy. Second we provide an effective hybrid genetic algorithm (HGA) based on the property of the optimization problem. In the proposed HGA, a heuristic search technique, based on the tradeoff between inventory cost and setup cost, is introduced. The long-run average cost of each solution in the model is estimated by Monte Carlo method. At last, computation tests indicate that when variance of stochastic demand increases, echelon policy outperforms installation policy and the proposed heuristic search technique greatly enhances the search capacity of HGA.

KW - Heuristic search

KW - Hybrid Genetic Algorithm

KW - Monte Carlo method

KW - Stochastic optimization

KW - Two-stage inventory

UR - https://www.scopus.com/pages/publications/78649586231

U2 - 10.1007/978-3-642-15597-0_53

DO - 10.1007/978-3-642-15597-0_53

M3 - Conference article

AN - SCOPUS:78649586231

SN - 0302-9743

VL - 6329 LNCS

SP - 484

EP - 492

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

IS - PART 2

T2 - 2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010

Y2 - 17 September 2010 through 20 September 2010

ER -

Stochastic optimization of two-stage multi-item inventory system with hybrid genetic algorithm

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Keywords

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