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Deep Reinforcement Learning-Based job shop scheduling of Smart Manufacturing

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Computers, Materials & Continua 2022年第12期73卷 5103-5120页

作者： Eman K.Elsayed Asmaa K.Elsayed Kamal A.Eldahshan School of Computers Science Canadian International College(CIC).Department of MathematicsFaculty of Science(Girls)AL-Azhar UniversityCairo11511Egypt Department of Mathematics Faculty of Science(Girls)AL-Azhar UniversityCairo11511Egypt Department of Mathematics Faculty of ScienceAL-Azhar UniversityCairo11511Egypt

Industry 4.0 production environments and smart manufacturing systems integrate both the physical and decision-making aspects of manufacturing operations into autonomous and decentralized *** of the key aspects of these systems is a production planning,specifically,scheduling operations on the *** cope with this problem,this paper proposed a Deep Reinforcement Learning with an Actor-Critic algorithm(DRLAC).We model the job-shop scheduling Problem(JSSP)as a Markov Decision Process(MDP),represent the state of a JSSP as simple Graph Isomorphism Networks(GIN)to extract nodes features during scheduling,and derive the policy of optimal scheduling which guides the included node features to the best next action of *** addition,we adopt the Actor-Critic(AC)network’s training algorithm-based reinforcement learning for achieving the optimal policy of the *** prove the proposed model’s effectiveness,first,we will present a case study that illustrated a conflict between two job scheduling,secondly,we will apply the proposed model to a known benchmark dataset and compare the results with the traditional scheduling methods and trending *** numerical results indicate that the proposed model can be adaptive with real-time production scheduling,where the average percentage deviation(APD)of our model achieved values between 0.009 and 0.21 comparedwith heuristic methods and values between 0.014 and 0.18 compared with other trending approaches.

关键词： Reinforcement learning job shop scheduling graphical isomorphism network actor-critic networks

Combined Timetabling Procedure and Complete Local Search for No-Wait job shop scheduling with Total Tardiness

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Journal of Donghua University(English Edition) 2014年第2期31卷 83-91页

作者：杨玉珍顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

The strong non-deterministic polynomial-hard( NP-hard)character of job shop scheduling problem( JSSP) has been acknowledged widely and it becomes stronger when attaches the nowait constraint,which widely exists in many production processes,such as chemistry process, metallurgical process. However,compared with the massive research on traditional job shop problem,little attention has been paid on the no-wait ***,in this paper, we have dealt with this problem by decomposing it into two sub-problems, the timetabling and sequencing problems,in traditional frame work. A new efficient combined non-order timetabling method,coordinated with objective of total tardiness,is proposed for the timetabling problems. As for the sequencing one,we have presented a modified complete local search with memory combined by crossover operator and distance counting. The entire algorithm was tested on well-known benchmark problems and compared with several existing *** experiments showed that our proposed algorithm performed both effectively and efficiently.

关键词： job shop scheduling no-wait timetabling tardiness complete local search with memory

A Hybrid Algorithm Based on Comprehensive Search Mechanisms for job shop scheduling Problem

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Complex System Modeling and Simulation 2024年第1期4卷 50-66页

作者： Lin Huang Shikui Zhao Yingjie Xiong School of Mechanical Engineering University of JinanJinan 250022China

The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial *** at the job shop scheduling problem,a hybrid algorithm based on comprehensive search mechanisms(HACSM)is proposed to optimize the maximum completion *** combines three search methods with different optimization scales,including fireworks algorithm(FW),extended Akers graphical method(LS1+_AKERS_EXT),and tabu search algorithm(TS).FW realizes global search through information interaction and resource allocation,ensuring the diversity of the ***1+_AKERS_EXT realizes compound movement with Akers graphical method,so it has advanced global and local search *** LS1+_AKERS_EXT,the shortest path is the core of the algorithm,which directly affects the encoding and decoding of *** order to find the shortest path,an effective node expansion method is designed to improve the node expansion *** the part of centralized search,TS based on the neighborhood structure is ***,the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature.

关键词： job shop scheduling fireworks algorithm tabu search Akers graphical hybrid scheduling algorithms

Infeasibility test algorithm and fast repair algorithm of job shop scheduling problem

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Journal of Southeast University(English Edition) 2011年第1期27卷 88-91页

作者：孙璐黄志张惠民顾文钧东南大学交通学院南京210096 Department of Civil Engineering Catholic University of America 华中科技大学计算机学院武汉430074 山西省公路局晋中分局晋中030600

To diagnose the feasibility of the solution of a job-shop scheduling problem（JSSP）,a test algorithm based on diagraph and heuristic search is developed and verified through a case ***,a new repair algorithm for modifying an infeasible solution of the JSSP to become a feasible solution is proposed for the general *** computational complexity of the test algorithm and the repair algorithm is both O（n） under the worst-case scenario,and O（2J＋M） for the repair algorithm under the best-case *** repair algorithm is not limited to specific optimization methods,such as local tabu search,genetic algorithms and shifting bottleneck procedures for job shop scheduling,but applicable to generic infeasible solutions for the JSSP to achieve feasibility.

关键词： infeasibility job shop scheduling repairing algorithm

A Promising Initial Population Based Genetic Algorithm for job shop scheduling Problem

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Journal of Software Engineering and Applications 2016年第5期9卷 208-214页

作者： Vedavyasrao S. Jorapur Vinod S. Puranik Anand S. Deshpande Mahesh Sharma Department of Industrial and Production Engineering Basaveshwar Engineering College Bagalkot India Basaveshwar Engineering College Bagalkot India Gogte Institute of Technology Belagavi India Fr. Conceicao Rodrigues College of Engineering Bandra West Mumbai India

job shop scheduling problem is typically a NP-Hard problem. In the recent past efforts put by researchers were to provide the most generic genetic algorithm to solve efficiently the job shop scheduling problems. Less attention has been paid to initial population aspects in genetic algorithms and much attention to recombination operators. Therefore authors are of the opinion that by proper design of all the aspects in genetic algorithms starting from initial population may provide better and promising solutions. Hence this paper attempts to enhance the effectiveness of genetic algorithm by providing a new look to initial population. This new technique along with job based representation has been used to obtain the optimal or near optimal solutions of 66 benchmark instances which comprise of varying degree of complexity.

关键词： job shop scheduling job Based Representation NP-Hard Recombination Operators etc.

A Dynamic job shop scheduling Method Based on Lagrangian Relaxation

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Tsinghua Science and Technology 1999年第1期4卷 29-34页

作者：熊锐范玉顺吴澄 Department of Automation Tsinghua University Beijing 100084

Due to the complexity of dynamic job shop scheduling in flexible manufacturing system(FMS), many heuristic rules are still used today. A dynamic scheduling approach based on Lagrangian relaxation is proposed to improve the quality and guarantee the real time capability of dynamic scheduling. The proposed method makes use of the dynamic predictive optimal theory combined with Lagrangian relaxation to obtain a good solution that can be evaluated quantitatively. The Lagrangian multipliers introduced here are capable of describing machine predictive states and system capacity constraints. This approach can evaluate the suboptimality of the scheduling systems. It can also quickly obtain high quality feasible schedules, thus enabling Lagrangian relaxation to be better used in the dynamic scheduling of manufacturing system. The efficiency and effectiveness of this method are verified by numerical experiments. 

关键词： job shop scheduling dynamic programming Lagrangian relaxation flexible manufacturing system (FMS)

Local Search Algorithm with Hybrid Neighborhood and Its Application to job shop scheduling Problem

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Journal of Southwest Jiaotong University(English Edition) 2004年第2期12卷 95-100页

作者：黄文奇曾立平 School of Computer Science and Technology Huazhong University of Science and Technology

A new local search method with hybrid neighborhood for job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while search procedure get trapped at local optima and carry the search to areas of the feasible set with better prospect. New strategies used for breaking out of entrapments are presented and they are helpful for the procedure to improve local optima. A performance comparison of the proposed method with some best-performing algorithms on all 10-job, 10-machine benchmark problems and the other two problems generated by Fisher and Thompson (ie., FT6 and FT20)is made. The experiment results show the better optimal performance of the proposed algorithm.

关键词： job shop scheduling Local search Hybrid neighborhood Off-trap strategy

An Tentative Taboo Search Algorithm for job shop scheduling

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Wuhan University Journal of Natural Sciences 2006年第3期11卷 547-550页

作者： HUANG Wenqi HUANG Zhi WANG Lei College of Computer Science and Technology Huazhong University of Scienee and Technology Wuhan 430074Hubei China

An effective heuristic algorithm for solving the minimum makespan problem of job shop scheduling is presented. With a given feasible solution as a start point, we use Taboo Search technique to get a schedule for each neighbor of all its neighbors, and select the best one of the schedules as the new start point. We repeat the procedure, unless the new start point is not better than the proceeding one. To make sure of better results, we introduce reverse technique to the algorithm as well. The computational experiments of the 45 standard instances show that our algorithm yields better results than TSAB algorithm.

关键词： heuristics job shop scheduling Taboo search tentative reverse

Genetic Algorithm for job shop scheduling with Earliness and Tardiness Penalties

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Genetic Algorithm for Job Shop Scheduling with Earliness and...