Title: A hybrid genetic algorithm for appointment scheduling in a health examination system

Authors: Ali Ala; Sajjad Ebadi Torkayesh; Ali Ebadi Torkayesh; Atabak Iranizad

Addresses: Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China ' Department of Industrial Engineering, Islamic Azad University, Tabriz branch, Tabriz, Iran ' Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey ' Faculty of Economics and Management, University of Tabriz, Tabriz, Iran

Abstract: A health examination system is a large system comprised of many units that include sectors or rooms, such as healthcare clinics, each of which requires unique tasks and experts to offer complete and timely healthcare. In general, every HES must accommodate a diverse population of individuals with unique medical histories and special behaviours. Access to and consideration of individuals' medical histories can reduce total exam times within a HES. Therefore, this study proposed a hybrid genetic algorithm that can be used to address the appointment scheduling in a HES to reduce the total preparation time of each room or sector, with a general aim to reduce total examination time. The proposed method is performed for 18 cases with different patient and room sizes in a health system. Results obtained all these cases showed higher accuracy and performance of the proposed method to the CPLEX solver in terms of the objective function and computational time.

Keywords: health examination system; HES; appointment scheduling; hybrid genetic algorithm; HGA; metaheuristic algorithms.

DOI: 10.1504/IJVCM.2020.111075

International Journal of Value Chain Management, 2020 Vol.11 No.4, pp.293 - 310

Received: 16 Sep 2019
Accepted: 09 Jan 2020
Published online: 09 Nov 2020 *

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