Genetic Algorithm for Inventory Positioning Problem with General Acyclic Supply Chain Networks

Haitao Li; Dali Jiang; Tinghong Yang; De Li

Genetic Algorithm for Inventory Positioning Problem with General Acyclic Supply Chain Networks

Haitao Li, Dali Jiang, Tinghong Yang, De Li

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Abstract

Inventory positioning, also known as safety stock placement, in supply chain networks is an important optimisation problem that has various applications in supply chain design and configuration. In this paper, we develop a new genetic algorithm (GA) for this NP-hard problem. Our new GA features custom designed procedure to generate feasible individuals by exploiting the problem structure. It also implements a multi-start strategy to enhance solution quality. Computational results show that our GA is able to offer near optimal solutions in reasonable computational time.

Original language	American English
Journal	European J. Industrial Engineering
Volume	10
State	Published - Jan 18 2016

Disciplines

Business Administration, Management, and Operations

Access to Document

GA-SupplyChain-2016.pdfFinal published version, 328 KBLicense: Unspecified

Cite this

@article{50ab541b7ce54c06877322c0c0b05b72,

title = "Genetic Algorithm for Inventory Positioning Problem with General Acyclic Supply Chain Networks",

abstract = " Inventory positioning, also known as safety stock placement, in supply chain networks is an important optimisation problem that has various applications in supply chain design and configuration. In this paper, we develop a new genetic algorithm (GA) for this NP-hard problem. Our new GA features custom designed procedure to generate feasible individuals by exploiting the problem structure. It also implements a multi-start strategy to enhance solution quality. Computational results show that our GA is able to offer near optimal solutions in reasonable computational time.",

author = "Haitao Li and Dali Jiang and Tinghong Yang and De Li",

year = "2016",

month = jan,

day = "18",

language = "American English",

volume = "10",

journal = "European J. Industrial Engineering",

}

TY - JOUR

T1 - Genetic Algorithm for Inventory Positioning Problem with General Acyclic Supply Chain Networks

AU - Li, Haitao

AU - Jiang, Dali

AU - Yang, Tinghong

AU - Li, De

PY - 2016/1/18

Y1 - 2016/1/18

N2 - Inventory positioning, also known as safety stock placement, in supply chain networks is an important optimisation problem that has various applications in supply chain design and configuration. In this paper, we develop a new genetic algorithm (GA) for this NP-hard problem. Our new GA features custom designed procedure to generate feasible individuals by exploiting the problem structure. It also implements a multi-start strategy to enhance solution quality. Computational results show that our GA is able to offer near optimal solutions in reasonable computational time.

AB - Inventory positioning, also known as safety stock placement, in supply chain networks is an important optimisation problem that has various applications in supply chain design and configuration. In this paper, we develop a new genetic algorithm (GA) for this NP-hard problem. Our new GA features custom designed procedure to generate feasible individuals by exploiting the problem structure. It also implements a multi-start strategy to enhance solution quality. Computational results show that our GA is able to offer near optimal solutions in reasonable computational time.

M3 - Article

VL - 10

JO - European J. Industrial Engineering

JF - European J. Industrial Engineering

ER -