Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study

Behzad Zahiri; Jun Zhuang; Mehrdad Mohammadi

doi:10.1016/j.tre.2017.04.009

Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study

Behzad Zahiri (Corresponding author), Jun Zhuang, Mehrdad Mohammadi

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

265 Citations (Scopus)

Abstract

In this paper, a novel multi-objective integrated sustainable-resilient mixed integer linear programming model for designing a pharmaceutical supply chain network under uncertainty is presented. To cope with the uncertainty aspect of the model, a new fuzzy possibilistic-stochastic programming approach is developed. Additionally, due to NP-hard nature of the problem, we propose a novel Pareto-based lower bound method as well as a new meta-heuristic algorithm.

Original language	English
Pages (from-to)	109-142
Number of pages	33
Journal	Transportation Research. Part E: Logistics and Transportation Review
Volume	103
DOIs	https://doi.org/10.1016/j.tre.2017.04.009
Publication status	Published - 1 Jul 2017
Externally published	Yes

Keywords

HIV
LGBTQ
Pharmaceutical supply chain network design
Resiliency
Sustainability
Uncertainty

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.tre.2017.04.009

Cite this

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title = "Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study",

abstract = "In this paper, a novel multi-objective integrated sustainable-resilient mixed integer linear programming model for designing a pharmaceutical supply chain network under uncertainty is presented. To cope with the uncertainty aspect of the model, a new fuzzy possibilistic-stochastic programming approach is developed. Additionally, due to NP-hard nature of the problem, we propose a novel Pareto-based lower bound method as well as a new meta-heuristic algorithm.",

keywords = "HIV, LGBTQ, Pharmaceutical supply chain network design, Resiliency, Sustainability, Uncertainty",

author = "Behzad Zahiri and Jun Zhuang and Mehrdad Mohammadi",

year = "2017",

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day = "1",

doi = "10.1016/j.tre.2017.04.009",

language = "English",

volume = "103",

pages = "109--142",

journal = "Transportation Research. Part E: Logistics and Transportation Review",

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TY - JOUR

T1 - Toward an integrated sustainable-resilient supply chain

T2 - A pharmaceutical case study

AU - Zahiri, Behzad

AU - Zhuang, Jun

AU - Mohammadi, Mehrdad

PY - 2017/7/1

Y1 - 2017/7/1

N2 - In this paper, a novel multi-objective integrated sustainable-resilient mixed integer linear programming model for designing a pharmaceutical supply chain network under uncertainty is presented. To cope with the uncertainty aspect of the model, a new fuzzy possibilistic-stochastic programming approach is developed. Additionally, due to NP-hard nature of the problem, we propose a novel Pareto-based lower bound method as well as a new meta-heuristic algorithm.

AB - In this paper, a novel multi-objective integrated sustainable-resilient mixed integer linear programming model for designing a pharmaceutical supply chain network under uncertainty is presented. To cope with the uncertainty aspect of the model, a new fuzzy possibilistic-stochastic programming approach is developed. Additionally, due to NP-hard nature of the problem, we propose a novel Pareto-based lower bound method as well as a new meta-heuristic algorithm.

KW - HIV

KW - LGBTQ

KW - Pharmaceutical supply chain network design

KW - Resiliency

KW - Sustainability

KW - Uncertainty

UR - https://www.scopus.com/pages/publications/85019053407

U2 - 10.1016/j.tre.2017.04.009

DO - 10.1016/j.tre.2017.04.009

M3 - Article

AN - SCOPUS:85019053407

SN - 1366-5545

VL - 103

SP - 109

EP - 142

JO - Transportation Research. Part E: Logistics and Transportation Review

JF - Transportation Research. Part E: Logistics and Transportation Review

ER -

Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study

Abstract

Keywords

UN SDGs

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