Research on Limited Buffer Scheduling Problems in Flexible Flow Shops with Setup Times

TL;DR

This paper introduces an improved whale optimization algorithm (IWOA) tailored for solving limited buffer scheduling problems in flexible flow shops with setup times, demonstrating superior performance over existing algorithms through extensive simulations.

Contribution

The paper develops a novel IWOA algorithm incorporating Levy flight, opposition-based learning, and simulated annealing to enhance global search capabilities in flexible flow shop scheduling.

Findings

IWOA outperforms ICA, BA, and standard WOA in solving scheduling problems.

Simulation results confirm IWOA's superior optimization ability.

The approach effectively handles practical buffer and setup time constraints.

Abstract

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.