Route scheduling for HSSP using adaptive genetic algorithm with constructive scheduling technique

Abstract

Shortest path finding has been a challenging task in most of the complex multipath scenarios. The complexity rises with the introduction of constraints to the scenarios. Healthcare service to the patient is one of the real world problems where travelling path has significant impact on the service time. The purpose of this research is to develop new approach to solve multiple travelling salesman problem (MTSP) for healthcare staff members offering healthcare services at patients homes travelling in different routes with the minimum total cost. The proposed approach uses Genetic Algorithm (GA) combined with Constructive Scheduling, Local Search, and Adaptive Technique to increase the efficiency. A case study with 45 patient task locations is generated according to referenced work. The result shows that the combined algorithms explore improved solution than that of the traditional GA. Constructive Scheduling using K-mean algorithm is applied to generate initial chromosome which provides improved results with acceptable computational time. Also, Adaptive GA shows a few different solutions to the traditional GA. All these approaches are beneficial to the traditional method in shortest path finding problems.

Heuristic and fuzzy scheduling synergy with knowledge acquisition of resource selection system development for home healthcare service

“…The first step creates possible demand service scenarios while the second stage estimates the optimal number of staff members offering the service. In our previous works, the experimental results (Sinthamrongruk, 2016), (Sinthamrongruk and Dahal, 2017) indicated that hybrid approach based on Genetic Algorithm (GA) provided the optimal service route.…”