Hybrid Cooperative Co-Evolution Algorithm for Deadlock-prone Distributed Assembly Flowshop Scheduling with Limited buffers Using Petri nets

TL;DR

This paper introduces a hybrid cooperative co-evolution algorithm enhanced with Petri net analysis to effectively solve deadlock-prone distributed assembly flowshop scheduling problems with limited buffers, minimizing makespan.

Contribution

It presents a novel Petri net-based deadlock analysis and an integrated HCCE algorithm with multiple mechanisms for improved scheduling in manufacturing environments.

Findings

HCCE effectively avoids deadlocks in DAFSP.

HCCE outperforms existing methods in minimizing makespan.

The Petri net-based approach accurately detects and amends deadlocks.

Abstract

The distributed assembly flowshop scheduling problem (DAFSP) can be applied to immense manufacturing environments. In DAFSP, jobs are first processed in distributed flowshops, and then assembled into final products by an assembly machine, which usually has limited buffers in practical application. This limited capacity can lead to deadlocks, halting job completion and blocking the entire manufacturing process. However, existing scheduling methods fail to address these deadlocks in DAFSP effectively. As such, we develop a hybrid cooperative co-evolution (HCCE) algorithm for solving the deadlock-prone DAFSP by minimizing the makespan. For the first time, we use Petri nets to analyze the deadlocks in DAFSP and propose a Petri net-based deadlock amending method (IDAM), which is further integrated into HCCE to ensure the feasibility (i.e., deadlock-freeness) of solutions. Importantly, HCCE contains an elite archive (EAR) and two subpopulations. It uses the problem-specific operators for heuristic initialization and global-search. To enhance the quality and diversity of solutions, an information transfer mechanism (ITM) is developed among subpopulation and EAR, and four local-search operators are performed sequentially on each individual in EAR. Finally, comprehensive experiments demonstrate the effectiveness and superiority of the proposed HCCE algorithm.