Resilient food supply chain design: Modelling framework and metaheuristic solution approach

Eleonora Bottani*, Teresa Murino, Massimo Schiavo, Renzo Akkerman

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

This paper addresses the Resilient Food Supply Chain Design (RFSCD) problem, which is the problem of designing a food supply chain that is resilient enough to ensure business operations continuity in the event of risks or disruptions. Based on a graph theory representation of the food supply chain, this paper proposes a bi-objective mixed-integer programming formulation for this problem. The objectives are to (1) maximize the total profit over a one-year time span and (2) minimize the total lead time of the product along the supply chain. To solve the model, an Ant Colony Optimization (ACO) algorithm is presented. The developed model is suitable for adoption for the design of a multi-product resilient food supply chain that makes use of a multiple sourcing policy to deal with unexpected fluctuations of market demand and disruptions in raw materials supply. The adapted ACO algorithm is tested on a case study, referring to the SC of readymade UHT tomato sauce, which is particularly vulnerable to such risks.

Original languageEnglish
Pages (from-to)177-198
Number of pages22
JournalComputers and Industrial Engineering
Volume135
DOIs
Publication statusPublished - 1 Sep 2019

Fingerprint

Food supply
Supply chains
Ant colony optimization
Graph theory
Integer programming
Profitability
Raw materials
Industry

Keywords

  • Ant colony optimization
  • Food supply chain design
  • Multi-objective optimization
  • Multiple-sourcing policy
  • Resilient supply chain design
  • Supply chain management

Cite this

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title = "Resilient food supply chain design: Modelling framework and metaheuristic solution approach",
abstract = "This paper addresses the Resilient Food Supply Chain Design (RFSCD) problem, which is the problem of designing a food supply chain that is resilient enough to ensure business operations continuity in the event of risks or disruptions. Based on a graph theory representation of the food supply chain, this paper proposes a bi-objective mixed-integer programming formulation for this problem. The objectives are to (1) maximize the total profit over a one-year time span and (2) minimize the total lead time of the product along the supply chain. To solve the model, an Ant Colony Optimization (ACO) algorithm is presented. The developed model is suitable for adoption for the design of a multi-product resilient food supply chain that makes use of a multiple sourcing policy to deal with unexpected fluctuations of market demand and disruptions in raw materials supply. The adapted ACO algorithm is tested on a case study, referring to the SC of readymade UHT tomato sauce, which is particularly vulnerable to such risks.",
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Resilient food supply chain design: Modelling framework and metaheuristic solution approach. / Bottani, Eleonora; Murino, Teresa; Schiavo, Massimo; Akkerman, Renzo.

In: Computers and Industrial Engineering, Vol. 135, 01.09.2019, p. 177-198.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Akkerman, Renzo

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AB - This paper addresses the Resilient Food Supply Chain Design (RFSCD) problem, which is the problem of designing a food supply chain that is resilient enough to ensure business operations continuity in the event of risks or disruptions. Based on a graph theory representation of the food supply chain, this paper proposes a bi-objective mixed-integer programming formulation for this problem. The objectives are to (1) maximize the total profit over a one-year time span and (2) minimize the total lead time of the product along the supply chain. To solve the model, an Ant Colony Optimization (ACO) algorithm is presented. The developed model is suitable for adoption for the design of a multi-product resilient food supply chain that makes use of a multiple sourcing policy to deal with unexpected fluctuations of market demand and disruptions in raw materials supply. The adapted ACO algorithm is tested on a case study, referring to the SC of readymade UHT tomato sauce, which is particularly vulnerable to such risks.

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