METAPHOR-LESS RAO ALGORITHMS FOR DISCRETE COMBINATORIAL FLOW SHOP SCHEDULING PROBLEM
Abstract - Rao algorithms are recently proposed population-based metaheuristics that have found success in a variety of engineering and scientific domains. Rao algorithms differ from other well-known heuristics in that they are not dependent on any one set of parameters or metaphors. For the first time in published works, the current research aims to apply Rao algorithms to the well-known complex combinatorial issue known as the permutation flow shop scheduling problem (PFSP). The goal is to minimize the makespan. The mechanisms underlying the initial solution generation, perturbation, and solution updates are described. Results of a few computations involving different combinations of tasks and devices are shown, demonstrating the viability of the suggested strategy.
Keywords - Rao algorithms, Permutation Flow Shop Scheduling, Makespan Minimization.