Component Analysis Based Approach to Support the Design of Meta-Heuristics for MLCLSP Providing Guidelines

Abstract

Today's supply chain are highly complex and globally set-up underlying a constant change with increasing speed. This has to be reflected by the planning processes and algorithms being utilized in the different stages of a supply chain. In the context of production planning, meta-heuristics are usually applied due to their ability to handle high complex problems. As a consequence, these algorithms require adaptation to the new scenario or even new solution approaches/strategies have to be devised. However, designing a meta-heuristic of good performance for a problem is a hard task, since it requires deep knowledge on the problem, as well as on the meta-heuristic side. Therefore, the existence of supporting guidelines for meta-heuristics might ease and speed-up the adaptation or design of these algorithms to better cope with the problem. In this paper, meta-heuristics are deconstructed into its components and an approach for component-based analysis is proposed to gain knowledge about their performance and how they perform the search. Based on the results of this analysis, guidelines can be devised. The proposed approach is applied for analyzing components of a good performing Genetic Algorithm (GA) for multi-level capacitated lot sizing problem (MLCLSP) and initial guidelines for the construction of GA in the domain of MLCLSP are generated.