A Q-learning-based improved multi-objective genetic algorithm for solving distributed heterogeneous assembly flexible job shop scheduling problems with transfers

Journal of Manufacturing Systems, Год журнала: 2025, Номер 79, С. 398 - 418

Опубликована: Фев. 8, 2025

Язык: Английский

Metaheuristics for multi-objective scheduling problems in industry 4.0 and 5.0: a state-of-the-arts survey

Frontiers in Industrial Engineering, Год журнала: 2025, Номер 3

Опубликована: Янв. 27, 2025

The advent of Industry 4.0 and the emerging 5.0 have fundamentally transformed manufacturing systems, introducing unprecedented levels complexity in production scheduling. This is further amplified by integration cyber-physical Internet Things, Artificial Intelligence, human-centric approaches, necessitating more sophisticated optimization methods. paper aims to provide a comprehensive perspective on application metaheuristic algorithms shop scheduling problems within context 5.0. Through systematic review recent literature (2015–2024), we analyze categorize various including Evolutionary Algorithms (EAs), swarm intelligence, hybrid methods, that been applied address complex challenges smart environments. We specifically examine how these handle multiple competing objectives such as makespan minimization, energy efficiency, costs, human-machine collaboration, which are crucial modern industrial settings. Our survey reveals several key findings: 1) metaheuristics demonstrate superior performance handling multi-objective compared standalone algorithms; 2) bio-inspired show promising results addressing environments; 3) tri-objective higher-order warrant in-depth exploration; 4) there an trend towards incorporating human factors sustainability optimization, aligned with principles. Additionally, identify research gaps propose future directions, particularly areas real-time adaptation, sustainability-aware algorithms. provides insights for researchers practitioners field scheduling, offering structured understanding current methodologies evolution from

Язык: Английский

An Optimized Method for Solving the Green Permutation Flow Shop Scheduling Problem Using a Combination of Deep Reinforcement Learning and Improved Genetic Algorithm

Mathematics, Год журнала: 2025, Номер 13(4), С. 545 - 545

Опубликована: Фев. 7, 2025

This paper tackles the green permutation flow shop scheduling problem (GPFSP) with goal of minimizing both maximum completion time and energy consumption. It introduces a novel hybrid approach that combines end-to-end deep reinforcement learning an improved genetic algorithm. Firstly, PFSP is modeled using (DRL) approach, named PFSP_NET, which designed based on characteristics PFSP, actor–critic algorithm employed to train model. Once trained, this model can quickly directly produce relatively high-quality solutions. Secondly, further enhance quality solutions, outputs from PFSP_NET are used as initial population for (IGA). Building upon traditional algorithm, IGA utilizes three crossover operators, four mutation incorporates hamming distance, effectively preventing prematurely converging local optimal Then, optimize consumption, energy-saving strategy proposed reasonably adjusts job order by shifting jobs backward without increasing time. Finally, extensive experimental validation conducted 120 test instances Taillard standard dataset. By comparing method algorithms such (SGA), elite (EGA), (HGA), discrete self-organizing migrating (DSOMA), water wave optimization (DWWO), monkey search (HMSA), results demonstrate effectiveness method. Optimal solutions achieved in 28 instances, latest updated Ta005 Ta068 values 1235 5101, respectively. Additionally, experiments 30 including 20-10, 50-10, 100-10, indicate reduce

Язык: Английский