Improving the Efficiency of Tabu Search for Machine Sequencing Problems

Abstract

Tabu search (TS) is becoming increasingly recognized as an efficient way of finding high-quality solutions to hard combinatorial problems. It may be described as an intelligent meta-heuristic for controlling simpler local search procedures. However, reported applications have often used a ‘brute-force’ approach without considering the most effective use of the computing effort available. This paper intends firstly to give a basic introduction to the ideas of TS, and then it will report some computational experiments on TS in the context of machine sequencing. These have shown that it is important to define the balance between exploration of the solution space and exploitation of the information obtained. The results will be compared with those obtained from a proven Simulated Annealing (SA) algorithm, which tends to confirm the general opinion that when implemented efficiently, TS is a more effective search paradigm than SA.