A Knowledge-Driven Memetic Algorithm for Energy-Aware Distributed Assembly Blocking Permutation Flow Shop Scheduling Problem

With the growing emphasis on sustainable manufacturing and efficient resource utilization, the distributed assembly blocking permutation flow shop scheduling problem (DABPFSP) has garnered increasing attention for its complexity and practical significance. To address the dual objectives of minimizin...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access S. 1
Hauptverfasser: Wu, Shaoxing, Liu, Li
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
adaptive selection strategy
Assembly
Costs
distributed assembly blocking permutation flow shop scheduling
Energy consumption
energy-aware scheduling
Heuristic algorithms
Indexes
Job shop scheduling
Knowledge-driven memetic algorithm
Mathematical models
Memetics
Production facilities
Sequential analysis
ISSN:2169-3536, 2169-3536
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:With the growing emphasis on sustainable manufacturing and efficient resource utilization, the distributed assembly blocking permutation flow shop scheduling problem (DABPFSP) has garnered increasing attention for its complexity and practical significance. To address the dual objectives of minimizing makespan and total energy consumption, this study develops a Mixed-Integer Linear Programming (MILP) model and a Knowledge-Driven Memetic Algorithm (KDMA). The algorithm begins with a two-layer encoding scheme that simultaneously represents factory assignments, job processing sequences and processing speeds. To promote population diversity and search efficiency, three distinct initialization strategies are employed. During the evolutionary process, adaptive neighborhood selection strategy is integrated to enhance solution refinement. Furthermore, a customized energy saving operator is introduced to minimize redundant energy consumption. To comprehensively assess its performance, KDMA is tested on 54 instances that span various different combinations of jobs, machines, factories, and products. Comparative experiments with multiple state-of-the-art algorithms reveal that KDMA achieves superior performance in both solution quality and computational stability.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3635873