Stochastic medical tourism problem with variable residence time considering gravity function

¹ Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
² Rennes School of Business, 2 Rue Robert d’Arbrissel, 35065 Rennes, France

^* Corresponding author: mirzapour@aut.ac.ir; b.mirzapour@gmail.com; seyed.mirzapour-al-e-hashem@esc-rennes.com

Received: 6 January 2022
Accepted: 18 May 2022

Abstract

Medical tourism is a recent term in healthcare logistics referring to travel of patients to receive health services and spending leisure time in a destination country. This transferring of patients leads to access high-quality health services which are cheaper than the original country of patients. During this travel, passengers who are the patients from another country, have this opportunity for complimentary entertainment packages (e.g., pleasure tours) in the aftercare period. As far as we know, the term of medical tourism is rarely studied in healthcare logistics and such services are highly important for developing countries. Such facts motivate us to develop a practical optimization model for the Medical Tour Centers (MTCs) for allocation of patients to hospitals in proper time and creation of memorable aftercare time for them. In this regard, the main aim of the proposed model is to maximize the total profit of MTCs through optimal allocation of patients to hospitals while considering an aftercare tour for the passengers. To make the proposed model more realistic, the optimal residence time in attractive places is simulated by a time-dependent gravity function. To address the uncertainty of medical tourism problem, a scenario-based two-stage stochastic optimization approach is extended to encounter different sources of uncertainty existing in surgical success, medical time, restoration restrictions, and the attraction of tourist places. Another novelty of this work is to propose an innovative hybrid meta-heuristic for large-scale instances, which is a combination of Progressive Hedging Algorithm (PHA) and Genetic Algorithm (GA). The model is analyzed by different test problems for small, medium, and large-scale instances where the hybrid meta-heuristic algorithm could solve them with an average gap of 3.4% in comparison with the commercial solver. The results revealed the importance of tourist opinion and public preferences in medical and pleasure tours, respectively, to improve the economic growth in this sector in developing countries.