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A new customer-oriented multi-task scheduling model for cloud manufacturing considering available periods of services using an improved hyper-heuristic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Chen, Mengjiao Xu, Jiyuan Zhang, Wenyu Li, Zhenghui Zhejiang Univ Finance & Econ Sch Informat Technol & Artificial Intelligence Hangzhou 310018 Peoples R China

Efficiently assigning services to multiple tasks decomposed from manufacturing orders is a critical challenge in cloud manufacturing. While multi-task scheduling has gained attention, existing approaches often overlook key factors such as different expectations of customers for various non-functional attributes of services and limitations on available periods of services, which hinder effective and efficient service scheduling. This challenge is particularly pronounced in industries such as automotive and healthcare manufacturing, where timely service delivery and individualization are crucial. To address this, a new customer-oriented multi-task scheduling model is proposed, which incorporates service availability and uses soft constraints for customer expectations that are balanced by a penalty function to improve customer satisfaction. An improved hyper-heuristic algorithm with a new encoding and decoding scheme is developed, featuring three new low-level heuristics and a reinforcement learning-based high-level strategy to enhance scheduling performance. Comparison experiments using the Gurobi solver validate the model's solution accuracy on small-scale instances. Moreover, experiments against five baselines for moderate-scale and large-scale instances demonstrate that the proposed model improves solution quality by an average of 31.60% and reduces computational time by an average of 46.35%, highlighting its potential to optimize multi-task scheduling on cloud manufacturing platforms while meeting diverse customer requirements.

关键词： Cloud manufacturing Multi-task scheduling Customer satisfaction Available periods hyper-heuristic algorithm Reinforcement learning

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A Monte Carlo hyper-heuristic algorithm with low-level heuristics reward prediction for missile path planning

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JOURNAL OF SUPERCOMPUTING 2025年第2期81卷 1-39页

作者： Xu, Shuangfei Huang, Zhanjun Bi, Wenhao Zhang, An Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China Natl Key Lab Aircraft Configurat Design Xian 710072 Shaanxi Peoples R China

Missile path planning under multiple aircraft relay guidance is important in long-range air-to-ground strikes. Traditional meta-heuristic algorithms applied in path planning problems lack flexibility in the algorithm iteration process, and current hyper-heuristic (HH) algorithms have difficulty estimating the performance of low-level heuristics (LLHs) applied to the population in different states. This study proposes a Monte Carlo hyper-heuristic (MCHH) algorithm, which is adaptive to various path planning scenarios. The LLH set contains 18 LLHs generated from the basic operators in three meta-heuristic algorithms. The high-level strategy (HLS) evaluates the states of individuals and the reward of the LLH applied to each individual. A discrete state-action-reward table is used to predict the effectiveness of different LLHs and thus determine the optimal LLH applied in iterations. The table is trained through the MC method. The results of simulation cases and algorithm comparison demonstrate the efficiency and superiority of the MCHH algorithm.

关键词： hyper-heuristic algorithm Monte Carlo method Reward prediction Path planning

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Deep-Q-network-enhanced aquila-equilibrium hyper-heuristic algorithm for preventive maintenance integrated disassembly line balancing involving worker redeployment

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Huang, Yufan Zhou, Binghai Tongji Univ Inst Ind Engn Sch Mech Engn Caoan Rd 4800Kaiwu Hall A444 Shanghai 201804 Peoples R China

The increasing production and replacement rates of modern commodities demand efficient recovery of end-of-life (EOL) products. This study promotes green and sustainable remanufacturing by investigating semi-automated disassembly lines. Preventive maintenance (PM), rarely studied in disassembly lines, is integrated with the line balancing problem and worker redeployment scheduling to construct a stable and reliable disassembly process. Additionally, this study considers the diversity of disassembly robots in PM scheduling, including their operating speed, energy consumption, and maintenance requirements, to enhance line efficiency and reduce carbon emissions. A mixed-integer programming model is proposed for the Preventive Maintenance-integrated Semi-Automated Disassembly Line Balancing Problem (PM-SADLBP), and is verified by an exact Epsilon-Constraint method. To solve this NP-hard problem, a Deep-Q-Network-enhanced Aquila-Equilibrium hyper-heuristic algorithm (DN-AEHH) is developed. A Hybrid Adaptive-length Triple-layer Real Encoding Approach and Self-repairing Decoding Mechanism are tailored to create an effective mapping between continuous solution space and discrete balancing and scheduling plans. Numerical experiments demonstrate that DNAEHH outperforms five state-of-the-art algorithms across multiple problem scales, with a dominant rate of 83.01%. Additionally, managerial application shows 7.72% improvement in energy efficiency and 36.04% reduction in weighted cycle time with DN-AEHH to optimize PM. These findings provide practical guidance for line establishment and maintenance, supporting decision-making for managers with diverse preferences and operational contexts.

关键词： Disassembly line balancing Preventive maintenance Deep-Q-Learning hyper-heuristic algorithm Remanufacturing

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hyper-heuristic algorithm for traffic flow-based vehicle routing problem with simultaneous delivery and pickup

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第6期10卷 2271-2287页

作者： Wang, Zheng Liu, Jinlong Zhang, Jingling Hangzhou City Univ Sch Comp & Computat Sci 48 Huzhou St Hangzhou 310015 Zhejiang Peoples R China Zhejiang Univ Technol Key Lab Special Equipment Mfg & Adv Proc Technol Minist Educ 18 Chaowang Rd Hangzhou 310014 Zhejiang Peoples R China

To address the realistic problem of seriously reducing distribution efficiency and increasing distribution cost caused by road traffic congestion, this paper constructs a time-dependent speed describing vehicle travel speed and road traffic flow by simulating the change of urban traffic flow, to establish a vehicle route problem model considering traffic flow with distribution cost and customer satisfaction as optimization objectives. To solve this problem, a hyper-heuristic algorithm based on tabu search is designed in this paper, in which the underlying search operator is selected more efficiently by a high-level heuristic strategy. In addition, the correctness of the model and the effectiveness of the algorithm are verified by conducting simulation experiments on several benchmark sets. Experiment results are shown as the travel speed of the vehicle increases, the average customer satisfaction in lc1-type instances increases to 0.94. And the impact of urban traffic changes on logistics costs and customer satisfaction is further analyzed. Graphical Abstract hyper-heuristic algorithm for traffic flow-based VRP with simultaneous delivery and pickup

关键词： vehicle routing problem simultaneous delivery and pickup traffic flow tabu search hyper-heuristic algorithm

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hyper-heuristic algorithm based on ε-Greedy for Solving the Online Seru Scheduling Problem 36

Hyper-heuristic Algorithm based on ε-Greedy for Solving the...

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36th Chinese Control and Decision Conference (CCDC)

作者： Tu, Fan Liu, Yang Lou, Qi Liu, Fajun Yu, Yang Northeastern Univ Dept Intelligent Data & Syst Engn Shenyang Peoples R China Beijing Inst Comp Technol & Applicat Beijing Peoples R China Shenyang City Univ Shenyang Peoples R China Shanghai Sci & Technol Museum Shanghai Peoples R China Dalian Univ Technol Sch Econ & Management Dalian Peoples R China

ISBN: (纸本)9798350387780;9798350387797

We address an online seru scheduling problem with the uncertainty of new batch arrivals. First, the decision-making process for online seru scheduling is delineated. Subsequently, drawing on this process, we design three simple and effective scheduling rules that have different advantages. To optimize their inherent benefits, an epsilon-greedy based hyper-heuristic algorithm is designed to adaptively select the appropriate rule for online seru scheduling. Within this algorithmic framework, the three scheduling rules serve as low-level heuristics, while e-greedy acts as a high-level strategy. The extensive experiments are conducted to demonstrate the performance of the proposed algorithm.

关键词： online seru scheduling scheduling rules hyper-heuristic algorithm new batch arrivals

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MEDHEA: Multidimensional estimation of distribution based hyper-heuristic evolutionary algorithm for energy-efficient distributed assembly no-wait flow-shop scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Zhang, Zi-Qi Zhu, Xue-Peng Xu, Yan-Xuan Qian, Bin Yang, Biao Hu, Rong Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Higher Educ Key Lab Ind Intelligence & Syst Yunnan Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Peoples R China

With the advancement of economic globalization and smart manufacturing, green and distributed manufacturing are attracting increasing attention. This study addresses the challenges of the energy-efficient distributed assembly no-wait flow-shop scheduling problem (EE_DANWFSP) with the dual objectives of minimizing both the makespan and total energy consumption. Given the complexity of this multi-objective problem, a multidimensional estimation of distribution based hyper-heuristic evolutionary algorithm (MEDHEA) is presented to tackle the EE_DANWFSP. First, problem properties are analyzed and problem-specific low-level heuristics (LLHs) are developed to directly search the solution space according to the characteristics of the problem. Then, MEDAbased high-level strategy (HLS) is designed in which a multidimensional probabilistic model is used to learn and accumulate promising patterns from sequences of LLHs. Next, updating mechanisms and sampling strategies are designed to estimate the critical characteristics of ordinal correlations and positional connections of LLHs. Considering the balance between both quality and diversity of the solutions, the problem-specific hybrid initialization methods are provided to produce the high-quality initial population. Furthermore, speed adjustment strategies are employed to strengthen the quality of nondominated solutions, and speedup scanning methods are integrated to enhance the computational efficiency. In addition, a MILP model for the EE_DANWFSP is formulated and validated by Gurobi solver. The effect of key parameters is investigated through a multifactor ANOVA. Finally, extensive experiments are conducted on well-known benchmark instances and comprehensive comparisons are performed with state-of-the-art algorithms across multiple metrics. Statistical results demonstrate the efficiency and effectiveness of the proposed MEDHEA in solving the EE_DANWFSP.

关键词： Distributed assembly flow-shop scheduling Energy-efficient scheduling MEDA-based HLS Low-level heuristics hyper-heuristic algorithm

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A novel evolutionary status guided hyper-heuristic algorithm for continuous optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第9期27卷 12209-12238页

作者： Zhong, Rui Yu, Jun Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Niigata Univ Inst Sci & Technol Niigata Japan

This paper proposes a novel evolutionary status guided hyper-heuristic algorithm named ES-HHA for continuous optimization. A representative hyper-heuristic algorithm consists of two components: the low-level component and the high-level component. In the low-level component, to balance the exploitation and exploration during optimization, we design an exploitative operator pool and an explorative operator pool as low-level heuristics (LLHs), where the former is constructed using local search based operators, and the latter consists of various mutation operators from differential evolution (DE). In the high-level component, we design a probabilistic selection function based on the fitness distance correlation (FDC) and the population diversity (PD). Since these two metrics can reflect the complexity of the fitness landscape and the status of the evolutionary swarm, the integration of these two metrics is expected to determine the sequence of heuristics automatically and intelligently. To evaluate the performance of our proposal, we implement comprehensive numerical experiments on CEC2014, CEC2022, and eight engineering optimization tasks. A total of 14 famous optimization approaches are adopted as competitors. Furthermore, the ablation experiment is conducted to evaluate the high-level component independently, while the sensitivity experiment contributes to determining the optimal hyperparameter setting. The experimental results and statistical analysis show that ES-HHA is competitive, and the evolutionary status guided probabilistic selection function can determine the optimization intelligently.

关键词： hyper-heuristic algorithm Evolutionary status Low-level heuristics (LLHs) Fitness distance correlation (FDC) Population diversity (PD)

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A novel intelligent hyper-heuristic algorithm for solving optimization problems

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5041-5053页

作者： Tong, Zhao Chen, Hongjian Liu, Bilan Cai, Jinhui Cai, Shuo Hunan Normal Univ Coll Informat Sci & Engn Changsha 410012 Hunan Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Peoples R China

In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea "ML to choose heuristics" (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems.

关键词： Arithmetic solution combinatorial optimization genetic algorithm hyper-heuristic algorithm Q-learning

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A hyper-heuristic algorithm for VRP problem and its application in highway construction

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2023年第2期16卷 143-152页

作者： Zhong, Wen Xie, Zhijun Li, Mai Zhang, Qi Chen, Shaomiao Hunan Pingyi Expressway Construct & Dev Co Ltd Yueyang Peoples R China Hunan Commun Res Inst Co Ltd Changsha Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Hunan Key Lab Serv Comp & New Software Serv Techno Xiangtan Peoples R China

Many problems can be described as vehicle routing problems (VRPs) in engineering applications. More and more scholars have been paying attention to VRP optimisation in recent years, especially for large-scale VRPs, with the development of logistics business. In this paper, a hyper-heuristic algorithm for large-scale VRPs is proposed. It uses the diversity of strategic space to improve the diversity of algorithms. In contrast, most of the current VPR optimisation methods only pay attention to the characteristics of solution space. The proposed algorithm consists of two layers, a high-level heuristic selection strategy and a low-level heuristic policy pool. The higher-level strategy adjusts the algorithm's weight and guides the search direction dynamically based on the historical performance of the lower-level local search strategy. Simulation experiments show that, compared to the other two state-of-the-art algorithms, the proposed algorithm is more stable and performs more efficiently under most GWKC instances. Additionally, the proposed algorithm is practically applied to a material distribution problem in highway construction, and better performance is achieved.

关键词： vehicle routing problem VPR hyper-heuristic algorithm HHA highway construction large scale optimisation

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A hyper-heuristic algorithm Based on Q-Learning for 3D Drone Trajectory Planning 15th

A Hyper-heuristic Algorithm Based on Q-Learning for 3D Drone...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Zhou, Zhenghan Wan, Mengjie Zhou, Tianwei Niu, Ben Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China

ISBN: (纸本)9789819771837;9789819771844

Trajectory planning problem for drone is crucial for safely executing tasks in complex environments. This study proposes a hyper-heuristic algorithm to solve the above problem. Firstly, the problem is formulated as a multi-constrained optimization problem, incorporating constraints on drone performance and environmental conditions. Secondly, a hyper-heuristic algorithm based on Q-learning (HHBQL) is introduced, which utilizes Q-learning (QL) as a high-level heuristic selection strategy and two heuristic algorithms as low-level optimizers to select suitable heuristic algorithms for drone trajectory planning. The low-level heuristics include particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO). Subsequently, the generated paths are smoothed using cubic B-spline curves. Finally, simulation experiments are conducted in two environments comparing the proposed algorithm with three other algorithms. Results indicate that the proposed algorithm outperforms the other three algorithms in terms of optimization performance.

关键词： hyper-heuristic algorithm Q-learning Trajectory Planning Drone

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