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A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

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RAIRO-OPERATIONS RESEARCH 2022年第6期56卷 4129-4143页

作者： Behnamian, Javad Asgari, Hamed Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Iran

Today, because the market is scattered around the world, manufacturing activities are not limited to a single location and have spread globally. As a result, the discussion of scheduling the factory has changed from a classic single to a network scheduling as a need in the real world. In this regard, this study considers the scheduling of multiple factories by taking into account the job transportation time between factories. The main problem here is that each job would be assigned to which factory and machine. In this research, unrelated parallel machines are considered in which the processing time of jobs depends on the machine and setup time. To minimize the makespan, first, a mixed-integer linear model was proposed in which two types of modeling have been combined. Then, a hyper-heuristic algorithm (HHA) was designed to solve the problem in a reasonable time by choosing the best method among four low-level heuristic methods that are precisely designed according to the properties of the problem. Finally, the efficiency of the proposed algorithm has been compared with the imperialist competitive algorithm (ICA) by conducting experiments. The results show that the proposed algorithm performs very well compared to the ICA and, in more than 75% of the test problems, the proposed algorithm was superior. Also, based on the analysis, in comparing the proposed algorithm with the ICA, it can be concluded that there is a significant difference between the results, and in all cases, the HHA was remarkably better. Considering the challenges and rapid changes of today's market that traditional centralized production planning does not have enough flexibility to respond to them, the results of this research are expected to be useful and attractive for planners in this field.

关键词： Cooperative production scheduling hyper-heuristic algorithm unrelated parallel machine sequence-dependent setup time transportation time

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A Novel Cooperative Multi-Stage hyper-heuristic for Combination Optimization Problems

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Complex System Modeling and Simulation 2021年第2期1卷 91-108页

作者： Fuqing Zhao Shilu Di Jie Cao Jianxin Tang Jonrinaldi School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China Department of Industrial Engineering Universitas AndalasPadang 25163Indonesia

A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuristic and a set of low-level *** low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the *** this study,a Cooperative Multi-Stage hyper-heuristic(CMS-HH)algorithm is proposed to address certain combinatorial optimization *** the CMS-HH,a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the *** the search phase,an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the *** addition,a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous *** performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems,including Boolean satisfiability problems,one-dimensional packing problems,permutation flow-shop scheduling problems,personnel scheduling problems,traveling salesman problems,and vehicle routing *** experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.

关键词： hyper-heuristic algorithm Multi-Armed Bandits(MAB) relay hybridization technology combinatorial optimization

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Convergence based Evaluation Strategies for Learning Agent of hyper-heuristic Framework for Test Case Prioritization 20

Convergence based Evaluation Strategies for Learning Agent o...

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20th IEEE International Conference on Software Quality, Reliability, and Security (QRS)

作者： Han, Jinjin Li, Zheng Guo, Junxia Mao, Ruilian Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728189130

Learning agent plays significant role in the hyperheuristic framework for test case prioritization, where an evaluation strategy is applied to evaluate the execution results produced by the current heuristic algorithm and select the most appropriate heuristic algorithm for the next generation. Hierarchical Distribution (HD) is used as evaluation strategy based on the dominance relationship between the individuals from the present and last generations. In addition to the distribution of the solution set, a good convergence towards the optimal Pareto front is often desired. In this paper, the convergence ability of the individuals is further considered in the design of the evaluation strategy for the learning agent, in which Pareto Dominance and Convergence Information are adopted. Three evaluation strategies are proposed and empirically studied, and the experimental results show that the hyper-heuristic algorithms with the proposed evaluation strategies are more effective and efficient for test case prioritization.

关键词： Test Case Prioritization hyper-heuristic algorithm Learning Agent Evaluation Strategy

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A Hybrid algorithms Construction of hyper-heuristic for Test Case Prioritization 44

A Hybrid Algorithms Construction of Hyper-heuristic for Test...

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44th Annual IEEE-Computer-Society International Conference on Computers, Software, and Applications (COMPSAC)

作者： Li, Zheng Xi, Yanzhao Zhao, Ruilian Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781728173030

By scheduling algorithms in the low-level algorithm library, the hyper-heuristic algorithm can help to effectively select an appropriate method to deal with hard computational search problems. The hyper-heuristic algorithm usually includes a high-level scheduling layer and a low-level algorithm layer. The high-level strategy layer selects the algorithm for the next scheduling by evaluating the execution effect of the different algorithms in the low-level layer, while the low-level layer includes a variety of different heuristic algorithms which called algorithm library. The concrete hyper-heuristic framework for multi-objective test case prioritization was presented where the 18 multi-objective algorithms were formed in the low-level library. It has been gradually realized that a hybrid algorithm by combining single objective algorithm and multi-objective optimization algorithm is better than the individual. This paper explores the influence of the construction pattern of algorithm library for the hyper-heuristic algorithm by constructing the fusion pattern of different types of algorithms.

关键词： hyper-heuristic algorithm test case prioritization multi-objective optimization

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A hyper-heuristic with deep Q-network for the multi-objective unmanned surface vehicles scheduling problem

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NEUROCOMPUTING 2024年 596卷

作者： Xu, Ningjun Shi, Zhangsong Yin, Shihong Xiang, Zhengrong Naval Univ Engn Coll Weapons Engn Wuhan 430033 Hubei Peoples R China Jiangsu Automat Res Inst Lianyungang 222006 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

This paper proposes a learning -based hyper -heuristic algorithm for the coverage path planning problem of multiple unmanned surface vehicles (USV). The makespan and coverage of the USVs are considered simultaneously. The proposed method does not need to specify the location of the mapping task points in advance;instead, the task allocation and path planning can be realized only based on the parameter information of the USV and the watershed. Considering the uncertainty in the actual mapping process, the triangular fuzzy numbers are used to represent the mapping time and mapping radius of USVs. In order to solve the multi-USV scheduling problem efficiently, this paper proposes an NSGA-II based on dueling double deep Q -network called NSGA-II-DQN. NSGA-II-DQN integrates ten effective global search operators and five local search operators, which can achieve a good balance between exploration and exploitation. Experiment results in various scenarios demonstrate that NSGA-II-DQN can efficiently search for travel paths covering the entire water area for multiple USVs in a short planning time.

关键词： Multi-objective optimization Unmanned surface vehicle Coverage path planning hyper-heuristic algorithm Deep reinforcement learning

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Simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2024年第11期124卷 3012-3051页

作者： Xie, Zhanghuang Li, Xiaomei Huang, Dian Appolloni, Andrea Fang, Kan Tianjin Univ Tianjin Peoples R China Univ Roma Tor Vergata Rome Italy Cranfield Univ Bedford England

PurposeWe consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such ***/methodology/approachWe propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the *** simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid ***/valueTo the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

关键词： Product platform Additive/subtractive hybrid machine Simulated annealing algorithm hyper-heuristic algorithm

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COOPERATING OF LOCAL SEARCHES BASED hyperheuristic APPROACH FOR SOLVING TRAVELING SALESMAN PROBLEM

COOPERATING OF LOCAL SEARCHES BASED HYPERHEURISTIC APPROACH ...

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International Conference on Evolutionary Computation Theory and Applications (ECTA 2011) / International Conference on Fuzzy Computation Theory and Applications (FCTA 2011)

作者： Mitra, Montazeri Bahrololoum, Abbas Nezamabadi-pour, Hossein Baghshah, Mandieh Soleymani Montazeri, Mandieh Shahid Bahonar Univ Dept Comp Engn Young Researchers Soc Kerman Iran Shahid Bahonar Univ Dept Comp Engn Kerman Iran Shahid Bahonar Univ Dept Elect Engn Kerman Iran Kerman Med Univ Kerman Iran

ISBN: (纸本)9789898425836

Until now various heuristic optimization methods have been developed for solving NP-Hard problems. These methods by trading off between exploration and exploitation attempt to find an optimum solution. In this paper, we introduce a new optimization algorithm based on hyper-heuristic for solving TSP. A hyper-heuristic approach has two layers. In low level, we have six local searches and in high level we use Genetic algorithm. Genetic algorithm corporate local searches efficiency. The proposed method has high ability to searching in solution space and explores and exploit appropriately. This method exploits space depended on characteristics of the region of the solution space that is currently under exploration and also the performance history of local searches. The proposed method is used to solve TSP and compared with well-known methods. The experimental results confirm the efficiency of the proposed method.

关键词： Meta-heuristic algorithm hyper-heuristic algorithm Traveling Salesman Problem

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A Reinforcement Learning Method for Solving the Production Scheduling Problem of Silicon Electrodes 19th

A Reinforcement Learning Method for Solving the Production S...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Huang, Yu-Fang Hu, Rong Wu, Xing Qian, Bin Yang, Yuan-Yuan Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

In this paper, a new silicon electrode production (SEP) scheduling problem resulting from silicon electrode manufacturing procedure is discussed. We combine two coupled subproblems to model the problem. One subproblem is a silicon rod cutting scheduling problem in parallel machines, and the other is a silicon electrode product scheduling problem in a hybrid flowshop. We present a reinforcement learning (RL) method to address this SEP problem. RL uses Q-learning algorithm to autonomously select heuristics from a pre-designed low-level heuristic set (LLHs). The selected heuristic is used to optimize the solution space for better results. Considering the "single-batch" coupling relationship in themanufacturing process, a two-stage encoding strategy is used for subproblems, and the related decoding mechanism was designed in the silicon rod cutting stage and silicon electrode production processing stage. Experimental results on newly introduced examples demonstrate that the suggested method effectively competes with cutting-edge algorithms.

关键词： Q-learning Silicon electrode Reinforcement learning hyper-heuristic algorithm Production scheduling

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Study on location-allocation problem and algorithm for emergency supplies considering timeliness and fairness

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期177卷

作者： Wang, Zheng Leng, Longlong Ding, Junjie Zhao, Yanwei Zhejiang Univ City Coll Sch Comp & Computat Sci Hangzhou 310015 Peoples R China Zhejiang Univ City Coll Sch Engn Hangzhou 310015 Peoples R China Zhejiang Univ Technol Sch Mech Engn Hangzhou 310023 Peoples R China 51 Huzhou St Hangzhou Zhejiang Peoples R China

To address the situation of insufficient reserve of resources in the initial stage of emergency rescue, a multi-objective location-allocation model for emergency supplies with timeliness and fairness being considered concurrently is proposed in this paper. The first objective is defined as time cost, including transport time cost and waiting time cost. The second objective is defined as the number of short supplies. The allocation of supplies is taken into account together with the urgency degree in a disaster area (DA), and the DAs with different ur-gency degrees are given different minimum allocation quantities of supplies to achieve fairness to a maximum extent. To solve this complex problem, a multi-objective hyper-heuristic (MOHH) optimization framework based on an evolutionary algorithm is proposed in this paper. In the framework, twelve low-level heuristics (LLHs) are designed with the actual information in the problem field being taken into account, and an online learning-based choice strategy is designed to choose high-quality and efficient LLHs. In addition, three acceptance criteria (AC) based on the D matrix are put forward to improve the performance of the MOHH framework. It is verified through comparative analysis experiments that the LLHs and the model are effective and the performance of the proposed multi-objective algorithm is better than that of NSGA-III and MOPSO.

关键词： Emergency logistics Location-allocation problem hyper-heuristic algorithm Multi-objective problem

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An adaptive melody search algorithm based on low-level heuristics for material feeding scheduling optimization in a hybrid kitting system

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

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

Facing highly diversified market demands in automotive industry, changing variants of components produced in mixed-model assembly lines (MMALs) has led to an increasing attention towards the material-feeding processes. Therefore, this paper originally proposes a novel type of material-feeding mode called hybrid kitting, leading to a better adaptation to MMALs. Since energy-saving and Just-in-time (JIT) principles are the two major concerns in production systems, a bi-objective mathematical model is established aiming to collaboratively minimize the multi-load automated guided vehicle (AGV) energy consumption as well as the kit conveyor depreciation cost in the hybrid kitting-based material-feeding system. Due to the non-deterministic polynomial hard (NP-hard) nature of the problem, a modified melody search-based hyper-heuristic algorithm (MMSA-HH) is proposed with seven low-level heuristic (LLH) operators. Based on the basic MSA, the melody composition rules are redesigned to enrich the diversity of solutions, adaptive adjustment of parameters is used to balance the local search and global search, and the fluctuated crowding distance calculation method is used in elite selection along with Pareto rank calculation. Computational experiment results reveal the effectiveness of the MMSA-HH when solving the problem. Finally, the managerial insights are given through comparing the impacts of kit container size, AGV type, and different kitting modes on the two objectives.

关键词： Mixed-model assembly line Material-feeding scheduling Automated guided vehicle routing problem Hybrid kitting Melody search algorithm hyper-heuristic algorithm

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