Solving Flow-Shop Scheduling Problems with Random Machine Breakdown and Limited Buffer Using a Pigeon-Inspired Hybrid Artificial Bee Colony Algorithm
TL;DR
This study hybridises the recently developed Pigeon-Inspired Optimisation Algorithm (PIOA) with the artificial bee colony (ABC) algorithm, and proves that the hybridisation of metaheuristics would improve the solution quality.
Abstract
A hybrid algorithm combining two metaheuristics is proposed to solve the flow-shop scheduling problem, aiming to minimise the makespan (Cmax). This approach accounts for random machine failures and limited buffer capacity between machines. Since flow-shop scheduling problems are NP-hard, the metaheuristics could be used to solve them effectively. Researchers proved that the hybridisation of metaheuristics would improve the solution quality. Therefore, this study hybridises the recently developed Pigeon-Inspired Optimisation Algorithm (PIOA) with the artificial bee colony (ABC) algorithm. The initial solutions are generated using a dynamic generation technique that relies on a set of constructive heuristics. The optimal solutions from the PIOA serve as input for the ABC algorithm. Various local search and variable neighbourhood search methods are also included to enhance solution quality. Extensive computational experiments, which focus on industrial scheduling scenarios and benchmark problem instances, are conducted to test the performance of the hybrid algorithm. Statistical analysis shows that the proposed algorithm outperforms other algorithms found in the existing literature.