Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109780 - 109780

Published: Oct. 18, 2024

Language: Английский

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Energy-efficient job shop scheduling problem with transport resources considering speed adjustable resources

International Journal of Production Research, Journal Year: 2023, Volume and Issue: 62(3), P. 867 - 890

Published: Feb. 21, 2023

This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable of two types, namely: machines where jobs are processed on and vehicles that around shop-floor. Therefore, being considered involves determining, simultaneously, processing each production operation, sequence operations for machine, allocation tasks to vehicles, travelling task empty loaded legs, vehicle. Among possible solutions, we interested in those providing trade-offs between makespan total energy consumption (Pareto solutions). To end, develop solve a bi-objective mixed-integer linear programming model. In addition, due complexity also propose multi-objective biased random key genetic algorithm simultaneously evolves several populations. The computational experiments performed have show it be effective efficient, even presence larger instances. Finally, provide extensive time trade-off analysis front) infer advantages general insights managers dealing such complex problem.

Language: Английский

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Knowledge-based multi-objective evolutionary algorithm for energy-efficient flexible job shop scheduling with mobile robot transportation

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102647 - 102647

Published: June 19, 2024

Language: Английский

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A comprehensive overview of industrial demand response status in Europe

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 203, P. 114797 - 114797

Published: July 31, 2024

Industrial demand response (IDR) will play a crucial role in shaping future electricity systems, as it is key element of just energy transition and industrial development. The aim this work to provide an overview the current status IDR holistic perspective. First, main benefits potential are reviewed, together with motivations challenges for sector. Most recent advances European markets regulations specific focus on applications explored. Then, different resources which currently available help industries participate implement programmes reviewed. In particular: 1) (possible) tools defining energy-aware scheduling planning manufacturing systems analysed; 2) aggregators (i.e. intermediaries between power markets) facilitating explicit examined; 3) importance digitalisation better services from industry highlighted, pointing out that digital twins, cyber-physical Internet Things sensors, robots, edge computing, artificial intelligence, big data promising technologies; 4) most related research projects Finally, analysed discussed how each those can address still preventing apply programmes.

Language: Английский

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A systematic literature review on energy-efficient hybrid flow shop scheduling

Cogent Engineering, Journal Year: 2023, Volume and Issue: 10(1)

Published: May 1, 2023

Scheduling problems should not only focus on minimizing completion time, but other performances, such as energy consumption, are urgent to investigate. Many companies and researchers competing develop various methods solve this problem. One problem that has received much attention lately is the energy-efficient Hybrid Flow Shop Problem (HFSP). The HFSP also popularly called flexible flow shop This research provides a comprehensive review of articles discuss scheduling determine trend gaps for future interests. uses systematic literature method. total reviewed in study 90 from January 2008 - December 2022. classified by year, country, journal/conferences, publisher, objective function, analysis results show continues increase. In addition, multi-objective dominating problems. presents gap directions.

Language: Английский

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A Bi-Learning Evolutionary Algorithm for Transportation-Constrained and Distributed Energy-Efficient Flexible Scheduling

IEEE Transactions on Evolutionary Computation, Journal Year: 2024, Volume and Issue: 29(1), P. 232 - 246

Published: Jan. 16, 2024

With the rise of globalization and environmental concerns, distributed scheduling energy-efficient have become crucial topics in informational manufacturing system. Additionally, growing consideration about realistic constraints, such as transportation time finite resources, has made problem increasingly complex. Facing these challenges, special mechanisms are required to improve efficiency solving algorithms. In this paper, a bi-learning evolutionary algorithm (BLEA) is proposed solve flexible job shop with constraints (DEFJSP-T). Firstly, we integrate statistical learning (SL) (EL) framework, while decomposition Pareto dominance methods employed different stages handle conflicting objectives. During SL stage, probability models established statistically search for advantageous substructures on each weight vector, an update mechanism devised exploration. EL genetic operators introduced improved local that takes into account properties realize sufficient exploitation. Finally, according performance SL, novel switching between designed ensure rational allocation computing resources. Extensive experiments conducted test performances BLEA. The comparison shows BLEA superior DEFJSP-T terms effectiveness.

Language: Английский

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Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

Integrated Computer-Aided Engineering, Journal Year: 2023, Volume and Issue: 30(2), P. 151 - 167

Published: Feb. 7, 2023

The flexible job shop is a well-known scheduling problem that has historically attracted much research attention both because of its computational complexity and importance in manufacturing engineering processes. Here we consider variant the where uncertainty operation processing times modeled using triangular fuzzy numbers. Our objective to minimize total energy consumption, which combines required by resources when they are actively an consumed these simply for being switched on. To solve this NP-Hard problem, propose memetic algorithm, hybrid metaheuristic method global search with local search. focus been on obtaining efficient method, capable similar solutions quality-wise state art reduced amount time. assess performance our present extensive experimental analysis compares it previous proposals evaluates effect different components.

Language: Английский

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Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning

Processes, Journal Year: 2024, Volume and Issue: 12(11), P. 2423 - 2423

Published: Nov. 2, 2024

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, impact dynamic disturbance events on production, need for energy-efficient integrated scheduling production equipment automated guided vehicles (AGVs) in a flexible job shop environment is investigated this study. Firstly, static model AGVs (ISPEA) developed based mixed-integer programming, which aims to optimize maximum completion time total energy consumption (EC). In recent years, reinforcement learning, including deep learning (DRL), has demonstrated significant advantages handling workshop issues with sequential decision-making characteristics, can fully utilize vast quantity historical data accumulated adjust plans timely manner changes conditions demand. Accordingly, DRL-based approach introduced address common disturbances emergency order insertions. Combined characteristics ISPEA problem an event-driven strategy events, four types agents, namely workpiece selection, machine AGV target selection are set up, refine status as observation inputs generate rules selecting workpieces, machines, AGVs, targets. These agents trained offline using QMIX multi-agent framework, utilized solve problem. Finally, effectiveness proposed method validated through comparison solution performance other typical optimization algorithms various cases.

Language: Английский

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A Deep Reinforcement Learning-Based Evolutionary Algorithm for Distributed Heterogeneous Green Hybrid Flowshop Scheduling

Processes, Journal Year: 2025, Volume and Issue: 13(3), P. 728 - 728

Published: March 3, 2025

Due to increasing energy consumption, green scheduling in the manufacturing industry has attracted great attention. In distributed involving heterogeneous plants, accounting for complex work sequences and consumption poses a major challenge. To address hybrid flowshop (DHGHFSP) while optimising total weighted delay (TWD) (TEC), deep reinforcement learning-based evolutionary algorithm (DRLBEA) is proposed this article. DRLBEA, problem-based heuristic initialization with random-sized population designed generate desirable initial solution. A bi-population global search local used obtain elite archive. Moreover, distributional Deep Q-Network (DQN) trained select best strategy. Experimental results on 20 instances show 9.8% increase HV mean value 35.6% IGD over state-of-the-art method. The effectiveness efficiency of DRLBEA solving DHGHFSP.

Language: Английский

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Optimizing Expected Working Time in Consecutive Sliding Window Systems

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 111126 - 111126

Published: April 1, 2025

Language: Английский

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