Article
Computer Science, Artificial Intelligence
Nayeli Jazmin Escamilla Serna, Juan Carlos Seck-Tuoh-Mora, Joselito Medina-Marin, Norberto Hernandez-Romero, Irving Barragan-Vite, Jose Ramon Corona Armenta
Summary: The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that has been extensively studied to model and optimize more complex situations reflecting the current needs of the industry. This work introduces a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), which utilizes the concepts of a cellular automaton to generate and share information among a set of leading solutions called smart-cells. Experimental results demonstrate the satisfactory performance of the GLNSA algorithm when compared with recent algorithms, using four benchmark sets and 101 test problems.
PEERJ COMPUTER SCIENCE
(2021)
Article
Engineering, Industrial
Jin Xie, Xinyu Li, Liang Gao, Lin Gui
Summary: This paper proposes a hybrid genetic tabu search algorithm for the distributed flexible job-shop scheduling problem, which outperforms other comparison algorithms in terms of solution quality and computation efficiency.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Management
Karim Tamssaouet, Stephane Dauzere-Peres
Summary: This article presents a framework that unifies and generalizes well-known literature results on local search for job-shop and flexible job-shop scheduling problems. The proposed framework focuses on quickly ruling out infeasible moves and evaluating the quality of feasible neighbors, which are crucial for the success of local search approaches. It can be applied to any scheduling problem with an appropriate defined neighborhood structure. The proposed framework introduces novel procedures for evaluating feasibility and estimating the value of objective functions for neighbor solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
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
Computer Science, Artificial Intelligence
Shijie Cao, Rui Li, Wenyin Gong, Chao Lu
Summary: This paper studies the large-scale energy-efficient distributed flexible job shop scheduling problem (EEDFJSP) with two minimized objectives. It proposes an inverse model and adaptive neighborhood search based cooperative optimizer to efficiently solve this problem. Experimental results show that the proposed algorithm performs better than six other state-of-the-art multi-objective optimization algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Kexin Sun, Debin Zheng, Haohao Song, Zhiwen Cheng, Xudong Lang, Weidong Yuan, Jiquan Wang
Summary: This paper proposes an improved hybrid genetic algorithm with variable neighborhood search (HGA-VNS) for addressing the flexible job shop scheduling problem. Experimental results show that the HGA-VNS algorithm is significantly better than other algorithms in terms of performance and can obtain more efficient and economic solutions in practical applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
A. Gnanavelbabu, Rylan H. Caldeira, T. Vaidyanathan
Summary: This study addresses the flexible shop scheduling problem with worker flexibility and uncertain processing times using a simheuristic approach. The results indicate the significant influence of selecting an appropriate probability distribution in uncertain environments on solving the problem.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Fei Luan, Ruitong Li, Shi Qiang Liu, Biao Tang, Sirui Li, Mahmoud Masoud
Summary: This paper introduces an energy-saving flexible job shop scheduling problem and efficiently solves it with an Improved Sparrow Search Algorithm (ISSA) based on optimizing power consumption and processing costs simultaneously.
Article
Operations Research & Management Science
Yiyi Xu, M'hammed Sahnoun, Fouad Ben Abdelaziz, David Baudry
Summary: This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Shahed Mahmud, Ripon K. Chakrabortty, Alireza Abbasi, Michael J. Ryan
Summary: The study introduces an integrated supply chain scheduling model to address highly customized and on-time delivery requirements, enhancing the performance of multi-objective particle swarm optimization. Two new meta-heuristic algorithms are developed with specific search mechanisms and mutation operators for the flexible job shop problem.
APPLIED SOFT COMPUTING
(2022)
Article
Automation & Control Systems
Youjun An, Xiaohui Chen, Kaizhou Gao, Yinghe Li, Lin Zhang
Summary: In this study, a flexible job-shop rescheduling problem with new job insertion and machine preventive maintenance is investigated. An imperfect PM model is established to determine the optimal maintenance plan for each machine, and a multiobjective optimization model is developed to jointly optimize production scheduling and maintenance planning. An improved nondominated sorting genetic algorithm III with adaptive reference vector is proposed to solve the model, and its effectiveness is demonstrated through numerical simulation experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Wenqiang Zhang, Huili Geng, Chen Li, Mitsuo Gen, Guohui Zhang, Miaolei Deng
Summary: Given the increasing severity of ecological issues, sustainable development and green manufacturing have emerged as crucial areas of research and practice. In this paper, a Q-learning-based multi-objective particle swarm optimization (QL-MoPSO) algorithm is proposed to address the distributed flow-shop scheduling problem (DFSP), aiming to minimize makespan and total energy consumption. The algorithm combines the advantages of Q-learning and particle swarm optimization to achieve faster convergence and better performance compared to traditional multi-objective evolutionary algorithms.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Operations Research & Management Science
Moussa Abderrahim, Abdelghani Bekrar, Damien Trentesaux, Nassima Aissani, Karim Bouamrane
Summary: This paper addresses the problem of job assignment in a job-shop manufacturing system and proposes an improved algorithm to minimize the maximum completion time of a job set. Experimental tests demonstrate the effectiveness of the proposed approach.
OPTIMIZATION LETTERS
(2022)
Article
Engineering, Chemical
Xunde Ma, Li Bi, Xiaogang Jiao, Junjie Wang
Summary: This paper proposes a new algorithm called the hybrid coronavirus population immunity optimization algorithm to solve the flexible job shop scheduling problem. The algorithm redefines the encoding and decoding scheme, designs a multi-population update mechanism and collaborative learning method, introduces an adaptive mutation operation, and proposes a knowledge-driven variable neighborhood search strategy. The experimental results show the effectiveness of the hybrid algorithm.
Article
Computer Science, Interdisciplinary Applications
Jinghe Sun, Guohui Zhang, Jiao Lu, Wenqiang Zhang
Summary: This paper addresses a many-objective flexible job-shop scheduling problem and proposes a new hybrid many-objective evolutionary algorithm to solve the problem. The algorithm, using strategies such as tabu search and reference-point based non-dominated sorting selection, effectively improves the quality and diversity of solutions.
COMPUTERS & OPERATIONS RESEARCH
(2021)
