A Hybrid Metaheuristics Parameter Tuning Approach for Scheduling through Racing and Case-Based Reasoning

Abstract

In real manufacturing environments, scheduling can be defined as the problem of effectively and efficiently assigning tasks to specific resources. Metaheuristics are often used to obtain near-optimal solutions in an efficient way. The parameter tuning of metaheuristics allows flexibility and leads to robust results, but requires careful specifications. The a priori definition of parameter values is complex, depending on the problem instances and resources. This paper implements a novel approach to the automatic specification of metaheuristic parameters, for solving the scheduling problem. This novel approach incorporates two learning techniques, namely, racing and case-based reasoning (CBR), to provide the system with the ability to learn from previous cases. In order to evaluate the contributions of the proposed approach, a computational study was performed, focusing on comparing our results previous published results. All results were validated by analyzing the statistical significance, allowing us to conclude the statistically significant advantage of the use of the novel proposed approach.

A Self-Parametrization Framework for Meta-Heuristics

“…Taguchi experiments search for the control inputs that allow the meta-heuristic to withstand the uncontrollable variation of the noise inputs. Other parametrization by DOE techniques such as F-Race and Sequential Parameter Optimization (SPO), can be found in [51][52][53][54][55][56][57][58][59][60][61][62][63][64].…”