A mayfly optimization algorithm

Computers & Industrial Engineering, Journal Year: 2020, Volume and Issue: 145, P. 106559 - 106559

Published: May 25, 2020

Language: Английский

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Co-Evolution With Deep Reinforcement Learning for Energy-Aware Distributed Heterogeneous Flexible Job Shop Scheduling

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2023, Volume and Issue: 54(1), P. 201 - 211

Published: Sept. 6, 2023

Energy-aware distributed heterogeneous flexible job shop scheduling (DHFJS) problem is an extension of the traditional FJS, which harder to solve. This work aims minimize total energy consumption (TEC) and makespan for DHFJS. A deep $Q$ -networks-based co-evolution algorithm (DQCE) proposed solve this NP-hard problem, includes four parts: First, a new co-evolutionary framework proposed, allocates sufficient computation global searching executes local search surrounding elite solutions. Next, nine features-based operators are designed accelerate convergence. Moreover, -networks applied learn select best operator each solution. Furthermore, efficient heuristic method reduce TEC. Finally, 20 instances real-world case employed evaluate effectiveness DQCE. Experimental results indicate that DQCE outperforms six state-of-the-art algorithms

Language: Английский

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A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

International Journal of Production Research, Journal Year: 2020, Volume and Issue: 59(13), P. 3880 - 3899

Published: May 20, 2020

With the development of global and decentralised economies, distributed production emerges in large manufacturing firms. A model exists with hybrid flowshops. As an extension flowshop scheduling problem (HFSP), (DHFSP) sequence dependent setup times (SDST) is a new challenging project. The DHFSP involves three sub-problems: first one to allocate factory for each job; second determine job factory; third machine at stage. This paper presents position-based mathematical discrete artificial bee colony algorithm (DABC) DHFSP-SDST optimise makespan. proposed DABC employs two-level encoding ensure initiative scheduling. Decoding method combines earliest available completion time rule feasible schedules. also employ effective solutions update techniques: neighbourhood operators, many Critical Factory Swap enhance exploitation. 780 benchmarks total are generated. Extensive experiments carried out test performance DABC. Computational results statistical analyses validate that outperforms best performing literature.

Language: Английский

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An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem

Swarm and Evolutionary Computation, Journal Year: 2021, Volume and Issue: 69, P. 100992 - 100992

Published: Oct. 9, 2021

Language: Английский

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A multiobjective evolutionary algorithm based on decomposition for hybrid flowshop green scheduling problem

Computers & Industrial Engineering, Journal Year: 2019, Volume and Issue: 136, P. 325 - 344

Published: July 18, 2019

Language: Английский

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Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots

Swarm and Evolutionary Computation, Journal Year: 2019, Volume and Issue: 52, P. 100600 - 100600

Published: Nov. 5, 2019

Language: Английский

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A multi-start variable neighbourhood descent algorithm for hybrid flowshop rescheduling

Swarm and Evolutionary Computation, Journal Year: 2019, Volume and Issue: 45, P. 92 - 112

Published: Jan. 21, 2019

Language: Английский

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A discrete artificial bee colony algorithm for the distributed heterogeneous no-wait flowshop scheduling problem

Applied Soft Computing, Journal Year: 2020, Volume and Issue: 100, P. 106946 - 106946

Published: Nov. 30, 2020

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 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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