A biased random-key genetic algorithm for the home health care problem

TL;DR

This paper introduces a biased random-key genetic algorithm to effectively solve the complex home health care routing and scheduling problem, achieving significant improvements over existing methods in computational experiments.

Contribution

The paper proposes a novel biased random-key genetic algorithm tailored for the home health care routing problem, incorporating recent state-of-the-art components for enhanced performance.

Findings

Up to 26.1% better results than local search methods

Improvements of 0.4% to 6.36% over previous genetic algorithms

Effective handling of multi-attribute routing with soft time windows

Abstract

Home health care problems consist of scheduling visits to home patients by health professionals while following a series of requirements. This paper studies the Home Health Care Routing and Scheduling Problem, which comprises a multi-attribute vehicle routing problem with soft time windows. Additional route inter-dependency constraints apply for patients requesting multiple visits, either by simultaneous visits or visits with precedence. We apply a mathematical programming solver to obtain lower bounds for the problem. We also propose a biased random-key genetic algorithm, and we study the effects of additional state-of-art components recently proposed in the literature for this genetic algorithm. We perform computational experiment using a publicly available benchmark dataset. Regarding the previous local search-based methods, we find results up to 26.1% better than those of the literature. We find improvements from around 0.4% to 6.36% compared to previous results from a similar genetic algorithm.