Window-Based Multi-Objective Optimization for Dynamic Patient Scheduling with Problem-Specific Operators

Abstract

The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality.