Review
Management
Janis S. Neufeld, Sven Schulz, Udo Buscher
Summary: This article presents the research progress on multi-objective hybrid flow shop scheduling problems, identifies important features in optimization algorithms, and provides a framework and test instances for evaluating algorithm suitability. The article is of great theoretical and practical significance for solving multi-objective optimization problems.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Engineering, Industrial
Weibo Ren, Jingqian Wen, Yan Yan, Yaoguang Hu, Yu Guan, Jinliang Li
Summary: This paper proposes a methodology for multi-objective optimisation of energy-aware flexible job-shop scheduling by developing a mixed integrated mathematical model and heuristic algorithm. Numerical examples demonstrate the effectiveness and performance of the method in achieving energy awareness in manufacturing systems.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Peize Li, Qiang Xue, Ziteng Zhang, Jian Chen, Dequn Zhou
Summary: This paper studies a new energy-efficient hybrid flow shop scheduling problem (EEHFSP) with uniform machines to minimize total tardiness, total energy cost, and carbon trading cost. The proposed Q-learning and general variable neighborhood search (GVNS) driven non-dominated sorting genetic algorithm II (QVNS-NSGA-II) outperforms other algorithms in terms of quantity, quality of Pareto solutions, and computational efficiency. The proposed approach can be applied to improve sustainability and productivity for hybrid flow shop manufacturers.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Alireza Goli, Ali Ala, Mostafa Hajiaghaei-Keshteli
Summary: This study investigates the energy awareness of non-permutation flow-shop scheduling and lot-sizing problems and proposes a hybrid algorithm to optimize them. The proposed algorithm is validated and evaluated for efficiency using mathematical modeling and meta-heuristic algorithms, showing it can find optimal solutions and outperform other algorithms in terms of time and quality.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Derya Deliktas, Ender Ozcan, Ozden Ustun, Orhan Torkul
Summary: The study introduces evolutionary algorithms to solve the bi-objective flexible job shop scheduling problem and compares their performance across various configurations. The transgenerational memetic algorithm using weighted sum method outperforms others and achieves the best-known results for almost all instances of bi-objective flexible job shop cell scheduling.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Jiaxuan Shi, Mingzhou Chen, Yumin Ma, Fei Qiao
Summary: The boredom-aware dual-resource constrained flexible job shop scheduling problem is investigated in this study to address the negative effects caused by improper task allocation and workers' boredom. A bi-level lexicographic model is established, and a two-stage multi-objective particle swarm optimization algorithm is proposed. Experimental results validate the effectiveness of the model and algorithm.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Enda Jiang, Ling Wang, Jingjing Wang
Summary: This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks (EADHFSPMT) and proposes a Novel Multi-Objective Evolutionary Algorithm based on Decomposition (NMOEA/D) to solve it. The algorithm includes special designs such as decoding scheme, local intensification operators, and dynamic adjustment strategy for weight vectors, which have been proven effective through extensive computational experiments. The NMOEA/D has superior performances compared to existing algorithms, as verified by statistical comparisons.
TSINGHUA SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Industrial
Wenkang Zhang, Yufan Zheng, Rafiq Ahmad
Summary: This work focuses on a multi-objective scheduling problem in a remanufacturing system that aims to reduce completion time and energy usage by determining the allocation/sequence of disassembly/reassembly jobs and the operation sequencing and workstation assignment of reprocessing jobs. A multi-objective mixed-integer programming model is developed, and an improved grey wolf optimization algorithm is introduced to achieve efficient scheduling. Experimental results demonstrate that the developed algorithm outperforms other existing methods in terms of solution accuracy, computing speed, solution stability, and convergence performance. Furthermore, a case study shows the algorithm's superiority in solving real-world remanufacturing scheduling problems in terms of energy usage and time cost reduction.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Youjun An, Xiaohui Chen, Yinghe Li, Yaoyao Han, Ji Zhang, Haohao Shi
Summary: With the proposal of an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm, this paper aims to solve the (hybrid) multi objective flexible job-shop scheduling problem. By introducing the V-dominance principle, HVNS structure and ESS strategy, the algorithm's performance has been enhanced and shows better performance compared to other intelligent algorithms.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Minghao Qu, Ying Zuo, Feng Xiang, Fei Tao
Summary: This paper proposes a many-objective optimization model to improve sustainability in the shop floor by considering makespan, total energy consumption, and other indicators. An improved algorithm is used to find the optimal or near-optimal solutions, and a real-life case study is conducted to validate the effectiveness of the model and algorithm.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Rui Li, Wenyin Gong, Chao Lu
Summary: This study addresses the multi-objective flexible job shop scheduling problem and proposes a hybrid self-adaptive multi-objective evolutionary algorithm based on decomposition (HPEA) to solve it. The algorithm shows better performance in solving the problem by utilizing problem-specific initial rules, local search methods, solution selection method, and parameter selection strategy.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Deming Lei, Bin Su
Summary: This study proposes a multi-class teaching-learning-based optimization (MTLBO) to minimize makespan and maximum tardiness simultaneously for the distributed hybrid flow shop scheduling problem (DHFSP) with sequence-dependent setup times. A two-string representation is adopted, and s classes are formed to improve search efficiency with a reward and punishment mechanism. Class evaluation, two teacher phases, and one learner phase are introduced for the evolution of each class. An elimination process is applied to the worst class to avoid wasting computing resources. Computational results from experiments demonstrate that MTLBO is a highly competitive method for DHFSP.
KNOWLEDGE-BASED SYSTEMS
(2023)
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
Lixin Wei, Jinxian He, Zeyin Guo, Ziyu Hu
Summary: This paper addresses the issue of infeasible scheduling scheme caused by dynamic events such as machine breakdown during workshop production. It establishes a mathematical model for a multi-objective dynamic flexible job shop scheduling problem with machine breakdown and proposes a multi-objective migrating birds optimization algorithm based on game theory. To solve the problem of difficult weight determination in weighted multi-objective optimization, game theory is introduced to balance the Pareto optimality and fairness between production efficiency and stability. The algorithm designs neighborhood operators based on path relinking and machine age to improve search ability. A multiple similarity measure method is also designed to select and replace solutions based on the attributes of multi-objective problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Jesus Para, Javier Del Ser, Antonio J. Nebro
Summary: This paper provides an in-depth review and analysis of multi-objective job shop scheduling optimization, with a focus on minimizing energy consumption. Through performance comparisons across various algorithms and synthetic test instances, the paper offers insights for good practices and further improvements in this vibrant research area.
APPLIED SCIENCES-BASEL
(2022)
Article
Management
Janis S. Neufeld, Martin Scheffler, Felix Tamke, Kirsten Hoffmann, Udo Buscher
Summary: The crew scheduling problem with attendance rates is important in regional passenger rail transport in Germany, but discussions on this topic are relatively rare in literature. A novel hybrid column generation approach that integrates realistic requirements for successful application has been proposed for solving this real-world problem in railway passenger transport. The effectiveness of the proposed algorithm is proven by a comprehensive computational study using real-world instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Maria Beranek, Udo Buscher
Summary: The study shows that ignoring the demand of the secondary market can lead to unrealistic results. In some cases, it is advantageous not to serve the secondary market and to concentrate the sales process on primary customers.
APPLIED MATHEMATICAL MODELLING
(2021)
Review
Management
Janis S. Neufeld, Sven Schulz, Udo Buscher
Summary: This article presents the research progress on multi-objective hybrid flow shop scheduling problems, identifies important features in optimization algorithms, and provides a framework and test instances for evaluating algorithm suitability. The article is of great theoretical and practical significance for solving multi-objective optimization problems.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Review
Computer Science, Interdisciplinary Applications
Eduardo Alarcon-Gerbier, Udo Buscher
Summary: Supply chain planning is typically based on fixed production locations. However, with the development of new technologies and modularization, mobile and modular units have emerged that can change locations quickly and at a low cost. This has generated interest in studying more flexible network positioning. By reviewing and classifying 125 research papers, this study provides a comprehensive overview of mathematical formulations, solution approaches, objectives, and main characteristics. Additionally, case studies presented in the literature are categorized and research trends for further development in this field are identified.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Benedikt Zipfel, Janis Neufeld, Udo Buscher
Summary: This paper studies the customer order scheduling problem in the context of additive manufacturing. It proposes a mixed-integer programming model that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed. The efficiency of the proposed heuristic approach is evaluated using comprehensive test data, focusing on minimizing the total weighted tardiness of orders, and the importance of the considered order-related objective is demonstrated through qualitative analysis.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Xiaolu Liu, Wenhan Shao, Jiaming Chen, Zhipeng Lu, Fred Glover, Junwen Ding
Summary: In this study, a multi-start local search algorithm (MLS) is proposed to optimize clustering results by reducing the dependence on the size and shape of ideal clusters, and defining a new objective function. This algorithm uses merge and split operations to optimize the objective function and make the iterative process insensitive to the initial solution. Experimental results demonstrate that MLS outperforms traditional centroid-based clustering algorithms in both solution quality and computational efficiency, and it is competitive to other reference algorithms such as spectral, density, and geometric based clustering algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Felix Tamke, Udo Buscher
Summary: This paper focuses on the vehicle routing problem with drones and drone speed selection (VRPD-DSS). A comprehensive mixed-integer problem is formulated to minimize operational costs, considering fuel consumption costs, labor costs, and energy costs. The study reveals that selecting different flight speeds for drones based on their energy consumption can significantly reduce operational costs and decrease overall delivery time compared to truck-only delivery. Additionally, the additional energy costs of the drone are largely negligible.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Tiancheng Zhang, Zhipeng Lu, Junwen Ding
Summary: This article proposes a guiding solution based local search method (GSLS) to solve the obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem in the physical design of integrated circuits. The GSLS algorithm outperforms the state-of-the-art algorithms in terms of solution quality and computational efficiency, achieving optimal solutions for a significant number of benchmark instances.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhipeng Lu, Yan Li, Zhouxing Su, Yaozhong Zhao, Junwen Ding
Summary: Due to new packaging schemes like MPW, the demand for rectilinear block placement is increasing. This paper presents an effective scoring-based metaheuristic algorithm (SMA) for the MPW problem, considering the characteristics of L/T/U-shaped blocks and different orientations in the scoring strategies. Computational experiments demonstrate that SMA achieves an average filling ratio of around 95%, showing its effectiveness.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Engineering, Industrial
Maria Beranek, Udo Buscher
Summary: This study builds on a two-period game-theoretic model to explore strategies for increasing sustainability in supply chains. The findings demonstrate that cooperation between players leads to the best results, both economically and ecologically.
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL
(2023)
Proceedings Paper
Automation & Control Systems
Julius Hoffmann, Janis S. Neufeld, Udo Buscher
Summary: In this study, two iterated greedy algorithms are proposed to minimize the total completion time of customer orders in a dedicated machine environment. The algorithms make use of problem properties and apply a new local search and construction function. Computational experiments demonstrate the superiority of the algorithms over two state-of-the-art approaches in solving the customer order scheduling problem with dedicated machines.
Proceedings Paper
Automation & Control Systems
Eduardo Alarcon-Gerbier, Benedikt Zipfel, Udo Buscher
Summary: This article addresses the development of small-scale, mobile factories equipped with 3D printing technologies. A mixed-integer program model and a heuristic method are proposed to minimize relocation, transport, and tardiness costs.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Steffen Rudert, Udo Buscher
Summary: This paper investigates the problem of internal returns resulting from imperfect production. Three heuristics are proposed to minimize the costs of new production and rework while fully satisfying demand. The results show that the developed heuristics are highly competitive compared to a commercial solver.
COMPUTATIONAL LOGISTICS (ICCL 2022)
(2022)
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)
