Akademik Veri Yönetim Sistemi
The 3rd International Conference on Applied Mathematics in Engineering (ICAME’24), Balıkesir, Türkiye, 26 - 28 Haziran 2024, ss.132
Home health care services offer the most important conditions for customers to receive more economical and better quality service at home under sterile conditions [1-2]. Vehicle routing and scheduling are important in the successful performance of home health care services. The time period during which the patient will be served, the quality of the service provided by health professionals and the travel costs that will occur depending on the route are important in planning process. Assuming that patients' treatment times are known in advance, patients request a variety of services and teams can provide services of varying quality depending on their level of service proficiency. It is possible to examine current life problems in a multi-objective way to reach realistic solutions. In the literature, it has been seen in home health care studies that the problem is combined mostly with weighted objective functions [3]. In scalarization, weights are chosen in proportion to the relative importance of the objective to find a single solution that satisfies the subjective preferences of a decision maker. However, it can be required to achieve the complete pareto front instead of a single solution in real world problems. In this study, in addition to team competencies, maximum travel limits have been determined for vehicles and teams. Pareto solutions were obtained with Multi Objective Manta Ray Foraging Optimizer [4]. Multi Objective Manta Ray Foraging Algorithm is a population-based algorithm that mimics foraging strategies of manta rays such as chain foraging, cyclone foraging, and somersault foraging. The integration of a fixed-sized external archive preserves the elitist concept by preserving the optimal Pareto set throughout the optimization process. Algorithm is coded on MATLAB R2021a and simulations are performed for different test instances to achieve the pareto solutions. The results obtained with Multi Objective Manta Ray Foraging Algorithm is compared with the outcomes from Non-dominated Sorting Genetic Algorithm and Multi Objective Particle Swarm Optimization to prove the accuracy of proposed approach.