Article
Computer Science, Artificial Intelligence
Jiang-Ping Huang, Quan-Ke Pan, Liang Gao, Ling Wang
Summary: The IGP algorithm is proposed to tackle the PCBs grouping problem, utilizing a new solution representation and heuristic method to enhance solutions, performing superiorly in experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics
Chenyao Zhang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao
Summary: A distributed blocking flowshop scheduling problem with no buffer and setup time constraints is studied. A mixed integer linear programming model is constructed and verified for correctness. An iterated greedy algorithm is presented to optimize the makespan criterion and collaborative interactions are considered to improve the exploration and exploitation of the algorithm.
Article
Engineering, Multidisciplinary
Bruno de Athayde Prata, Marcelo Seido Nagano
Summary: This study proposes an innovative iterated greedy algorithm for the no-wait permutation flowshop layout problem. The algorithm outperforms five other algorithms in terms of two performance measures.
ENGINEERING OPTIMIZATION
(2022)
Article
Engineering, Industrial
Chin-Chia Wu, Danyu Bai, Xingong Zhang, Shuenn-Ren Cheng, Jia-Cheng Lin, Zong-Lin Wu, Win-Chin Lin
Summary: This study focuses on the customer's order scheduling problem on m parallel machines, taking into account uncertainty factors in real production. It introduces scenario-dependent processing times and due dates, aiming to minimize total tardiness across possible scenarios. Several dominant rules, lower bounds, heuristics, and algorithms are proposed for exact and approximate solutions, with computational results reported.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Victor Fernandez-Viagas, Antonio Costa
Summary: This study introduces two approximate algorithms for solving the single machine scheduling problem, evaluating their effectiveness against both anticipatory and non-anticipatory setup strategies. The computational experience confirms the proposed approaches' effectiveness in comparison to other promising algorithms in related scheduling problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Xue-Lei Jing, Quan-Ke Pan, Liang Gao
Summary: This paper presents two local-search based metaheuristics to solve the DPFSP_UPT problem with uncertain processing times, achieving significantly better solutions than five competing algorithms by utilizing multiple local search methods.
APPLIED SOFT COMPUTING
(2021)
Article
Operations Research & Management Science
Fernando Siqueira de Almeida, Marcelo Seido Nagano
Summary: In this article, the m-machine no-wait flow shop scheduling problem with sequence dependent setup times is addressed. A new heuristic called I G(A) is developed to solve the problem by repeatedly performing a process of destruction and construction of an existing solution. Computational experiments show that I G(A) outperforms the best literature method for similar applications in overall solution quality by about 35%. Therefore, IG(A) is recommended to solve the problem.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Zhi-Yuan Wang, Quan-Ke Pan, Liang Gao, Yu -Long Wang
Summary: This paper proposes a method to solve the distributed flowshop group scheduling problem with sequence-dependent setup time. By establishing a mathematical model and using a two-stage iterated greedy algorithm, this method can effectively address the problem. Experimental results show that the proposed method outperforms other algorithms in terms of the relative deviation index values.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Jingcao Cai, Shejie Lu, Jun Cheng, Lei Wang, Yin Gao, Tielong Tan
Summary: This study investigates the distributed scheduling problem in hybrid flow shops and proposes a collaborative variable neighborhood search algorithm (CVNS) to simultaneously minimize total tardiness and makespan. The algorithm simplifies the problem and defines various neighborhood structures and global search operators. Experimental results validate the advantages of CVNS over the considered problem.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Yuhang Wang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao, Yiping Liu
Summary: The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Three issues of DFGSP, including assigning groups to factories, arranging group sequences in each factory, and scheduling job sequences in each group, need to be solved due to its strong coupling. To solve these problems, a mixed-integer linear programming model is constructed and verified, and two rapid evaluation methods are designed based on group insertion and job insertion. An effective two-stage iterated greedy algorithm (tIGA) is proposed, which includes cooperative neighborhood search strategies and enhanced search strategies to improve the search breadth and depth. Experimental results show that the proposed algorithm outperforms other algorithms in terms of objective values and demonstrates the effectiveness of the proposed tIGA.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Gang Xuan, Win-Chin Lin, Shuenn-Ren Cheng, Wei-Lun Shen, Po-An Pan, Chih-Ling Kuo, Chin-Chia Wu
Summary: In many real-world environments, uncertainties in job processing times and due dates caused by machine breakdowns, worker performance instabilities, working environment changes, and transportation delays are addressed by introducing scenario-dependent processing time and due date concepts. Different algorithms, including exact and heuristic methods, are proposed to minimize job tardiness and improve solution diversity.
Article
Computer Science, Artificial Intelligence
Lucija Ulaga, Marko Durasevic, Domagoj Jakobovic
Summary: This article discusses the importance of making timely scheduling decisions in real-world situations. It explores the possibility of using efficient but simple iterative local search methods to solve scheduling problems. The study finds that simple methods can achieve better results compared to complex metaheuristic algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Management
Ahmed Missaoui, Ruben Ruiz
Summary: Hybrid Flowshop Scheduling Problems (HFS) are realistic machine sequencing models. We propose a new local search procedure IG, which produces state-of-the-art results according to comprehensive computational experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Zhi-Yuan Wang, Quan-Ke Pan, Liang Gao, Xue-Lei Jing, Qing Sun
Summary: This paper addresses a distributed flowshop group robust scheduling problem with uncertain processing times. The proposed cooperative iterated greedy (CIG) algorithm outperforms other competitors in terms of average relative deviation index. The CIG algorithm utilizes modified sequence rules, dummy scenario method, and specific operators to optimize the family and job scheduling sub-problems, and employs a cooperation mechanism to emphasize their coupling relationship.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Eduardo Queiroga, Rian G. S. Pinheiro, Quentin Christ, Anand Subramanian, Artur A. Pessoa
Summary: This paper introduces an iterated local search algorithm for the single machine scheduling problem, which has practical significance in solving batch scheduling problems. The proposed algorithm is compared with dynamic programming methods, showing competitiveness and efficiency through experiments.
JOURNAL OF HEURISTICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Hande Oztop, M. Fatih Tasgetiren, Levent Kandiller, Quan-Ke Pan
Summary: This study addresses the no-idle permutation flowshop scheduling problem (NIPFSP) and proposes MILP and CP models as well as IG_RL and ILS_RL algorithms. The performance of these methods is compared with existing approaches, and the results show that the proposed methods perform well on certain instances.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Jiang-Ping Huang, Quan-Ke Pan, Ponnuthurai Nagaratnam Suganthan
Summary: This article explores the printed circuit board (PCB) grouping problem (PGP) in the electronic assembly industry by presenting a mathematical model and four heuristics based on an iterative scheme. A new solution representation is proposed and experiments show that the presented heuristics outperform those in the literature.
ENGINEERING OPTIMIZATION
(2022)
Article
Computer Science, Artificial Intelligence
Jia-Yang Mao, Quan-Ke Pan, Zhong-Hua Miao, Liang Gao, Shuai Chen
Summary: This paper studies the distributed permutation flowshop scheduling problem with preventive maintenance. It proposes a hash map-based algorithm and demonstrates its effectiveness in optimizing computational efficiency.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Industrial
Ying-Ying Huang, Quan-Ke Pan, Liang Gao
Summary: This paper investigates the distributed permutation flowshop scheduling problem and proposes an effective memetic algorithm (EMA). A constructive heuristic and an initialisation method are used to generate high-quality and diverse initial populations. The EMA uses a new structure of a small iteration nested within a large iteration and includes targeted and flexible local search methods. The experimental results confirm the effectiveness and efficiency of the EMA.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Biao Zhang, Quan-ke Pan, Lei-lei Meng, Xin-li Zhang, Xu-chu Jiang
Summary: Lot streaming is a widely used technique to overlap successive operations. This study addresses the multi-objective hybrid flowshop rescheduling problem with consistent sublots (MOHFRP_CS) and proposes a multi-objective migrating birds optimisation algorithm based on decomposition (MMBO/D). The algorithm decomposes the problem into sub-problems, dynamically adjusts the weights assigned to the sub-problems, and employs a global update strategy. Experimental results demonstrate that MMBO/D outperforms other state-of-the-art multi-objective evolutionary algorithms for the addressed problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Xue-Lei Jing, Quan-Ke Pan, Liang Gao, Ling Wang
Summary: A new scheduling problem involving distributed permutation flowshop scheduling with uncertain processing times and carryover sequence-dependent setup time is addressed. A robust model is established with the makespan criterion, along with the discovery of a counter-intuitive paradox and acceleration methods. An iterated greedy algorithm is proposed to solve the problem, outperforming six competing algorithms in extensive experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhi-Yuan Wang, Quan-Ke Pan, Liang Gao, Yu -Long Wang
Summary: This paper proposes a method to solve the distributed flowshop group scheduling problem with sequence-dependent setup time. By establishing a mathematical model and using a two-stage iterated greedy algorithm, this method can effectively address the problem. Experimental results show that the proposed method outperforms other algorithms in terms of the relative deviation index values.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Ying-Ying Huang, Quan-Ke Pan, Liang Gao, Zhong-Hua Miao, Chen Peng
Summary: This paper proposes a two-phase evolutionary algorithm (TEA) to solve the multi-objective distributed assembly permutation flowshop scheduling problem, achieving high-quality and diverse solutions by simultaneously optimizing two criteria.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Shuai Chen, Quan-Ke Pan, Liang Gao, Zhong-Hua Miao, Chen Peng
Summary: This paper studies an energy-efficient distributed blocking flowshop scheduling problem and proposes a knowledge-based iterated Pareto greedy algorithm (KBIPG) to simultaneously minimize the makespan and total energy consumption. By adjusting machine speeds and designing local intensification methods, the effectiveness of the algorithm is demonstrated.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xuan He, Quan-Ke Pan, Liang Gao, Ling Wang, Ponnuthurai Nagaratnam Suganthan
Summary: This article addresses the flowshop sequence-dependent group scheduling problem (FSDGSP) by considering both production efficiency measures and energy efficiency indicators. A mixed-integer linear programming model and a critical path-based accelerated evaluation method are proposed. A greedy cooperative co-evolutionary algorithm (GCCEA) is designed to explore the solution space, and a random mutation operator and a greedy energy-saving strategy are employed.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Zhongkai Li, Hongyan Sang, Quanke Pan, Kaizhou Gao, Yuyan Han, Junqing Li
Summary: In this article, a dynamic AGV scheduling model is proposed, which includes an aperiodic departure method and a real-time task list update method. The model can reassign AGVs for new tasks and special cases, proving its effectiveness through verification using a discrete invasive weed optimization algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Jianhui Mou, Peiyong Duan, Liang Gao, Quanke Pan, Kaizhou Gao, Amit Kumar Singh
Summary: This study explores the application of biologically inspired Plasticity Neural Network in the industrial intelligent dispatching energy storage system, focusing on the intelligence and fault detection performance of the control system. By implementing a fault diagnosis model based on Deep Belief Network, the study achieves a 100% transmission probability for the constructed intelligent energy storage scheduling system. Compared with other classical algorithm models, the proposed algorithm shows a higher success rate, detection accuracy, lower energy consumption, and more significant detection effect. Therefore, the constructed system has higher real-time performance, more accurate fault detection performance, and significantly better system detection and protection performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin-Rui Tao, Quan-Ke Pan, Hong-Yan Sang, Liang Gao, Ao-Lei Yang, Miao Rong
Summary: This study develops a nondominated sorting genetic algorithm-II (NSGA-II) with Q-learning to address the disturbance factors in the distributed permutation flowshop problem. An iterated greedy algorithm (IG) is proposed to generate an initial solution, and the NSGA-II algorithm is designed to optimize dual-objective problems. The results confirm the high efficiency of the proposed algorithm in solving the rescheduling problem in the distributed permutation flowshop.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yu Du, Jun-qing Li, Xiao-long Chen, Pei-yong Duan, Quan-ke Pan
Summary: This study introduces a hybrid multi-objective optimization algorithm to solve a flexible job shop scheduling problem with time-of-use electricity price constraint, involving machine processing speed, setup time, idle time, and the transportation time between machines. The algorithm combines estimation of distribution algorithm and deep Q-network, with two knowledge-based initialization strategies designed for better performance.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Engineering, Civil
Dongdong Wang, Suying Pan, Jin Zhou, Quanke Pan, Zhonghua Miao, Jiangke Yang
Summary: This paper addresses the distributed event-triggered formation control problem of networked nonholonomic mobile robots (NNMRs) in a leader-follower-based framework. An event-triggered mechanism (ETM) is introduced for the design of the kinematic controller using an auxiliary reference vector and combined with backstepping technique and sliding mode approach to propose a unified integrated dynamic controller. The designed event-triggered condition is derived based on the local communication among robots utilizing the nonholonomic property of NNMRs, ensuring the exclusion of Zeno behavior before achieving the desired formation configuration. The theoretical results are validated through simulation analysis and implemented on a real-time physical NNMR experimental platform, demonstrating the key feature of the ETM integral formation scheme in effectively reducing communication resource usage and energy consumption while maintaining comparable performance to the conventional periodic communication mechanism (PCM).
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(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)