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 Reinforcement Learning Driven Cooperative Meta-Heuristic Algorithm for Energy-Efficient Distributed No-Wait Flow-Shop Scheduling With Sequence-Dependent Setup Time

IEEE Transactions on Industrial Informatics, Journal Year: 2022, Volume and Issue: 19(7), P. 8427 - 8440

Published: Nov. 4, 2022

Green manufacturing has attracted increasing attention under the background of carbon peaking and neutrality. Distributed production widely existed in various industries with development globalization. This article investigates an energy-efficient distributed no-wait flow-shop scheduling problem sequence-dependent setup time (DNWFSP-SDST) to minimization makespan total energy consumption (TEC). A mixed-integer linear programming model DNWFSP-SDST is constructed a cooperative meta-heuristic algorithm based on Q-learning (CMAQ) proposed address this article. In CMAQ, heuristic named RNRa generate initial solutions. bipopulation framework double designed further optimize According properties DNWFSP-SDST, energy-saving strategy knowledge improve TEC. The results experiments show that performance CMAQ superior certain state-of-the-art comparison algorithms solving DNWFSP-SDST.

Language: Английский

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A Cooperative Scatter Search With Reinforcement Learning Mechanism for the Distributed Permutation Flowshop Scheduling Problem With Sequence-Dependent Setup Times

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2023, Volume and Issue: 53(8), P. 4899 - 4911

Published: March 29, 2023

The integration of reinforcement learning technology into meta-heuristic algorithms to address complex combinatorial optimization problems has attracted much attention in recent years. A cooperative scatter search with $Q$ -learning mechanism (QCSS) is proposed for solving the DPFSP-SDST. In diversification generation method, two effective heuristic are designed construct an initial population high quality and diversity. improved eight domain knowledge-guided perturbation operators combined balance exploration exploitation capabilities QCSS algorithm. reference set (RefSet) divided subpopulations, adaptive competition adopted between subpopulations enhance efficiency. addition, a restart RefSet update phase ensure diversity solutions. performance algorithm verified on benchmark set, experimental results demonstrate robustness effectiveness

Language: Английский

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Ensemble meta-heuristics and Q-learning for solving unmanned surface vessels scheduling problems

Swarm and Evolutionary Computation, Journal Year: 2023, Volume and Issue: 82, P. 101358 - 101358

Published: July 7, 2023

Language: Английский

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Reliability-Aware Multi-Objective Memetic Algorithm for Workflow Scheduling Problem in Multi-Cloud System

IEEE Transactions on Parallel and Distributed Systems, Journal Year: 2023, Volume and Issue: 34(4), P. 1343 - 1361

Published: Feb. 23, 2023

With the development of cloud computing, multi-cloud systems have become common platforms for hosting and executing workflow applications in recent years. However, complexity scheduling increases exponentially because diversified billing mechanisms, heterogeneous virtual machines, reliability systems. This article focuses on a multi-objective problem (MOWSP-MCS). The makespan, cost, are considered optimization objectives from perspective users. Compared with classical environment, MOWSP-MCS allows users to apply backup technique improve reliability. To solve MOWSP-MCS, this proposes reliability-aware memetic algorithm (RA-MOMA) containing diversification strategy intensification strategy. In strategy, several problem-specific genetic operators introduced construct offspring individuals. four neighborhood designed based critical path resource utilization rate quality individuals archive set. A comprehensive numerical experiment is conducted evaluate effectiveness RA-MOMA. comparisons related algorithms demonstrate superiority RA-MOMA solving MOWSP-MCS.

Language: Английский

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Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 264, P. 110309 - 110309

Published: Jan. 20, 2023

Language: Английский

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Reinforcement Learning-Based Multiobjective Evolutionary Algorithm for Mixed-Model Multimanned Assembly Line Balancing Under Uncertain Demand

IEEE Transactions on Cybernetics, Journal Year: 2023, Volume and Issue: 54(5), P. 2914 - 2927

Published: Jan. 6, 2023

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers researchers configure line that increases production efficiency robustness. Hence, this work addresses cost-oriented mixed-model multimanned balancing under demand, presents a new robust mixed-integer linear programming model minimize the penalty costs simultaneously. addition, reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed tackle problem. The includes priority-based solution representation task-worker-sequence decoding considers robustness processing idle time reductions. Five crossover three mutation operators are proposed. $Q$ -learning-based strategy determines operator at each iteration effectively obtain Pareto sets of solutions. Finally, time-based probability-adaptive coordinate operators. experimental study, based on 269 benchmark instances, demonstrates proposal outperforms 11 competitive MOEAs previous single-objective approach managerial insights from results as well limitations also highlighted.

Language: Английский

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An end-to-end deep reinforcement learning method based on graph neural network for distributed job-shop scheduling problem

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121756 - 121756

Published: Sept. 27, 2023

Language: Английский

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Scheduling Eight-Phase Urban Traffic Light Problems via Ensemble Meta-Heuristics and Q-Learning Based Local Search

IEEE Transactions on Intelligent Transportation Systems, Journal Year: 2023, Volume and Issue: 24(12), P. 14415 - 14426

Published: Aug. 16, 2023

This paper addresses urban traffic light scheduling problems (UTLSP) with eight phases. The objective is to minimize the total vehicle delay time by assigning phases and phase-timing optimally. A novel hybrid algorithm framework combining meta-heuristics Q-learning proposed solve UTLSP for first time. First, a mathematical model developed describe UTLSP. Second, five are employed improved concerned problems. Based on feature of UTLSP, local search operators improve exploitation performance meta-heuristics. Third, two Q-learning-based ensemble strategies designed select premium during meta-heuristics' iterations. Finally, experiments conducted 10 cases different scales. 26 algorithms compared validation. Experimental results verify effectiveness strategies. Comparisons discussions show that water cycle strategy has best competitiveness solving considered

Language: Английский

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Scheduling Multiobjective Dynamic Surgery Problems via Q-Learning-Based Meta-Heuristics

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2024, Volume and Issue: 54(6), P. 3321 - 3333

Published: Feb. 21, 2024

This work addresses multiobjective dynamic surgery scheduling problems with considering uncertain setup time and processing time. When dealing them, researchers have to consider rescheduling due the arrivals of urgent patients. The goals are minimize fuzzy total medical cost, maximum completion time, maximize average patient satisfaction. First, we develop a mathematical model for describing addressed problems. is expressed by triangular numbers. Then, four meta-heuristics improved, eight variants developed, including artificial bee colony, genetic algorithm, teaching-learning-base optimization, imperialist competitive algorithm. For improving initial solutions' quality, two initialization strategies developed. Six local search proposed fine exploitation $Q$ -learning algorithm used choose suitable among them in iterative process meta-heuristics. states actions defined according characteristic Finally, algorithms tested 57 instances different scales. analysis discussions verify that improved colony most one all compared algorithms.

Language: Английский

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