Neighbourhood search-based optimization algorithms for production scheduling: a survey

Abstract

Neighbourhood search is one of the general strategies used in designing heuristic algorithms for discrete optimization. Apart from its simplicity from the conceptual and implementation point of view, a notable characteristic of neighbourhood search is its generality: no assumption is made about the objective and the constraints, whereas other heuristic methods depend on the particular problem at hand. Neighbourhood search is, to say the least, mathematically unexciting, and for many problems specific heuristic algorithms exist with better performance. However, from a practical point of view, the ease of conception and implementation of a neighbourhood search algorithm make it a most interesting candidate for the quick prototyping of optimization software for many domains, including manufacturing. These characteristics have justified the continuous interest in neighbourhood search. Some algorithms have been proposed to overcome the greatest shortcoming of neighbourhood search, i.e. the tendency to get stuck in a local minimum. In this paper the two most interesting neighbourhood search-based algorithms, simulated annealing and tabu search, are presented and evaluated by comparing them with an exact algorithm for a simple scheduling problem. Due to the complexity of optimization problems encountered in the CIM world, the practitioner will find these algorithms a most useful tool.