Article
Computer Science, Artificial Intelligence
Biao Zhang, Chao Lu, Lei-lei Meng, Yu-yan Han, Hong-yan Sang, Xu-chu Jiang
Summary: Inspired by a real-world cellular manufacturing system, this study focuses on a reconfigurable distributed flowshop scheduling problem with grouped jobs. A mixed integer linear programming model is developed for small-scaled instances, and a nested variable neighborhood descent algorithm is proposed for larger instances. The proposed algorithm outperforms other state-of-the-art metaheuristics and the math solver CPLEX.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Chemical
Kun Li, Huixin Tian
Summary: This paper proposes a learning and swarm based multiobjective variable neighborhood search (LS-MOVNS) algorithm to solve the multiobjective PFSP problem. LS-MOVNS achieves a balance between exploration and exploitation in a multiobjective environment through integrating swarm-based search with VNS using machine learning techniques.
Article
Computer Science, Artificial Intelligence
Biao Zhang, Quan-Ke Pan, Lei-Lei Meng, Xin-Li Zhang, Ya-Ping Ren, Jun-Qing Li, Xu-Chu Jiang
Summary: This paper introduces the issue of consistent sublots into the hybrid flowshop scheduling problem and develops a mixed integer linear programming (MILP) model and a collaborative variable neighborhood descent algorithm (CVND). The CVND shows excellent performance in local exploitation and global search, with high algorithm efficiency. Results indicate that the CVND has significant advantages in solution quality and relative percentage deviation values.
APPLIED SOFT COMPUTING
(2021)
Article
Chemistry, Multidisciplinary
Jianguo Zheng, Yilin Wang
Summary: A hybrid bat optimization algorithm is proposed in this paper to solve a three-stage distributed assembly permutation flowshop scheduling problem, with the aim of minimizing makespan. By classifying populations, utilizing a selection mechanism, and implementing learning strategies to aid the population in jumping out of local optimal frontiers, the algorithm effectively addresses the trade-offs between convergence, diversity, exploration, and mining capacity. The simulation results show that the proposed algorithm outperforms other metaheuristic algorithms in solving the DAPFSP.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Yuan-Zhen Li, Quan-Ke Pan, Ruben Ruiz, Hong-Yan Sang
Summary: This paper studies the distributed assembly mixed no-idle permutation flowshop scheduling problem (DAMNIPFSP) with the objective of minimizing total tardiness. An improved Iterated Greedy algorithm named RIG (Referenced Iterated Greedy) is proposed, which includes two novel destruction methods, four new reconstruction methods, and six new local search methods based on the characteristics of DAMNIPFSP. Experimental results show that RIG algorithm is a state-of-the-art procedure for DAMNIPFSP with the total tardiness criterion.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Industrial
Biao Han, Quan-Ke Pan, Liang Gao
Summary: This paper addresses a serial distributed permutation flowshop scheduling problem (SDPFSP) inspired by a printed circuit board assembly process. A cooperative iterated greedy (CIG) algorithm is developed to optimize the solution. Problem-specific operators and computational experiments are conducted to verify the effectiveness of the proposed algorithm and its superiority over existing methods.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Kuo-Ching Ying, Pourya Pourhejazy, Chen-Yang Cheng, Ren-Siou Syu
Summary: This research extends the distributed assembly permutation flowshop scheduling problem to account for flexible assembly and sequence-independent setup times in a supply chain-like setting. Constructive heuristic and customised metaheuristic algorithms are proposed to solve this emerging scheduling extension, demonstrating higher performance compared to existing algorithms.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Li Haoran, Li Xinyu, Gao Liang
Summary: With the development of globalization, distributed manufacturing has become a main mode of manufacturing. This paper addresses a distributed heterogeneous no-wait flowshop scheduling problem (DHNWFSP) and proposes a discrete artificial bee colony algorithm (DABC) to effectively solve it, considering the heterogeneity between factories in distributed flow-shop scheduling for the first time. The proposed DABC achieves the highest-quality solutions in comparison with state-of-art algorithms, as shown by numerical experiments for small and large-scale problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Hesham Alfares, Awsan Mohammed, Mustafa Ghaleb
Summary: This paper introduces models and algorithms for a two-machine scheduling problem with position-dependent job processing times. It aims to minimize overall makespan for two parallel machines through integer linear programming models and heuristic solution methods, producing near-optimal solutions quickly.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yuan-Zhen Li, Quan-Ke Pan, Jun-Qing Li, Liang Gao, M. Fatih Tasgetiren
Summary: This research focuses on distributed permutation flow shop scheduling problem with mixed no-idle constraints, using a mixed-integer linear programming model and an Adaptive Iterated Greedy algorithm with restart strategy. The algorithm shows excellent performance in large-scale experiments.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Korhan Karabulut, Hande Oztop, Damla Kizilay, M. Fatih Tasgetiren, Levent Kandiller
Summary: This paper addresses a distributed permutation flowshop scheduling problem with sequence-dependent setup times. To minimize the maximum completion time among the factories, a new mixed-integer linear programming model and a new constraint programming model are proposed. Additionally, an evolution strategy algorithm is employed to obtain high-quality solutions in a short time. The computational results demonstrate that the proposed algorithm outperforms state-of-the-art metaheuristic algorithms for large instances.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Zi-Qi Zhang, Bin Qian, Rong Hu, Huai-Ping Jin, Ling Wang
Summary: This paper introduces an innovative three-dimensional matrix-cube-based estimation algorithm to solve the DAPFSP problem, which improves computational efficiency through global exploration and local exploitation, achieving significantly better results than existing algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Fernando Luis Rossi, Marcelo Seido Nagano
Summary: The distributed permutation flowshop scheduling problem (DPFSP) has been widely studied due to the complex production systems with mixed no-idle flowshops. Although the issue of identical factories with mixed no-idle flowshop environments has not been explored in literature, new solutions including MILP formulation, constructive heuristic, and iterated greedy algorithms have been proposed. Extensive experiments showed that the proposed methods outperformed existing approaches.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Operations Research & Management Science
Wahiba Jomaa, Mansour Eddaly, Bassem Jarboui
Summary: This paper proposes two variable neighborhood search algorithms to solve the permutation flowshop scheduling problem considering preventive maintenance in the non-resumable case. Computational results demonstrate the high performance of these algorithms compared to other approaches, and it is suggested that the change of initial solutions during the optimization process may improve algorithm performance.
OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Hui Yu, Kai-Zhou Gao, Zhen-Fang Ma, Yu-Xia Pan
Summary: This study focuses on a significant distributed assembly permutation flowshop scheduling problem in practical manufacturing systems. Several meta-heuristics, including artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed to solve the problem. Experimental results demonstrate that the proposed Jaya algorithm with Q-learning-based local search performs well and achieves optimal solutions for the majority of benchmark instances.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Zhicheng Cai, Xiaoping Li, Ruben Ruiz
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2019)
Article
Computer Science, Theory & Methods
Zhicheng Cai, Xiaoping Li, Ruben Ruiz, Qianmu Li
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2018)
Article
Computer Science, Theory & Methods
Long Chen, Xiaoping Li, Ruben Ruiz
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2018)
Article
Computer Science, Theory & Methods
Jie Zhu, Xiaoping Li, Ruben Ruiz, Xiaolong Xu
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2018)
Article
Computer Science, Information Systems
Xiaoping Li, Zhi Yang, Ruben Ruiz, Tian Chen, Shaochun Sui
INFORMATION SCIENCES
(2018)
Article
Management
Ruben Ruiz, Quan-Ke Pan, Bahman Naderi
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2019)
Article
Automation & Control Systems
Yadi Wang, Xiaoping Li, Ruben Ruiz
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Luis Fanjul-Peyro, Ruben Ruiz, Federico Perea
COMPUTERS & OPERATIONS RESEARCH
(2019)
Review
Management
Reza Zanjirani Farahani, Samira Fallah, Ruben Ruiz, Sara Hosseini, Nasrin Asgari
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Article
Management
Consuelo Parreno-Torres, Ramon Alvarez-Valdes, Ruben Ruiz
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Editorial Material
Biochemistry & Molecular Biology
Motoaki Seki
PLANT MOLECULAR BIOLOGY
(2019)
Article
Management
Shunji Tanaka, Kevin Tierney, Consuelo Parreno-Torres, Ramon Alvarez-Valdes, Ruben Ruiz
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Article
Management
Pedro Alfaro-Fernandez, Ruben Ruiz, Federico Pagnozzi, Thomas Stutzle
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2020)
Article
Automation & Control Systems
Haiyan Xu, Xiaoping Li, Ruben Ruiz, Haihong Zhu
Summary: This paper investigates single-machine group scheduling with nonperiodical maintenance and deteriorating effects, proposing batch-based heuristics and an iterated greedy algorithm as solutions. The study proves the NP-hardness of the problem and demonstrates the superiority of the proposed methods through comprehensive computational and statistical analyses.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Yamin Wang, Xiaoping Li, Ruben Ruiz
PROCEEDINGS OF THE 2018 IEEE 22ND INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN ((CSCWD))
(2018)
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)