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Information Diffusion Models and Fuzzing Algorithms for a Privacy-Aware Data Transmission Scheduling in 6G Heterogeneous ad hoc Networks 认领 引用 被引量：1
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作者 Borja Bordel Sánchez Ramón Alcarria Tomás Robles 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1214-1234,共21页
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h... In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services. 展开更多
关键词 6G networks ad hoc networks privacy scheduling algorithms diffusion models fuzzing algorithms
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A Workflow Scheduling Method Based on the Combination of Tunicate Swarm Algorithm and Highest Response Ratio Next Scheduling 认领 引用
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作者 Yujie Tian Ming Zhu +2 位作者 Jing Li Cong Liu Ziyang Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1950-1963,共14页
Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with ... Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with the Highest Response Ratio Next policy.The Tunicate Swarm Algorithm generates a cost-minimizing task-to-VM mapping scheme,while the Highest Response Ratio Next dynamically dispatches tasks in the ready queue with the highest-priority.Experimental results demonstrate that the Tunicate Swarm-Highest Response RatioNext reduces costs by up to 94.8%compared to meta-heuristic baselines.It also achieves competitive cost efficiency vs.a learning-based method while offering superior operational simplicity and efficiency,establishing it as a highly practical solution for dynamic cloud environments. 展开更多
关键词 Workflow scheduling cloud computing tunicate swarm algorithm highest response ratio next scheduling
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Hyper-heuristic Evolutionary Algorithm Utilizing Q-learning for Addressing the Distributed Flexible Job-Shop Scheduling Problem in the Context of Worker Absenteeism 认领 引用
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作者 Zhiqing Li Yongquan Zhou Qifang Luo 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1932-1969,共38页
The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This... The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This study extends the DFJSPW model to introduce an enhanced framework,DFJSPWA,which optimizes maximum makespan,worker workload,and total energy consumption by integrating worker load factors with random absenteeism.To solve this complex problem,we propose a Q-learning-based Hyper-heuristic Evolutionary Algorithm(QLHHEA).This algorithm features a segmented encoding scheme that implicitly captures absenteeism information,utilizing a decoding process tailored for both standard and rescheduling contexts.Additionally,we construct a pool of twelve efficient Low-Level Heuristics(LLHs)combined with Q-learning to enable the adaptive selection of operators.Furthermore,a Hybrid Rescheduling Method(HRM)is developed,employing three response strategies based on worker status and the urgency of the absenteeism.Comprehensive experiments on 58 benchmark instances demonstrate that QLHHEA significantly outperforms six established algorithms,including MOEA/D and NSGA-II.Statistical validation confirms the superiority of the proposed method.This research provides a robust theoretical and methodological framework for improving scheduling efficiency and resource utilization in distributed production systems facing worker absenteeism. 展开更多
关键词 Worker absenteeism Distributed flexible job shop scheduling problem Hyper-heuristic evolutionary algorithm Dynamic rescheduling Q-learning
An Adaptive Imperialist Competitive Algorithm with Cooperation for Flexible Jobshop and Parallel Batch Processing Machine Scheduling 认领 引用
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作者 Jie Wang Deming Lei 《Computers, Materials & Continua》 SCIE EI 2026年第6期1934-1960,共27页
Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered;however,the flexible jobshop and parallel batch processing machine scheduling problem(FJPBPMSP)is prev... Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered;however,the flexible jobshop and parallel batch processing machine scheduling problem(FJPBPMSP)is prevalent in real-life manufacturing processes and is seldom investigated.In this study,FJPBPMSP is examined,where flexible processing and batch processing are performed sequentially.An adaptive imperialist competitive algorithm with cooperation(CAICA)is proposed to minimize makespan and total energy consumption simultaneously.In CAICA,a four-string representation is adopted,and initial empires with novel structures are formed by uniformly dividing the population.An adaptive assimilation and revolution are designed.An adaptive assimilation and revolution are designed.An adaptive imperialist competition with cooperation is provided.Search strategies,imperialists,and colonies are also renewed by new procedures.Computational experiments are conducted on 50 instances.The computational results show that the new strategies of CAICA are effective,and CAICA can provide better results than its comparative algorithms in solving FJPBPMSP. 展开更多
关键词 Flexible jobshop scheduling parallel batch processing scheduling imperialist competition algorithm cooperation
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Precedence Criteria and Gradient-Based Scheduling Algorithm for the Airplane Refueling Problem 认领 引用
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作者 LIN Hao HE Cheng 《Chinese Quarterly Journal of Mathematics》 2026年第1期38-49,共12页
The airplane refueling problem can be stated as follows.We are given n airplanes which can refuel one another during the flight.Each airplane has a reservoir volume wj(liters)and a consumption rate pj(liters per kilom... The airplane refueling problem can be stated as follows.We are given n airplanes which can refuel one another during the flight.Each airplane has a reservoir volume wj(liters)and a consumption rate pj(liters per kilometer).As soon as one airplane runs out of fuel,it is dropping out of the flight.The problem asks for finding a refueling scheme such that the last plane in the air reach a maximal distance.An equivalent version is the n-vehicle exploration problem.The computational complexity of this non-linear combinatorial optimization problem is open so far.This paper employs the neighborhood exchange method of single-machine scheduling to study the precedence relations of jobs,so as to improve the necessary and sufficiency conditions of optimal solutions,and establish an efficient heuristic algorithm which is a generalization of several existing special algorithms. 展开更多
关键词 Combinatorial optimization Scheduling method The airplane refueling problem Optimality criteria Heuristic algorithm
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Integrated scheduling optimization of carrier-based aircraft deck operation for comprehensive efficiency 认领 引用 被引量：1
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作者 Wei Han Haonan Wu +2 位作者 Fang Guo Liangliang Cheng Xichao Su 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第8期190-215,共26页
An aircraft carrier's formidable combat capability relies on sorties,with efficient deck operations as the core to enhance sortie and recovery capabilities.First,this study develops an integrated mathematical mode... An aircraft carrier's formidable combat capability relies on sorties,with efficient deck operations as the core to enhance sortie and recovery capabilities.First,this study develops an integrated mathematical model for carrier-based aircraft deployment,sortie,and maintenance scheduling to assist decision-makers in formulating sorties and coordinating resource allocation(e.g.,personnel,equipment),aiming to optimize the comprehensive effectiveness index of integrated carrier-based aircraft scheduling.Second,a hybrid multi-layer coded genetic algorithm(HMCGA)integrated with heuristic rules is proposed;it uses four-layer coding to resolve inter-sub-process coupling and supports integrated carrier-based aircraft scheduling,covering hangar transfer,deck transfer,aircraft maintenance support,and sortie execution.Then,case simulation shows the proposed model and algorithm effectively improve operational effectiveness and ensure accuracy.Finally,comparisons of 50 independent simulation results(PSO,DE,WOA,CPLEX)effectively validate HMCGA's superiority and universality in solving integrated carrier-based aircraft scheduling problems across fleet scales,further confirming the model and algorithm's reliability. 展开更多
关键词 Comprehensive efficiency Hybrid multi-layer coding genetic algorithm Scheduling optimization Integrated scheduling
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LLM‑assisted adaptive large neighborhood search for agile earth observation satellite scheduling 认领 引用
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作者 Feiran WANG Jiawei CHEN +4 位作者 Yonghao DU Yanjie SONG Yingwu CHEN Rammohan MALLIPEDDI Witold PEDRYCZ 《ENGINEERING Management》 CSCD 2026年第1期213-239,共27页
The Agile Earth Observation Satellite Scheduling Problem(AEOSSP)is a complex NP‑hard challenge that involves selecting,sequencing,and timing observation tasks to maximize imaging profits while adhering to various cons... The Agile Earth Observation Satellite Scheduling Problem(AEOSSP)is a complex NP‑hard challenge that involves selecting,sequencing,and timing observation tasks to maximize imaging profits while adhering to various constraints.In our study,we developed a mixed‑integer programming model for AEOSSP,incorporating key constraints related to visible time windows and time dependencies.To tackle this,we propose an Evolutionary Adaptive Large Neighborhood Search Algorithm(evALNS)enhanced by Large Language Models(LLMs).Our work pioneers the application of LLMs to ALNS by being the first to automatically develop and evolve its critical destroy heuristics.However,a naive application of LLMs is insufficient for such a complex domain.We therefore introduce a novel Dual‑Population Co‑Evolutionary Computing Framework(DPEC)to bridge the LLM’s knowledge gap by synergizing LLM‑generated heuristics with expert‑designed ones.This co‑evolution,guided by a Functional Natural Language Embedding(FNLE)strategy and customized prompts,significantly enhances the adaptability and efficiency of ALNS.Extensive numerical experiments demonstrated the superiority of the evALNS evolved under our framework,achieving an average profit improvement of 8.48%compared to the original ALNS with expert‑designed destroy operators. 展开更多
关键词 large language model algorithm design adaptive large neighborhood search satellite scheduling
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Optimal scheduling of active distribution networks based on multi-scenario fuzzy set based charging station resource prediction 认领 引用
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作者 Zhang Maosong Zhang Chunyu +3 位作者 Hao Shi Yang Jie Yang Lingxiao Wang Xiuqin 《High Technology Letters》 EI CAS 2026年第1期97-108,共12页
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po... With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy. 展开更多
关键词 charging station resource prediction subtractive optimizer algorithm multi-scenario fuzzy set two-stage optimal scheduling distribution network cost optimization
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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm 认领 引用 被引量：2
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作者 Jeng-Shyang Pan Na Yu +3 位作者 Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada 《Computers, Materials & Continua》 SCIE EI 2025年第2期2495-2520,共26页
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource... The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment. 展开更多
关键词 Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy
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A Shufled Frog-Leaping Algorithm with Competition for Parallel Batch Processing Machines Scheduling in Fabric Dyeing Process 认领 引用 被引量：1
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作者 Mingbo Li Deming Lei 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第5期1789-1808,共20页
As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that a... As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that at least one machine is not eligible for at least one job.PBPMSP and scheduling problems with machine eligibility are frequently considered;however,PBPMSP with machine eligibility is seldom explored.This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition(CSFLA)to minimize makespan.In CSFLA,the initial population is produced in a heuristic and random way,and the competitive search of memeplexes comprises two phases.Competition between any two memeplexes is done in the first phase,then iteration times are adjusted based on competition,and search strategies are adjusted adaptively based on the evolution quality of memeplexes in the second phase.An adaptive population shuffling is given.Computational experiments are conducted on 100 instances.The computational results showed that the new strategies of CSFLA are effective and that CSFLA has promising advantages in solving the considered PBPMSP. 展开更多
关键词 Batch processing machines shuffled frog-leaping algorithm competition parallel machines scheduling
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Multi-strategy Enhanced Hiking Optimization Algorithm for Task Scheduling in the Cloud Environment 认领 引用
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作者 Libang Wu Shaobo Li +2 位作者 Fengbin Wu Rongxiang Xie Panliang Yuan 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第3期1506-1534,共29页
Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been prop... Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission. 展开更多
关键词 Task scheduling Chebyshev chaos Hybrid speed update strategy Metaheuristic algorithms The Hiking Optimization Algorithm(HOA)
An Adaptive Firefly Algorithm for Dependent Task Scheduling in IoT-Fog Computing 认领 引用
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作者 Adil Yousif 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2869-2892,共24页
The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation ... The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads. 展开更多
关键词 Fog computing scheduling resource management firefly algorithm genetic algorithm ant colony optimization
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Modeling and Optimization of Diffusion Process Scheduling under Strict Queue Time Constraints in Semiconductor Manufacturing 认领 引用
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作者 Liangchao Chen Yan Qiao +3 位作者 Siwei Zhang Bin Liu Yonghua Shao Sijun Zhan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期488-519,共32页
This article examines wafer lots scheduling in the diffusion area in semiconductor manufacturing.The diffusion area comprises multiple tool groups.Each of them contains non-identical semiconductor tools.All tools can ... This article examines wafer lots scheduling in the diffusion area in semiconductor manufacturing.The diffusion area comprises multiple tool groups.Each of them contains non-identical semiconductor tools.All tools can process multiple wafer lots simultaneously,and wafer lots processed together in a tool are called a wafer batch.Besides,each wafer lot has specific queue time limits(QTLs)between consecutive processing operations,making the scheduling problem more complicated.To solve it,a discrete backtracking search optimization algorithm(DBSA)is designed for optimizing both wafer lot assignments and wafer batch processing sequences.Once the processing sequence of wafer batches at each tool is determined,a linear program(LP)is built to obtain optimal starting and completion time points of wafer batches while satisfying QTLs.If a schedule is examined to have no feasible solution by the LP,a proposed approach is used to regroup wafer lots to form wafer batches and adjust their processing sequences to potentially make it feasible.Extensive experiments show that DBSA reliably produces feasible schedules and outperforms GA,MixPSO,and GWO,with up to 17.75%,19.19%,and 9.21%reductions in average cycle time,respectively,demonstrating its superiority in both solution quality and practical applicability. 展开更多
关键词 Metaheuristic algorithms queue time limits scheduling semiconductor manufacturing
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On-orbit refueling robust mission scheduling with uncertain duration for geosynchronous orbit spacecraft 认领 引用
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作者 Shuai YIN Chuanjiang LI +3 位作者 Edoardo FADDA Yanning GUO Guangtao RAN Paolo BRANDIMARTE 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期410-424,共15页
With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on pre... With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on predetermined refueling duration;however,the precise mission scheduling solution will be difficult to apply due to uncertain refueling duration caused by orbital transfer deviations and stochastic actuator faults during actual on-orbit service.Therefore,this paper proposes a robust mission scheduling strategy for geosynchronous orbit spacecraft on-orbit refueling missions with uncertain refueling duration.Firstly,a robust mission scheduling model is constructed by introducing the budget uncertainty set to describe the uncertain refueling duration.Secondly,a hybrid harris hawks optimization algorithm is designed to explore the optimal mission allocation and refueling sequences,which combines cubic chaotic mapping to initialize the population,and the crossover in the genetic algorithm is introduced to enhance global convergence.Finally,the typical simulation examples are constructed with real-mission scenarios in three aspects to analyze:performance comparisons with various algorithms;robustness analyses via comparisons of different on-orbit refueling durations;investigations into the impacts of different initial population strategies on algorithm performance,demonstrating the proposed mission scheduling framework's robustness and effectiveness by comparing it with the exact mission scheduling. 展开更多
关键词 Geosynchronous orbit(GEO) Hybrid Harris Hawks Optimization algorithm(HHHO) Mission scheduling On-orbit refueling Robust optimization
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Unrelated Parallel-Machine Scheduling Problem with Time-Changing Effects and Dynamic Job Arrivals 认领 引用
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作者 GUAN Zhicheng ZHANG Xinying CHEN Lu 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期1057-1070,I0022,共14页
With machine deterioration,time-changing effects are observed in the ion implantation work center of wafer fabrication.Furthermore,jobs arrive in a dynamic pattern.A mixed integer stochastic programming model is formu... With machine deterioration,time-changing effects are observed in the ion implantation work center of wafer fabrication.Furthermore,jobs arrive in a dynamic pattern.A mixed integer stochastic programming model is formulated to address the unrelated parallel-machine scheduling problem at the work center.The objective is to minimize the average flow time of wafers.Time-changing effects and dynamic job arrivals are considered simultaneously.A genetic algorithm with a reinforcement learning procedure(GA-RL)is developed to solve real-size problems.The reinforcement learning procedure aims to determine the optimal confidence levels during the search for the best solution.Computational analyses demonstrate the efficiency of the GA-RL.Sensitivity analyses provide valuable managerial insights into actual production processes. 展开更多
关键词 dynamic job arrivals parallel-machine scheduling time-changing effects genetic algorithm reinforcement learning
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Multi-Level Subpopulation-Based Particle Swarm Optimization Algorithm for Hybrid Flow Shop Scheduling Problem with Limited Buffers 认领 引用 被引量：1
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作者 Yuan Zou Chao Lu +1 位作者 Lvjiang Yin Xiaoyu Wen 《Computers, Materials & Continua》 SCIE EI 2025年第8期2305-2330,共26页
The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on th... The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on the hybrid flow shop scheduling problem with limited buffers(LBHFSP).This paper deeply investigates the LBHFSP to optimize the goal of the total completion time.To better solve the LBHFSP,a multi-level subpopulation-based particle swarm optimization algorithm(MLPSO)is proposed,which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO(particle swarm optimization)algorithm.In MLPSO,firstly,considering the impact of the limited buffers on the process of subsequent operations,a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers.Secondly,an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population.Afterward,a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability.Additionally,a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability.Lastly,numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years.The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP. 展开更多
关键词 Hybrid flow shop scheduling problem limited buffers PSO algorithm collaborative search blocking phenomenon
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Scheduling and heat integration of multi-product plant based on genetic algorithm 认领 引用
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作者 Ke Li Lingqi Kong +1 位作者 Xinping Wang Mengyu Liu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第11期115-128,共14页
The research on scheduling and heat integration of batch process plays an important role in reducing energy consumption,improving production efficiency and enhancing the competitiveness of industries.The complexity an... The research on scheduling and heat integration of batch process plays an important role in reducing energy consumption,improving production efficiency and enhancing the competitiveness of industries.The complexity and difficulty of the model solving are increased due to the comprehensive consideration of both scheduling and heat integration.In this paper,the mixed integer nonlinear programming(MINLP) mathematical model of multi-product plant heat integration optimization with the goal of energy-saving annual profit(EAP) is established.The simultaneous optimization and sequential optimization are carried out respectively by bi-level programming(BP) based on the genetic algorithm(GA),and the calculation results are compared.EAP better captures the trade-off relationship between scheduling schemes,energy-saving profits,and equipment costs.The bi-level programming approach based on GA categorizes variables into integer and real types,enabling structural optimization and parameter optimization of the heat exchanger network.This,in turn,enhances solution efficiency and overcomes the limitations of conventional optimization algorithms in terms of solution speed and quality.Two examples show that the EAP of indirect heat integration considering the storage tank are 21% and 2% higher than that of the direct heat integration,and EAP of the simultaneous optimization are26% and 6% higher than that of the sequential optimization.The example demonstrates that the model and algorithm are applicable to batch multi-product plants,such as those in the chemical,pharmaceutical,and food industries,and possess strong practicality and innovation. 展开更多
关键词 Multi-product plant Heat integration Scheduling Genetic algorithm Heat exchanger network Bi-level programming
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A Traffic Scheduling Strategy in SDN Data Center Based on Fibonacci Tree Optimization Algorithm 认领 引用
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作者 Wang Yaomin Hu Ping +3 位作者 Zeng Jing Li Donghong Yuan Lu Long Hua 《China Communications》 SCIE EI CSCD 2025年第11期176-191,共16页
To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in t... To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in the operator data center.Fibonacci tree optimization algorithm(FTO)is embedded into the analysis prediction and the online scheduling stages,the FTO traffic scheduling strategy is proposed.By taking the global optimal and the multi-modal optimization advantage of FTO,the traffic scheduling optimal solution and many suboptimal solutions can be obtained.The experiment results show that the FTO traffic scheduling strategy can schedule traffic in data center networks reasonably,and improve the load balancing in the operator data center network effectively. 展开更多
关键词 Fibonacci tree optimization algorithm(FTO) multi-modal optimization SDN data center traffic scheduling
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An Adaptive Cooperated Shuffled Frog-Leaping Algorithm for Parallel Batch Processing Machines Scheduling in Fabric Dyeing Processes 认领 引用
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作者 Lianqiang Wu Deming Lei Yutong Cai 《Computers, Materials & Continua》 SCIE EI 2025年第5期1771-1789,共19页
Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing ... Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility. 展开更多
关键词 Batch processing machine parallel machine scheduling shuffled frog-leaping algorithm fabric dyeing process machine eligibility
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A Q-Learning-Assisted Co-Evolutionary Algorithm for Distributed Assembly Flexible Job Shop Scheduling Problems 认领 引用
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作者 Song Gao Shixin Liu 《Computers, Materials & Continua》 SCIE EI 2025年第6期5623-5641,共19页
With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research s... With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research studies a distributed flexible job shop scheduling problem with assembly operations.Firstly,a mixed integer programming model is formulated to minimize the maximum completion time.Secondly,a Q-learning-assisted coevolutionary algorithmis presented to solve themodel:(1)Multiple populations are developed to seek required decisions simultaneously;(2)An encoding and decoding method based on problem features is applied to represent individuals;(3)A hybrid approach of heuristic rules and random methods is employed to acquire a high-quality population;(4)Three evolutionary strategies having crossover and mutation methods are adopted to enhance exploration capabilities;(5)Three neighborhood structures based on problem features are constructed,and a Q-learning-based iterative local search method is devised to improve exploitation abilities.The Q-learning approach is applied to intelligently select better neighborhood structures.Finally,a group of instances is constructed to perform comparison experiments.The effectiveness of the Q-learning approach is verified by comparing the developed algorithm with its variant without the Q-learning method.Three renowned meta-heuristic algorithms are used in comparison with the developed algorithm.The comparison results demonstrate that the designed method exhibits better performance in coping with the formulated problem. 展开更多
关键词 Distributed manufacturing flexible job shop scheduling problem assembly operation co-evolutionary algorithm Q-learning method
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