Article
Computer Science, Artificial Intelligence
Ying Sun, Jeng-Shyang Pan, Pei Hu, Shu-Chuan Chu
Summary: This paper introduces the Equilibrium Optimizer (EO) algorithm and the Enhanced Equilibrium Optimizer (EEO) algorithm based on communication strategies for solving the Job Shop Scheduling Problem (JSSP). Experimental results show that the improved algorithm has made significant improvements in solving JSSP.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Computer Science, Interdisciplinary Applications
Huali Fan, Hegen Xiong, Mark Goh
Summary: This paper introduces a mathematical programming model for dynamic job shop scheduling problem with extended technical precedence constraints (ETPC) and utilizes a constructive heuristic to solve large-scale problems. It demonstrates the effectiveness of genetic programming-based hyper-heuristic approach in generating problem-specific dispatching rules.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Binzi Xu, Yi Mei, Yan Wang, Zhicheng Ji, Mengjie Zhang
Summary: Dynamic Flexible Job Shop Scheduling (DFJSS) is a challenging problem with conflicting objectives. The traditional heuristic template may have limitations in handling dynamic environments, leading to the proposal of a novel heuristic template that delays routing decisions for improved decision accuracy. Through Genetic Programming Hyper-Heuristic (GPHH), the rules in the heuristic template are automatically evolved for better performance.
EVOLUTIONARY COMPUTATION
(2021)
Article
Mathematics
Anran Zhao, Peng Liu, Xiyu Gao, Guotai Huang, Xiuguang Yang, Yuan Ma, Zheyu Xie, Yunfeng Li
Summary: In this paper, a pure reactive scheduling method is proposed to deal with the uncertainty of new job arrivals in job shop scheduling. The method combines data mining, discrete event simulation, and dispatching rules to assign optimal DRs to each scheduling subperiod, achieving the purpose of locally updating the scheduling strategy and enhancing the overall scheduling effect of the manufacturing system.
Article
Computer Science, Interdisciplinary Applications
Shu Luo, Linxuan Zhang, Yushun Fan
Summary: This paper proposes an on-line rescheduling framework named as two-hierarchy deep Q network (THDQN) for the dynamic multi-objective flexible job shop scheduling problem with new job insertions. By optimizing two practical objectives including total weighted tardiness and average machine utilization rate, the trained THDQN has shown effectiveness and generality on a wide range of test instances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Chemical
Xiang Tian, Xiyu Liu
Summary: The study introduces a new hybrid heuristic algorithm to solve the JSSP problem and achieves good results by improving genetic algorithms, modifying particle swarm optimization, and local search methods.
Article
Engineering, Electrical & Electronic
Anran Zhao, Peng Liu, Yunfeng Li, Zheyu Xie, Longhao Hu, Haoyuan Li
Summary: In this study, a DR real-time selection system was developed using dispatching rules and decision tree algorithms to solve the job shop scheduling problem under personalized market demands. The system, which has self-feedback characteristics, can quickly and easily handle JSSP of different scales.
Article
Green & Sustainable Science & Technology
Ali Firat Inal, Cagri Sel, Adnan Aktepe, Ahmet Kursad Turker, Suleyman Ersoz
Summary: In a production environment, scheduling plays a crucial role in determining job and machine allocations as well as the operation sequence. The job shop production system presents challenges due to its diverse jobs, complex routes, and real-life events. To address the dynamic scheduling problem, a multi-agent system with reinforcement learning is proposed to minimize tardiness and flow time, thereby improving the dynamic scheduling techniques.
Article
Computer Science, Interdisciplinary Applications
Yong Gui, Dunbing Tang, Haihua Zhu, Yi Zhang, Zequn Zhang
Summary: In this paper, a scheduling method based on deep reinforcement learning is proposed for the dynamic flexible job-shop scheduling problem. By using composite scheduling actions to provide a continuous rule space and weight selection, the proposed method achieves better scheduling performance than a single dispatching rule and the DQN-based method.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Engineering, Industrial
Weibo Ren, Yan Yan, Yaoguang Hu, Yu Guan
Summary: This study proposed a novel proactive-reactive methodology for dynamic job-shop scheduling in flexible manufacturing systems, which formulated a joint optimisation model and designed a flowchart for dynamic decision-making, and developed a particle swarm optimisation algorithm integrated with genetic operators to generate a reschedule plan in time. Computational results demonstrate the efficiency of the developed methodology in practical production.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Mathematics
Hankun Zhang, Borut Buchmeister, Xueyan Li, Robert Ojstersek
Summary: This paper proposes an Improved Heuristic Kalman Algorithm to solve the dynamic job shop scheduling problem, which shows effective results in experiments with improved convergence rate, robustness, and reasonable running time.
Article
Engineering, Chemical
Xiaojun Long, Jingtao Zhang, Kai Zhou, Tianguo Jin
Summary: This paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve the dynamic flexible job-shop scheduling problem. By arranging the processing sequence of jobs and the relationship between operations and machines, the algorithm improves the economic benefit of the job-shop and the utilization rate of processing machines. Experimental results demonstrate the effectiveness of the proposed algorithm.
Article
Engineering, Industrial
Salama Shady, Toshiya Kaihara, Nobutada Fujii, Daisuke Kokuryo
Summary: This paper proposes a feature selection approach based on the Gene Expression Programming (GEP) algorithm to evolve high-quality scheduling rules in simple structures. By restricting the search space and selecting only meaningful features, this approach can speed up the search process and generate rules with high interpretability.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Krzysztof Kurowski, Tomasz Pecyna, Mateusz Slysz, Rafal Rozycki, Grzegorz Waligora, Jan Weglarz
Summary: The Job Shop Scheduling Problem (JSSP) is a complex and industry essential scheduling problem. Traditional algorithms for optimizing the makespan of a given schedule are limited by computational power. Inspired by the use of Quantum Annealing (QA), we propose a new approach using gate-model quantum architecture to solve JSSP instances. We demonstrate the effectiveness of Quantum Approximate Optimization Algorithm (QAOA) in solving JSSP and analyze its performance with varying parameters.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Simon Strassl, Nysret Musliu
Summary: This paper provides a systematic analysis of the job shop scheduling problem, extending the instance set and evaluating the performance of various algorithms. The findings show that the existing instances cover a smaller area and lead to different conclusions about algorithm performance. Different algorithms excel on different subsets of instances and can be trained using machine learning models.
COMPUTERS & OPERATIONS RESEARCH
(2022)
