Two-stage stochastic programming approach for the medical drug inventory routing problem under uncertainty

Research output: Contribution to journalArticle

Standard

Two-stage stochastic programming approach for the medical drug inventory routing problem under uncertainty. / Nikzad, Erfaneh; Bashiri, Mahdi; Oliveira, Fabricio.

In: Computers and Industrial Engineering, Vol. 128, 01.02.2019, p. 358-370.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Bibtex - Download

@article{80634ae4245146bbadddbd47a8e483e2,
title = "Two-stage stochastic programming approach for the medical drug inventory routing problem under uncertainty",
abstract = "Medical drug shortages are an important issue in health care, since they can significantly affect patients’ health. Thus, selecting the appropriate distribution and inventory policies plays an important role in decreasing drug shortages. In this context, inventory routing models can be used to determine optimal policies in the context of medical drug distribution. However, in real-world conditions, some parameters in these models are subject to uncertainty. This paper examines the effects of uncertainty in the demand by relying on a two-stage stochastic programming approach to incorporate it into the optimization model. A two-stage model is then proposed and two different approaches based on chance constraints are used to assess the validity of the proposed model. In the first model, a scenario-based two-stage stochastic programming model without probabilistic constraint is proposed, while in the other two models, proposed for validation of the first model, probabilistic constraints are considered. A mathematical-programming based algorithm (a matheuristic) is proposed for solving the models. Moreover, the Latin hypercube sampling method is employed to generate scenarios for the scenario-based models. Numerical examples show the necessity of considering the stochastic nature of the problem and the accuracy of the proposed models and solution method.",
keywords = "Chance constraints, Latin hypercube sampling method, Matheuristic algorithm, Medical drug distribution, Stochastic inventory routing problem, Two-stage stochastic programming",
author = "Erfaneh Nikzad and Mahdi Bashiri and Fabricio Oliveira",
year = "2019",
month = "2",
day = "1",
doi = "10.1016/j.cie.2018.12.055",
language = "English",
volume = "128",
pages = "358--370",
journal = "Computers and Industrial Engineering",
issn = "0360-8352",
publisher = "Elsevier Limited",

}

RIS - Download

TY - JOUR

T1 - Two-stage stochastic programming approach for the medical drug inventory routing problem under uncertainty

AU - Nikzad, Erfaneh

AU - Bashiri, Mahdi

AU - Oliveira, Fabricio

PY - 2019/2/1

Y1 - 2019/2/1

N2 - Medical drug shortages are an important issue in health care, since they can significantly affect patients’ health. Thus, selecting the appropriate distribution and inventory policies plays an important role in decreasing drug shortages. In this context, inventory routing models can be used to determine optimal policies in the context of medical drug distribution. However, in real-world conditions, some parameters in these models are subject to uncertainty. This paper examines the effects of uncertainty in the demand by relying on a two-stage stochastic programming approach to incorporate it into the optimization model. A two-stage model is then proposed and two different approaches based on chance constraints are used to assess the validity of the proposed model. In the first model, a scenario-based two-stage stochastic programming model without probabilistic constraint is proposed, while in the other two models, proposed for validation of the first model, probabilistic constraints are considered. A mathematical-programming based algorithm (a matheuristic) is proposed for solving the models. Moreover, the Latin hypercube sampling method is employed to generate scenarios for the scenario-based models. Numerical examples show the necessity of considering the stochastic nature of the problem and the accuracy of the proposed models and solution method.

AB - Medical drug shortages are an important issue in health care, since they can significantly affect patients’ health. Thus, selecting the appropriate distribution and inventory policies plays an important role in decreasing drug shortages. In this context, inventory routing models can be used to determine optimal policies in the context of medical drug distribution. However, in real-world conditions, some parameters in these models are subject to uncertainty. This paper examines the effects of uncertainty in the demand by relying on a two-stage stochastic programming approach to incorporate it into the optimization model. A two-stage model is then proposed and two different approaches based on chance constraints are used to assess the validity of the proposed model. In the first model, a scenario-based two-stage stochastic programming model without probabilistic constraint is proposed, while in the other two models, proposed for validation of the first model, probabilistic constraints are considered. A mathematical-programming based algorithm (a matheuristic) is proposed for solving the models. Moreover, the Latin hypercube sampling method is employed to generate scenarios for the scenario-based models. Numerical examples show the necessity of considering the stochastic nature of the problem and the accuracy of the proposed models and solution method.

KW - Chance constraints

KW - Latin hypercube sampling method

KW - Matheuristic algorithm

KW - Medical drug distribution

KW - Stochastic inventory routing problem

KW - Two-stage stochastic programming

UR - http://www.scopus.com/inward/record.url?scp=85059055877&partnerID=8YFLogxK

U2 - 10.1016/j.cie.2018.12.055

DO - 10.1016/j.cie.2018.12.055

M3 - Article

VL - 128

SP - 358

EP - 370

JO - Computers and Industrial Engineering

JF - Computers and Industrial Engineering

SN - 0360-8352

ER -

ID: 30817288