Tabu search and iterated local search for the cyclic bottleneck assignment problem

Abstract

In this paper, we study a new variant of the classical assignment problem referred to as the cyclic bottleneck assignment problem (CBAP). This problem arises in surgical and operating room (OR) scheduling and can be described as follows. Each of n successive block times can be allocated to anyone of n surgeons. The number of patients to be scheduled for surgery on a given block and the length of stay in the recovery unit of each operated patient are dependent on the type of surgery. The problem is to assign one surgeon to each OR time so as to minimize the maximum number of patients that will be expected to occupy the recovery unit in the long run.This problem has been shown to be NP-hard. Consequently, we present two algorithms that can be used to solve the problem efficiently. One algorithm is based on the Tabu search method, and the other follows an iterated local search scheme. We evaluate our algorithms on 470 benchmark instances and 470 newly generated instances, and compare them with available metaheuristics and commercial solver CPLEX. The computational results for most of our problem instances show that our proposed approaches achieve excellent performance. Our algorithms outperform existing approaches and CPLEX. The tabu search approach works slightly better than the iterated local search approach.