Swarm Intelligent Algorithm For Re-entrant Hybrid Flow shop Scheduling Problems

TL;DR

This paper introduces a novel dynamic wolf pack algorithm based on Levy flight to optimize re-entrant hybrid flowshop scheduling, demonstrating effective solutions for complex manufacturing scheduling problems.

Contribution

It proposes LDWPA, a new hybrid algorithm combining Levy flight and dynamic regeneration to improve search efficiency and solution quality in RHFS scheduling.

Findings

LDWPA effectively solves RHFS scheduling problems.

The algorithm enhances convergence speed and solution diversity.

Results outperform traditional methods in the case study.

Abstract

In order to solve Re-entrant Hybrid Flowshop (RHFS) scheduling problems and establish simulations and processing models, this paper uses Wolf Pack Algorithm (WPA) as global optimization. For local assignment, it takes minimum remaining time rule. Scouting behaviors of wolf are changed in former optimization by means of levy flight, extending searching ranges and increasing rapidity of convergence. When it comes to local extremum of WPA, dynamic regenerating individuals with high similarity adds diversity. Hanming distance is used to judge individual similarity for increased quality of individuals, enhanced search performance of the algorithm in solution space and promoted evolutionary vitality.A painting workshop in a bus manufacture enterprise owns typical features of re-entrant hybrid flowshop. Regarding it as the algorithm applied target, this paper focus on resolving this problem with LDWPA (Dynamic wolf pack algorithm based on Levy Flight). Results show that LDWPA can solve re-entrant hybrid flowshop scheduling problems effectively.