A Stochastic Optimization Approach to a Location-Allocation Problem of Organ Transplant Centers

Ghane, Mahshid; Tavakkoli-Moghaddam, Reza

doi:10.22094/joie.2018.266

A Stochastic Optimization Approach to a Location-Allocation Problem of Organ Transplant Centers

Document Type : Original Manuscript

Authors

¹ MSc, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

² Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

10.22094/joie.2018.266

Abstract

Decision-making concerning thelocation of critical resource on the geographical network is important in many industries.In the healthcare system,these decisions include location of emergency and preventive care. The decisions of location play a crucial role due to determining the travel time between supply and de//////mand points and response time in emergencies.Organs are considered as highly perishable products,whosevarietyof each product has a specific perish time. Despite the importance of this field,only a small proportion of healthcare sector is dedicated to this field. Matching and finding the best recipient for a donated organ is one of the major problems in this field, which is also crucial for the overall organ transplantation process.Balancing the demand and supply in a transplant organ supply chain in order to decrease the waiting list needs certain scheduling and management.The main contribution of this paper consists of considering recipient regionsas another component of the supply chain;in addition,importance of transportation time and waiting lists hasled us to consider a bi-objective model. In addition, uncertainty of input data has led us to consider a stochastic approach.

Highlights

A bi-objective stochastic mathematical model for a location-allocation problem of organ transplant centers is proposed
A mixed-integer nonlinear programming model is considered for minimizing the total transportation time and the maximizing shortage of organ simultaneously
A scenario-based stochastic approach is used in order to model the problem
The ε-constraint method is proposed for solving the problem

Keywords

Main Subjects