Learning-based two-phase cooperative optimizer for distributed machine scheduling with heterogeneous factories and order priorities DOI Creative Commons
Tianyong Wu, Cong Luo, Youkou Dong

et al.

Egyptian Informatics Journal, Journal Year: 2023, Volume and Issue: 25, P. 100424 - 100424

Published: Dec. 8, 2023

In the realm of customized manufacturing, production cycles are often compressed to capture market opportunities swiftly. The blanking system stands as inaugural and pivotal phase in large equipment manufacturing. This study abstracts a novel problem from real-world systems, distributed unrelated parallel machine scheduling with heterogeneous factories order priorities (DUPMS-HP). presented work formulates bi-objective DUPMS-HP, aiming minimize both total weighted tardiness workload gap each machine. A learning-based two-phase cooperative optimizer (LCTPO) is introduced address this NP-hard problem, featuring: i) evolutionary algorithm during first stage for global search ensure diversity; ii) incorporation five problem-specific local strategies balance priority due date constraints. Additionally, reinforcement learning applied learn select best neighborhood operator elite solution, further enhancing diversity. effectiveness proposed validated through comparative analysis state-of-the-art algorithms on 20 instances. Experimental results affirm that LCTPO more adept at solving DUPMS-HP compared alternative algorithms.

Language: Английский

Combining meta-heuristics and Q-learning for scheduling lot-streaming hybrid flow shops with consistent sublots DOI

Benxue Lu,

Kaizhou Gao, Yaxian Ren

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 91, P. 101731 - 101731

Published: Sept. 11, 2024

Language: Английский

Citations

3
Research on computing task scheduling method for distributed heterogeneous parallel systems DOI Creative Commons

Xianzhi Cao,

Chong Chen, Shiwei Li

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 15, 2025

Abstract With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in achieving energy efficiency through to realize green computing. This paper investigates heterogeneous flow problem minimize system consumption under time constraints. First, set independent tasks capable computation on terminals, is performed according computational capabilities each terminal. The modeled as mixed-integer nonlinear programming using Directed Acyclic Graph input model. Then, dynamic method based heuristic reinforcement learning algorithms proposed schedule flows. Furthermore, redundancy applied certain reliability analysis enhance fault tolerance improve service quality. Experimental results show that our can achieve improvements, by 14.3% compared existing approaches two practical workflow instances.

Language: Английский

Citations

0
Collaborative scheduling of energy-saving spare parts manufacturing and equipment operation strategy using a self-adaptive two-stage memetic algorithm DOI
Qiang Luo, Qianwang Deng, Huining Zhuang

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2023, Volume and Issue: 87, P. 102707 - 102707

Published: Dec. 10, 2023

Language: Английский

Citations

6
Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling DOI Creative Commons

Fayong Zhang,

LI Cai-xian,

Rui Li

et al.

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(3), P. 3459 - 3471

Published: Feb. 10, 2024

Abstract This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative competitive multi-objective optimization method, which refer as LCCMO. We begin by presenting multi-rule initialization approach create population that combines strong convergence diversity. diverse forms foundation for our process. Next, develop multi-level global search strategy explores effective genes within solutions from different angles sub-problems. enhances optimal solutions. Moreover, design competition cooperation populations expedite convergence. encourages exchange of information ideas among populations, thereby accelerating progress. also multi-operator local technique, investigates elite various directions, leading improved In addition, integrate Q-learning into swarm optimizer explore regions objective space, enhancing diversity archive. guides selection operators small-size population, contributing more efficient optimization. evaluate effectiveness LCCMO, conduct numerical experiments 20 instances. The experimental results unequivocally demonstrate LCCMO outperforms six state-of-the-art algorithms. underscores potential learning knowledge-driven evolutionary framework in performance autonomy when it comes solving EADHWS.

Language: Английский

Citations

2
Learning-based two-phase cooperative optimizer for distributed machine scheduling with heterogeneous factories and order priorities DOI Creative Commons
Tianyong Wu, Cong Luo, Youkou Dong

et al.

Egyptian Informatics Journal, Journal Year: 2023, Volume and Issue: 25, P. 100424 - 100424

Published: Dec. 8, 2023

In the realm of customized manufacturing, production cycles are often compressed to capture market opportunities swiftly. The blanking system stands as inaugural and pivotal phase in large equipment manufacturing. This study abstracts a novel problem from real-world systems, distributed unrelated parallel machine scheduling with heterogeneous factories order priorities (DUPMS-HP). presented work formulates bi-objective DUPMS-HP, aiming minimize both total weighted tardiness workload gap each machine. A learning-based two-phase cooperative optimizer (LCTPO) is introduced address this NP-hard problem, featuring: i) evolutionary algorithm during first stage for global search ensure diversity; ii) incorporation five problem-specific local strategies balance priority due date constraints. Additionally, reinforcement learning applied learn select best neighborhood operator elite solution, further enhancing diversity. effectiveness proposed validated through comparative analysis state-of-the-art algorithms on 20 instances. Experimental results affirm that LCTPO more adept at solving DUPMS-HP compared alternative algorithms.

Language: Английский

Citations

1