Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Jinlong Zhao, Ling Wang, Jianxin Tang
Summary: This paper proposes an optimal block knowledge-driven backtracking search algorithm (BKBSA) to solve the distributed assembly No-wait flow shop scheduling problem (DANWFSP), with constructive heuristics for generating initial solutions, block-shifting based on knowledge, and feedback control using similarity between candidate solutions. Additionally, a VND algorithm is proposed for further optimization. Test results on large-scale and small-scale instances show that BKBSA is an effective algorithm for solving DANWFSP.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Victor M. Valenzuela-Alcaraz, M. A. Cosio-Leon, A. Danisa Romero-Ocano, Carlos A. Brizuela
Summary: The study proposes a cooperative coevolutionary algorithm for solving the no-wait job shop scheduling problem. The algorithm evolves permutations and binary chains simultaneously to optimize sequencing and timetabling decisions, and includes one-step perturbation mechanisms to improve solution quality. Experimental results show that the proposed algorithm produces competitive results and obtains new best values for some instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
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
Automation & Control Systems
Fuqing Zhao, Zesong Xu, Ling Wang, Ningning Zhu, Tianpeng Xu, J. Jonrinaldi
Summary: This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP) and proposes a population-based iterated greedy algorithm (PBIGA) to address the problem. The PBIGA is shown to be effective and outperforms state-of-the-art algorithms in terms of minimizing total flowtime. Experimental results on large-scale benchmark instances demonstrate the superiority of the proposed PBIGA.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
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
Engineering, Industrial
Christos Koulamas, George J. Kyparisis
Summary: The study focuses on the no-wait flow shop scheduling problem with a rejection option and presents polynomial-time algorithms to minimize different objective functions efficiently.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Management
Czeslaw Smutnicki, Jaroslaw Pempera, Grzegorz Bocewicz, Zbigniew Banaszak
Summary: This paper investigates the problem of cyclic scheduling in a manufacturing system, considering the flow of jobs with identical technological routes, no-wait constraints, and missing operations. The problem is decomposed into two sub-problems, and alternative methods are provided for finding the minimal cycle time and optimal processing order of jobs. A metaheuristic approach is used to solve the latter sub-problem. Experimental examination demonstrates the efficiency and quality of the proposed algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Shahed Mahmud, Alireza Abbasi, Ripon K. Chakrabortty, Michael J. Ryan
Summary: This study develops an algorithm that combines multi-operator based differential evolution and communication strategy for solving the job shop scheduling problem, and further optimizes the best solution order with sequential Tabu Search to maintain population diversity, avoid premature convergence, and improve convergence speed.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Shu Luo, Linxuan Zhang, Yushun Fan
Summary: In this study, a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named HMAPPO is proposed to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem. The method consists of objective agent, job agent, and machine agent, with various job selection rules and machine assignment rules designed to achieve temporary objectives at each rescheduling point. Extensive numerical experiments have confirmed the effectiveness and superiority of HMAPPO compared to other known dynamic scheduling methods.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Industrial
Jinsheng Gao, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang
Summary: This paper addresses the no-wait job shop scheduling problem with due date and subcontracting cost constraints, introducing two mathmatical models to find solutions for the problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Automation & Control Systems
Xinnian Wang, Keyi Xing, Yanxiang Feng, Yunchao Wu
Summary: This study addresses the scheduling problem of deadlock-prone flexible manufacturing systems subject to no-wait constraints for the first time, and develops a new scheduling algorithm based on the place-timed Petri net (PN) model and heuristic search. The problem is solved through timetabling and sequencing, using a controlled PN model for timetabling and a hybrid heuristic search approach for sequencing.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
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
Jin Xie, Xinyu Li, Liang Gao, Lin Gui
Summary: This paper proposes a hybrid algorithm combining genetic algorithm and tabu search to solve job shop scheduling problems. The evaluation of famous benchmark instances shows that the algorithm outperforms other methods in terms of computational efficiency and solution quality.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Sihan Wang, Xinyu Li, Qihao Liu
Summary: This paper proposes a genetic Tabu search algorithm with neighborhood clipping for solving the job shop scheduling problem. Experimental results demonstrate that the proposed algorithm outperforms other competitive algorithms.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Engineering, Industrial
Jin Xie, Xinyu Li, Liang Gao, Lin Gui
Summary: This paper focuses on the role of neighbourhood structures in solving the job shop scheduling problem (JSP) and proposes a new N8 neighbourhood structure. Experimental results show that the N8 structure is more effective and efficient in solving JSP compared to other neighbourhood structures.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)