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A heuristic algorithm for the distributed and flexible job-shop scheduling problem

JOURNAL OF SUPERCOMPUTING 2014年第1期67卷 69-83页

作者： Ziaee, Mohsen Univ Bojnord Dept Ind Engn Bojnord *** Iran

The distributed manufacturing takes place in a multi-factory environment including several factories, which may be geographically distributed in different locations, or in a multi-cell environment including several independent manufacturing cells located in the same plant. Each factory/cell is capable of manufacturing a variety of product types. An important issue in dealing with the production in this decentralized manner is the scheduling of manufacturing operations of products (jobs) in the distributed manufacturing system. In this paper, we study the distributed and flexible job-shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. A fast heuristic algorithm based on a constructive procedure is developed to obtain good quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and promising for practical problems.

关键词： distributed scheduling Flexible job shop Makespan Heuristic

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A Bi-Population Cooperative Memetic Algorithm for distributed Hybrid Flow-Shop scheduling

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021年第6期5卷 947-961页

作者： Wang, Jing-Jing Wang, Ling Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

In the context of globalization and decentralized economies, the distributed manufacturing and scheduling systems have become emerging in large enterprises. This article addresses the distributed hybrid flow-shop scheduling problem (DHFSP) with heterogeneous factories to minimize makespan. Different from single-factory scheduling problem, DHFSP contains several strongly coupled sub-problems, i.e., factory assignment of jobs and the job sequencing in each hybrid flow-shop. In this article, we present a mixed integer linear programming model for the DHFSP and make a first attempt to propose a bi-population cooperative memetic algorithm (BCMA) for solving such a strongly NP-hard problem. The proposed optimization framework comprises collaborative initialization, bi-population cooperation and local intensification. To generate diverse solutions with small makespan, two knowledge-based heuristics are designed for collaborative initialization by utilizing the lower bound of the problem and the historic information of elite solutions during search process. To balance exploration and exploitation, two populations are evolved in a cooperative way. To further enhance the optimization capability, intensification search with multiple problem-specific operators is incorporated. The effect of parameter setting is investigated and extensive computational tests are carried out. The comparative results show that the BCMA is more effective than both the Math solver Gurobi and the existing iterated greedy algorithm, and the effectiveness of each specific design is also verified in solving DHFSP.

关键词： Job shop scheduling Memetics Cooperative memetic algorithm bi-population distributed scheduling hybrid flow-shop scheduling decomposition lower bound

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Fuzzy distributed Hybrid Flow Shop scheduling Problem With Heterogeneous Factory and Unrelated Parallel Machine: A Shuffled Frog Leaping Algorithm With Collaboration of Multiple Search Strategies

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IEEE ACCESS 2020年 8卷 214209-214223页

作者： Wang, Lei Li, Dongdong Anhui Polytech Univ Sch Mech Engn Wuhu 241000 Peoples R China

The single factory production is shifting to multi-factory production which brings new challenges. Hybrid flow shop problem (HFSP) as a common problem in production-manufacturing has been studied extensively, however distributed hybrid flow shop problem (DHFSP) has not been paying sufficient attention. Fuzzy distributed hybrid flow shop problem (FDHFSP) is studied and a shuffled frog leaping algorithm with collaboration of multiple search strategies (SFLA-CMSS) is proposed to optimize multiple objectives simultaneously. In SFLA-CMSS, multiple search-mode collaboration strategy in memeplex search consists of multiple search strategies. A search strategy is selected according to the historical search status of the solution. Dynamic memeplex number adjustment strategy is used to provide more search opportunity to the memeplex which has better search capability. Experiments are conducted and the computational results reveal that SFLA-CMSS has promising advantages on FDHFSP.

关键词： Production facilities Job shop scheduling scheduling Energy consumption Search problems Parallel machines Optimization Hybrid flow shop distributed scheduling heterogeneous factory unrelated parallel machine fuzzy scheduling shuffled frog leaping algorithm

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

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Huang, Jiang-Ping Gao, Liang Li, Xin-Yu Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

distributed Job-shop scheduling Problem (DJSP) is a hotspot in industrial and academic fields due to its valuable application in the real-life productions. For DJSP, the available methods aways complete the job selection first and then search for an appropriate factory to assign the selected job, which means job selection and job assignment are done independently. This paper proposes an end-to-end Deep Reinforcement Learning (DRL) method to make the two decisions simultaneously. To capture the problem characteristics and realize the objective optimization, the Markov Decision Process (MDP) of DJSP is formulated. Specialised action space made up of operation-factory pairs is designed to achieve the simultaneous decision-making. A stitched disjunctive graph representation of DJSP is specially designed, and a Graph Neural Network (GNN) based feature extraction architecture is proposed to dig the state embedding during problem solving. A Proximal Policy Optimization (PPO) method is applied to train an action-selection policy. To further lead the agent to assign jobs to the factory with smaller makespan, a probability enhancement mechanism is designed. The experimental results on 240 test instances have shown that the proposed method outperforms 8 classical Priority Dispatching Rules (PDRs), 3 closely-related RL methods and 5 metaheuristics in terms of effectiveness, stability and generalization.

关键词： distributed job shop scheduling distributed scheduling Reinforcement learning

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Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 423-433页

作者： Zhang, Guanghui Xing, Keyi Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Syst Engn Inst 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Guizhou Inst Technol Sch Sci Guiyang 550003 Guizhou Peoples R China

This paper studies the distributed two-stage assembly flowshop problem with separate setup times, which is a generalisation for the regular two-stage assembly flowshop problem in the distributed manufacturing environment. The optimization objective is to find a suitable job schedule such that the criterion of total completion time is minimized. To deal with such a problem, we propose a novel memetic algorithm (MA) based on a recently developed social spider optimization (SSO). To the best of our knowledge, it is the first effort to explore the SSO-based MA (MSSO) and to apply SSO in the field of combinational optimization. In the proposed MSSO algorithm, we first modify the original version of SSO to adapt to the distributed problems, and then integrate two improvement techniques, problem-special local search and self-adaptive restart strategy, within MA framework. In the numerical experiment, the parameters used in MSSO are calibrated and suitable parameter values are suggested based on the Taguchi method. Experimental results and comparisons with the existing algorithms validate the effectiveness and efficiency of the proposed MSSO for addressing the considered problem. In addition, the effect of problem scale parameters on MSSO and the effectiveness of the proposed improvement techniques are also investigated and demonstrated.

关键词： Assembly flowshop distributed scheduling Memetic algorithm Separate setup time Social spider optimization

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Fair scheduling of bag-of-tasks applications using distributed Lagrangian optimization

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JOURNAL OF PARALLEL AND distributed COMPUTING 2014年第1期74卷 1914-1929页

作者： Bertin, Remi Hunold, Sascha Legrand, Arnaud Touati, Corinne CNRS F-75700 Paris France Univ Grenoble LIG Lab MESCAL Project Montbonnot St Martin France

Large scale distributed systems typically comprise hundreds to millions of entities (applications, users, companies, universities) that have only a partial view of resources (computers, communication links). How to fairly and efficiently share such resources between entities in a distributed way has thus become a critical question. Although not all applications are suitable for execution on large scale distributed computing platform, ideal are the Bag-of-Tasks (BoT) applications. Hence a large fraction of jobs in workloads imposed on Grids is made of sequential applications submitted in the form of BoTs. Up until now, mainly simple mechanisms have been used to ensure a fair sharing of resources among these applications. Although these mechanisms are proved to be efficient for CPU-bound applications, they are known to be ineffective in the presence of network-bound applications. A possible answer resorts to Lagrangian optimization and distributed gradient descent. Under certain conditions, the resource sharing problem can be formulated as a global optimization problem, which can be solved by a distributed self-stabilizing supply and demand algorithm. In the last decade, this technique has been applied to design various network protocols (variants of TCP, multi-path network protocols, wireless network protocols) and even distributed algorithms for smart grids. In this article, we explain how to use this technique for fairly scheduling concurrent BoT applications with arbitrary communication-to-computation ratio on a Grid. Yet, application heterogeneity raises severe convergence and stability issues that did not appear in the previous contexts and need to be addressed by non-trivial modifications. The effectiveness of our proposal is assessed through an extensive set of complex and realistic simulations. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Lagrangian optimization Steady-state scheduling distributed scheduling Grid computing

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Q-Learning-Assisted Meta-Heuristics for scheduling distributed Hybrid Flow Shop Problems

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Computers, Materials & Continua 2024年第9期80卷 3573-3589页

作者： Qianyao Zhu Kaizhou Gao Wuze Huang Zhenfang Ma Adam Slowik Institute of Systems Engineering Macao University of Science and TechnologyMacao99078China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin75-453Poland

The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted *** work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned ***,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are *** to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local *** of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during ***,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed *** experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random *** verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving *** Friedman test is executed on the results by five *** is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

关键词： distributed scheduling hybrid flow shop meta-heuristics local search Q-learning

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A Hybrid Pareto-Based Tabu Search for the distributed Flexible Job Shop scheduling Problem With E/T Criteria

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IEEE ACCESS 2018年 6卷 58883-58897页

作者： Li, Jun-Qiang Duan, Peiyong Cao, Jinde Lin, Xiao-Ping Han, Yu-Yan Shandong Normal Univ Sch Informat & Engn Jinan 250014 Shandong Peoples R China Nanjing Forestry Univ Sch Econ & Management Nanjing 210037 Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing 211189 Jiangsu Peoples R China Southeast Univ Res Ctr Complex Syst & Network Sci Nanjing 210096 Jiangsu Peoples R China Liaocheng Univ Sch Comp Liaocheng 252059 Peoples R China

During recent years, distributed manufacturing optimization problems have been researched and applied in many fields, such as steelmaking system and textile production process. To solve the multi objective distributed flexible job shop scheduling problem, a hybrid Pareto-based tabu search algorithm (HPTSA) is investigated to minimize four objectives simultaneously, i.e., the makespan, the maximal workload, the total workload, and the earliness/tardiness (E/T) criteria. In the proposed algorithm, several approaches considering both the problem characteristics and the objective features are used to initialize the group of solutions. Then, five types of neighborhood structures that consider both problem structures are developed to enhance the exploitation and exploration capabilities. In addition, a well-designed backward method is proposed to optimize the E/T criteria. Based on the realistic production data in the steelmaking system, several instances with different problem scales are randomly generated. Four efficient multi objective optimization algorithms are selected to make detailed comparisons with the proposed HPTSA algorithm. After detailed tests on the realistic instances, the experimental comparison results show that the proposed algorithm shows competitive performance compared with the selected efficient algorithms.

关键词： Flexible job shop scheduling problem tabu search multi-objective optimization Pareto archive set distributed scheduling

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A distributed Real-time scheduling System for Industrial Wireless Networks

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2021年第5期20卷 1-28页

作者： Modekurthy, Venkata P. Saifullah, Abusayeed Madria, Sanjay Univ Nevada 4247 SEB4505 S Maryland Pkwy Las Vegas NV 89154 USA Wayne State Univ 5057 Woodward AveSuite 14110-2 Detroit MI 48202 USA Missouri Univ Sci & Technol 325A Comp Sci Bldg500 W 15th St Rolla Rolla MO 65409 USA

The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such industrial IoT. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose distributedHART-a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. distributed! IART obviates the need of creating and disseminating a central global schedule in our approach, thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We have implemented distributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in distributedHART compared to existing centralized approach while ensuring similar schedulability.

关键词： WirelessHART real-time networking distributed scheduling

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Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping

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ENGINEERING OPTIMIZATION 2020年第9期52卷 1461-1474页

作者： Lei, Deming Wang, Tian Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China

Two-stage hybrid flowshop scheduling in a single factory has been considered fully;however, the distributed two-stage hybrid flowshop scheduling problem (DTHFSP) is seldom studied in a multi-factory environment. In this study, a DTHFSP with sequence-dependent setup times is investigated and a shuffled frog-leaping algorithm with memeplex grouping (MGSFLA) is proposed to minimize makespan and the number of tardy jobs. After an initial population is generated by an heuristic, two phases are executed sequentially with a new population division. In the second phase, all memeplexes are categorized into three groups, the different search processes are implemented in the different groups and the best memeplex is excluded from population division. A number of experiments are conducted on many instances and computational results validate the effectiveness of the main strategies and the promising advantages of an MGSFLA.

关键词： Shuffled frog-leaping algorithm distributed scheduling two-stage hybrid flowshop scheduling problem sequence-dependent setup times

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