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Learning-Based Metaheuristic Approach for Home Healthcare Optimization Problem

Mariem Belhor1,2,3, Adnen El-Amraoui1,*, Abderrazak Jemai2, François Delmotte1

1 Univ. Artois, LGI2A Laboratory, UR 3926, Technoparc FUTURA 62400, Béthune, France
2 Univ. Carthage, SERCOM Laboratory, EPT, Marsa, 2078, Tunisia
3 Univ. Manouba, National School of Computer Science, Manouba, 2010, Tunisia

* Corresponding Author: Adnen El-Amraoui. Email: email

Computer Systems Science and Engineering 2023, 45(1), 1-19.


This research focuses on the home health care optimization problem that involves staff routing and scheduling problems. The considered problem is an extension of multiple travelling salesman problem. It consists of finding the shortest path for a set of caregivers visiting a set of patients at their homes in order to perform various tasks during a given horizon. Thus, a mixed-integer linear programming model is proposed to minimize the overall service time performed by all caregivers while respecting the workload balancing constraint. Nevertheless, when the time horizon become large, practical-sized instances become very difficult to solve in a reasonable computational time. Therefore, a new Learning Genetic Algorithm for mTSP (LGA-mTSP) is proposed to solve the problem. LGA-mTSP is composed of a new genetic algorithm for mTSP, combined with a learning approach, called learning curves. Learning refers to that caregivers’ productivity increases as they gain more experience. Learning curves approach is considered as a way to save time and costs. Simulation results show the efficiency of the proposed approach and the impact of learning curve strategy to reduce service times.


Cite This Article

M. Belhor, A. El-Amraoui, A. Jemai and F. Delmotte, "Learning-based metaheuristic approach for home healthcare optimization problem," Computer Systems Science and Engineering, vol. 45, no.1, pp. 1–19, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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