Self-encoding Barnacle Mating Optimizer Algorithm for Manpower Scheduling in Flow Shop

TL;DR

This paper introduces the Self-encoding Barnacle Mating Optimizer (SBMO), a novel algorithm designed to efficiently solve the Flow Shop Manpower Scheduling Problem by considering worker proficiency, with demonstrated superior performance.

Contribution

The paper proposes a new algorithm, SBMO, combining encoding, crossover, mutation, and neighborhood search to address FSMSP, a problem considering worker proficiency in flow shop scheduling.

Findings

SBMO outperforms classic algorithms in approximate ratio.

SBMO demonstrates high stability and efficiency.

Extensive simulations confirm the effectiveness of SBMO.

Abstract

Flow Shop Scheduling (FSS) has been widely researched due to its application in many types of fields, while the human participant brings great challenges to this problem. Manpower scheduling captures attention for assigning workers with diverse proficiency to the appropriate stages, which is of great significance to production efficiency. In this paper, we present a novel algorithm called Self-encoding Barnacle Mating Optimizer (SBMO), which solves the FSS problem considering worker proficiency, defined as a new problem, Flow Shop Manpower Scheduling Problem (FSMSP). The highlight of the SBMO algorithm is the combination with the encoding method, crossover and mutation operators. Moreover, in order to solve the local optimum problem, we design a neighborhood search scheme. Finally, the extensive comparison simulations are conducted to demonstrate the superiority of the proposed SBMO. The results indicate the effectiveness of SBMO in approximate ratio, powerful stability, and execution time, compared with the classic and popular counterparts.