Optimising integrated inventory policy for perishable items in a multi-stage supply chain

Alexandre Dolgui, Manoj Kumar Tiwari, Yerasani Sinjana, Sri Krishna Kumar, Young-Jun Son

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

The value of perishable products is most affected by the time delays in a supply chain. A major issue is how to integrate the existing practices in production, inventory holding and distribution, besides considering the perishable nature of the products, so as to deliver an optimised policy for the perishable commodities. Standard inventory control models are often not adequate for perishable products and there is a need for a new integrated model to focus on consolidation of production, inventory and distribution processes. We develop such a mathematical model to search for an optimal integrated inventory policy for perishable items in a multi-stage supply chain. We specifically assume the exponential deterioration rate so as to be consistent with the growth rate of the micro-organisms responsible for deterioration. We propose and analyse some general properties of the model and apply it to a three-stage supply chain. We show that this integrated model which includes inventory control and fleet selection can be optimised with an evolutionary technique like genetic algorithm. A novel genetic algorithm that avoids revisits and employs a parameter-less self-adaptive mutation operator is developed. The results are compared with those obtained with CPLEX for small-sized problems. We show that our model and optimisation approach gives near optimal results for varied demand scenarios.

Original languageEnglish (US)
Pages (from-to)1-24
Number of pages24
JournalInternational Journal of Production Research
DOIs
StateAccepted/In press - Dec 1 2017

Fingerprint

Supply chains
Inventory control
Deterioration
Genetic algorithms
Consolidation
Time delay
Integrated
Supply chain
Perishable items
Inventory policy
Mathematical models
Production-inventory
Integrated model
Perishable products
Genetic algorithm
Scenarios
Mathematical model
Microorganisms
Evolutionary
Mutation

Keywords

  • fleet selection
  • non-revisiting genetic algorithm
  • perishable products
  • production-inventory-distribution
  • supply chain

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Optimising integrated inventory policy for perishable items in a multi-stage supply chain. / Dolgui, Alexandre; Tiwari, Manoj Kumar; Sinjana, Yerasani; Kumar, Sri Krishna; Son, Young-Jun.

In: International Journal of Production Research, 01.12.2017, p. 1-24.

Research output: Contribution to journalArticle

Dolgui, Alexandre ; Tiwari, Manoj Kumar ; Sinjana, Yerasani ; Kumar, Sri Krishna ; Son, Young-Jun. / Optimising integrated inventory policy for perishable items in a multi-stage supply chain. In: International Journal of Production Research. 2017 ; pp. 1-24.
@article{b1160e25028c45d6a8b5fc52e9e4e91a,
title = "Optimising integrated inventory policy for perishable items in a multi-stage supply chain",
abstract = "The value of perishable products is most affected by the time delays in a supply chain. A major issue is how to integrate the existing practices in production, inventory holding and distribution, besides considering the perishable nature of the products, so as to deliver an optimised policy for the perishable commodities. Standard inventory control models are often not adequate for perishable products and there is a need for a new integrated model to focus on consolidation of production, inventory and distribution processes. We develop such a mathematical model to search for an optimal integrated inventory policy for perishable items in a multi-stage supply chain. We specifically assume the exponential deterioration rate so as to be consistent with the growth rate of the micro-organisms responsible for deterioration. We propose and analyse some general properties of the model and apply it to a three-stage supply chain. We show that this integrated model which includes inventory control and fleet selection can be optimised with an evolutionary technique like genetic algorithm. A novel genetic algorithm that avoids revisits and employs a parameter-less self-adaptive mutation operator is developed. The results are compared with those obtained with CPLEX for small-sized problems. We show that our model and optimisation approach gives near optimal results for varied demand scenarios.",
keywords = "fleet selection, non-revisiting genetic algorithm, perishable products, production-inventory-distribution, supply chain",
author = "Alexandre Dolgui and Tiwari, {Manoj Kumar} and Yerasani Sinjana and Kumar, {Sri Krishna} and Young-Jun Son",
year = "2017",
month = "12",
day = "1",
doi = "10.1080/00207543.2017.1407500",
language = "English (US)",
pages = "1--24",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - Optimising integrated inventory policy for perishable items in a multi-stage supply chain

AU - Dolgui, Alexandre

AU - Tiwari, Manoj Kumar

AU - Sinjana, Yerasani

AU - Kumar, Sri Krishna

AU - Son, Young-Jun

PY - 2017/12/1

Y1 - 2017/12/1

N2 - The value of perishable products is most affected by the time delays in a supply chain. A major issue is how to integrate the existing practices in production, inventory holding and distribution, besides considering the perishable nature of the products, so as to deliver an optimised policy for the perishable commodities. Standard inventory control models are often not adequate for perishable products and there is a need for a new integrated model to focus on consolidation of production, inventory and distribution processes. We develop such a mathematical model to search for an optimal integrated inventory policy for perishable items in a multi-stage supply chain. We specifically assume the exponential deterioration rate so as to be consistent with the growth rate of the micro-organisms responsible for deterioration. We propose and analyse some general properties of the model and apply it to a three-stage supply chain. We show that this integrated model which includes inventory control and fleet selection can be optimised with an evolutionary technique like genetic algorithm. A novel genetic algorithm that avoids revisits and employs a parameter-less self-adaptive mutation operator is developed. The results are compared with those obtained with CPLEX for small-sized problems. We show that our model and optimisation approach gives near optimal results for varied demand scenarios.

AB - The value of perishable products is most affected by the time delays in a supply chain. A major issue is how to integrate the existing practices in production, inventory holding and distribution, besides considering the perishable nature of the products, so as to deliver an optimised policy for the perishable commodities. Standard inventory control models are often not adequate for perishable products and there is a need for a new integrated model to focus on consolidation of production, inventory and distribution processes. We develop such a mathematical model to search for an optimal integrated inventory policy for perishable items in a multi-stage supply chain. We specifically assume the exponential deterioration rate so as to be consistent with the growth rate of the micro-organisms responsible for deterioration. We propose and analyse some general properties of the model and apply it to a three-stage supply chain. We show that this integrated model which includes inventory control and fleet selection can be optimised with an evolutionary technique like genetic algorithm. A novel genetic algorithm that avoids revisits and employs a parameter-less self-adaptive mutation operator is developed. The results are compared with those obtained with CPLEX for small-sized problems. We show that our model and optimisation approach gives near optimal results for varied demand scenarios.

KW - fleet selection

KW - non-revisiting genetic algorithm

KW - perishable products

KW - production-inventory-distribution

KW - supply chain

UR - http://www.scopus.com/inward/record.url?scp=85035761703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85035761703&partnerID=8YFLogxK

U2 - 10.1080/00207543.2017.1407500

DO - 10.1080/00207543.2017.1407500

M3 - Article

SP - 1

EP - 24

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

ER -