Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology – Part II

Abstract

Purpose – This is the second part of a two-part paper. The purpose of this paper is to report the results on the application of the methods that use the Response Surface Methodology to investigate an evolutionary algorithm (EA) and memory-based approach referred to as McBAR – the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants. Design/methodology/approach – The methods applied in this paper are fully explained in the first part. They are utilized to investigate the performances (ability to determine solutions to problems) of techniques composed of McBAR and some EA-based techniques for solving some multi-objective dynamic resource-constrained project scheduling problems with a variable number of tasks. Findings – The main results include the following: first, some algorithmic components of McBAR are legitimate; second, the performance of McBAR is generally superior to those of the other techniques after increase in the number of tasks in each of the above-mentioned problems; and third, McBAR has the most resilient performance among the techniques against changes in the environment that set the problems. Originality/value – This paper is novel for investigating the enumerated results.