A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints

Zhuo Dai, Faisal Aqlan, Xiaoting Zheng, Kuo Gao

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS's solution is higher than that of HGA's solution, whereas HGA is faster than HHS.

Original languageEnglish (US)
Pages (from-to)338-352
Number of pages15
JournalComputers and Industrial Engineering
Volume119
DOIs
StatePublished - May 1 2018

Fingerprint

Heuristic algorithms
Supply chains
Genetic algorithms
Nonlinear programming
Costs
Carbon
Industry
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

@article{165c72d1c94942c4844ed0332bc9cc70,
title = "A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints",
abstract = "Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS's solution is higher than that of HGA's solution, whereas HGA is faster than HHS.",
author = "Zhuo Dai and Faisal Aqlan and Xiaoting Zheng and Kuo Gao",
year = "2018",
month = "5",
day = "1",
doi = "10.1016/j.cie.2018.04.007",
language = "English (US)",
volume = "119",
pages = "338--352",
journal = "Computers and Industrial Engineering",
issn = "0360-8352",
publisher = "Elsevier Limited",

}

A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. / Dai, Zhuo; Aqlan, Faisal; Zheng, Xiaoting; Gao, Kuo.

In: Computers and Industrial Engineering, Vol. 119, 01.05.2018, p. 338-352.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints

AU - Dai, Zhuo

AU - Aqlan, Faisal

AU - Zheng, Xiaoting

AU - Gao, Kuo

PY - 2018/5/1

Y1 - 2018/5/1

N2 - Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS's solution is higher than that of HGA's solution, whereas HGA is faster than HHS.

AB - Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS's solution is higher than that of HGA's solution, whereas HGA is faster than HHS.

UR - http://www.scopus.com/inward/record.url?scp=85045098916&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045098916&partnerID=8YFLogxK

U2 - 10.1016/j.cie.2018.04.007

DO - 10.1016/j.cie.2018.04.007

M3 - Article

AN - SCOPUS:85045098916

VL - 119

SP - 338

EP - 352

JO - Computers and Industrial Engineering

JF - Computers and Industrial Engineering

SN - 0360-8352

ER -