A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem

IEEE Transactions on Cybernetics, Journal Year: 2022, Volume and Issue: 53(5), P. 3337 - 3350

Published: Aug. 22, 2022

Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises. In this study, energy consumption is considered in distributed blocking flow shop scheduling problem (DBFSP). A hyperheuristic with Q -learning (HHQL) presented to address energy-efficient DBFSP (EEDBFSP). employed select an appropriate low-level heuristic (LLH) a predesigned LLH set according historical information fed back by LLH. An initialization method, considers both total tardiness (TTD) (TEC), proposed construct initial population. The ε -greedy introduced utilize learned knowledge while retaining certain degree of exploration process selecting acceleration operation job on critical path designed optimize TTD. deceleration noncritical TEC. statistical computational experimentation extensive benchmark testified that HHQL outperforms other comparison algorithm regarding efficiency significance solving EEDBFSP.

Language: Английский

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An Iterative Greedy Algorithm With Q-Learning Mechanism for the Multiobjective Distributed No-Idle Permutation Flowshop Scheduling

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2024, Volume and Issue: 54(5), P. 3207 - 3219

Published: Feb. 13, 2024

The distributed no-idle permutation flowshop scheduling problem (DNIPFSP) has widely existed in various manufacturing systems. makespan and total tardiness are optimized simultaneously considering the variety of scales problems with introducing an improved iterative greedy (IIG) algorithm. variable neighborhood descent (VND) algorithm is applied to local search method Two perturbation operators based on critical factory proposed as structure VND. In destruction phase, scale varies size problem. An insertion operator-based strategy sorts undeleted jobs after phase. $Q$ -learning mechanism for selecting weighting coefficients introduced obtain a relatively small objective value. Finally, tested benchmark suite compared other existing algorithms. experiments show that IIG obtained more satisfactory results.

Language: Английский

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A DQN-based memetic algorithm for energy-efficient job shop scheduling problem with integrated limited AGVs

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 87, P. 101544 - 101544

Published: March 28, 2024

Language: Английский

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Multiobjective Flexible Job-Shop Rescheduling With New Job Insertion and Machine Preventive Maintenance

IEEE Transactions on Cybernetics, Journal Year: 2022, Volume and Issue: 53(5), P. 3101 - 3113

Published: March 14, 2022

In the actual production, insertion of new job and machine preventive maintenance (PM) are very common phenomena. Under these situations, a flexible job-shop rescheduling problem (FJRP) with both PM is investigated. First, an imperfect (IPM) model established to determine optimal plan for each machine, optimality proven. Second, in order jointly optimize production scheduling planning, multiobjective optimization developed. Third, deal this model, improved nondominated sorting genetic algorithm III adaptive reference vector (NSGA-III/ARV) proposed, which hybrid initialization method designed obtain high-quality initial population critical-path-based local search (LS) mechanism constructed accelerate convergence speed algorithm. numerical simulation, effect parameter setting on NSGA-III/ARV investigated by Taguchi experimental design. After that, superiority operators overall performance proposed demonstrated. Next, comparison two IPM models carried out, verifies effectiveness model. Last but not least, we have analyzed impact different effects decisions integrated maintenance-production schemes.

Language: Английский

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A Pareto-Based Discrete Jaya Algorithm for Multiobjective Carbon-Efficient Distributed Blocking Flow Shop Scheduling Problem

IEEE Transactions on Industrial Informatics, Journal Year: 2022, Volume and Issue: 19(8), P. 8588 - 8599

Published: Nov. 9, 2022

Carbon peaking and carbon neutrality, which are significant strategies for national sustainable development, have attracted enormous attention from researchers in the manufacturing domain. A Pareto-based discrete Jaya algorithm (PDJaya) is proposed to solve carbon-efficient distributed blocking flow shop scheduling problem (CEDBFSP) with criteria of total tardiness emission this article. The mixed-integer linear programming model presented CEDBFSP. An effective constructive heuristic produced generate initial population. new individual generated by update mechanism PDJaya. self-adaptive operator local search strategy designed enhance exploitation capability critical-path-based saving introduced further reduce emissions. effectiveness each PDJaya verified compared state-of-the-art algorithms benchmark suite. numerical results demonstrate that efficient optimizer solving

Language: Английский

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A collaborative iterative greedy algorithm for the scheduling of distributed heterogeneous hybrid flow shop with blocking constraints

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 201, P. 117256 - 117256

Published: April 18, 2022

Language: Английский

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An effective iterative greedy algorithm for distributed blocking flowshop scheduling problem with balanced energy costs criterion

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 129, P. 109502 - 109502

Published: Aug. 20, 2022

Language: Английский

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Intelligent optimization under blocking constraints: A novel iterated greedy algorithm for the hybrid flow shop group scheduling problem

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 258, P. 109962 - 109962

Published: Oct. 5, 2022

Language: Английский

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Energy-Efficient Iterative Greedy Algorithm for the Distributed Hybrid Flow Shop Scheduling With Blocking Constraints

IEEE Transactions on Emerging Topics in Computational Intelligence, Journal Year: 2023, Volume and Issue: 7(5), P. 1442 - 1457

Published: May 8, 2023

With the global energy shortage, climate anomalies, environmental pollution becoming increasingly prominent, saving scheduling has attracted more and concern than before. This paper studies energy-efficient distributed hybrid flow-shop problem (DHFSP) with blocking constraints. Our aim is to find job sequence low consumption as much possible in a limited time. In this paper, we formulate mathematical model of DHFSP constraints propose an improved iterative greedy (IG) algorithm optimize sequence. proposed algorithm, first, problem-specific strategy presented, namely, search strategy, which can assign appropriate jobs factory minimize each processing factory. Next, new selection mechanism inspired by Q-learning provide strategic guidance for scheduling. provides historical experience different factories. Finally, five types local strategies are designed machines be scheduled. These further improve ability QIG reduce caused blocking. Simulation results statistical analysis on 90 test problems show that superior several high-performance algorithms convergence rate quality solution.

Language: Английский

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Intelligent optimization under the makespan constraint: Rapid evaluation mechanisms based on the critical machine for the distributed flowshop group scheduling problem

European Journal of Operational Research, Journal Year: 2023, Volume and Issue: 311(3), P. 816 - 832

Published: May 31, 2023

Language: Английский

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