Article
Computer Science, Artificial Intelligence
Shuxian Sun, Huchang Liao
Summary: This study proposes a value-driven multiple criteria sorting procedure that considers uncertain assignment examples with probabilistic linguistic information. By introducing probability linguistic term set and weighted additive value function, decision-makers' preferences can be more comprehensively reflected.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Computer Science, Artificial Intelligence
Ali Ozarslan, Gulsah Karakaya
Summary: In this study, interactive approaches are developed to sort alternatives evaluated on multiple criteria. Mathematical models are used to define the possible category ranges of alternatives iteratively, assuming the decision maker's preferences are consistent with an additive utility function. Simulation-based and model-based parameter generation methods are proposed to assign the alternatives to categories. A practical approach is developed to solve the incompatibility problem of the randomly generated parameters. The performance of the approaches is tested on different problems, showing that relative entropy-based alternative selection methods can effectively reduce the assessment burden of the decision maker.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Computer Science, Interdisciplinary Applications
Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao, Xiaoxin Mao
Summary: This paper introduces a novel preference learning method for multiple criteria sorting problems. It utilizes convex quadratic programming to construct a value-based preference model, extending the applicability of decision analysis methods. Experimental results demonstrate that the proposed method shows promising performance in dealing with complex decision problems.
INFORMS JOURNAL ON COMPUTING
(2021)
Article
Management
Krzysztof Martyn, Milosz Kadzinski
Summary: This study proposes preference learning algorithms for inferring the parameters of a sorting model from large sets of examples, with application in Multiple Criteria Decision Analysis. By utilizing artificial neural networks and gradient descent optimization algorithms, the study achieves high predictive accuracy.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Milosz Kadzinski, Krzysztof Ciomek
Summary: This study explores the interactive elicitation of holistic preference information for multiple criteria sorting and introduces several active learning strategies for selecting alternatives. Experimental results demonstrate that heuristic strategies based on current classification analysis can achieve competitive results compared to strategies predicting future stages.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Operations Research & Management Science
Jindong Qin, Yingying Liang, Luis Martinez, Alessio Ishizaka, Witold Pedrycz
Summary: This paper introduces a novel multiple criteria sorting method, ORESTE-SORT, and its main characteristics and properties. With the introduction of the assignment rule driven by attitudes, this method can effectively handle preference relationships and sort port group competitiveness.
ANNALS OF OPERATIONS RESEARCH
(2023)
Review
Computer Science, Artificial Intelligence
Pavel Anselmo Alvarez, Alessio Ishizaka, Luis Martinez
Summary: Multi-Criteria Decision Making (MCDM) is a complex process that aims to support decision makers in making more effective and consistent decisions. This paper presents a systematic review of MCDM sorting methods based on 30 years of research, revealing that methodological development in this area is still growing. The study also highlights the applied methods' trends and the spectrum of application areas addressed, providing insights for further research directions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Jindong Qin, Yingying Zeng, Yujie Zhou
Summary: This study proposes a novel sorting model based on DEA and BWM methods to address multi-criteria sorting problems in ecological risk assessment. The Context-Dependent DEASort method positions decision-making units into diverse categories under uncertain circumstances, taking into account experts' preferences for a more flexible and reasonable sorting solution. Additionally, a common weight set model is introduced to evaluate the attractiveness and progress of each DMU efficiently.
INFORMATION SCIENCES
(2021)
Article
Operations Research & Management Science
Khaled Belahcene, Vincent Mousseau, Wassila Ouerdane, Marc Pirlot, Olivier Sobrie
Summary: Multiple criteria sorting methods categorize objects based on their attribute values and the ordering of categories is determined by the scale of attributes. This survey reviews the literature on multiple criteria sorting methods and focuses on the underlying models. The proposal is divided into two parts, with Part I discussing two main models (UTADIS and Electre Tri) and providing a structured overview of multiple criteria sorting models and methods for determining their parameters or learning based on assignment examples. Part II, to be published in a forthcoming issue, aims to provide a theoretical perspective of the field and discuss issues related to imperfect or insufficient information.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Geriatrics & Gerontology
Lingyu Zhang, Yanbing Hou, Qianqian Wei, Ruwei Ou, Kuncheng Liu, Junyu Lin, Tianmi Yang, Yi Xiao, Bi Zhao, Huifang Shang
Summary: The study assessed and compared the diagnostic utility of the new Movement Disorder Society (MDS) MSA criteria with the 2008 MSA criteria. The results showed that the sensitivity of the MDS MSA criteria was significantly higher than that of the 2008 MSA criteria, while the specificities were not significantly different. Therefore, the MDS MSA criteria can be considered as a useful diagnostic tool for clinical practice.
FRONTIERS IN AGING NEUROSCIENCE
(2023)
Article
Management
Meng Zhao, Zeshui Xu, Wenxian Zhao, Daiwei Wei
Summary: This paper introduces a two-stage utility function with aspiration based on closeness degree, which is suitable for solving Multiple Experts Multiple Criteria Decision Making (MEMCDM) problems in mass data and uncertain linguistic environment. The paper extensively discusses the closeness degree of distribution, various types of utility functions, and proposes an approach for evaluating MEMCDM problems.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2022)
Article
Operations Research & Management Science
Khaled Belahcene, Vincent Mousseau, Wassila Ouerdane, Marc Pirlot, Olivier Sobrie
Summary: Multiple criteria sorting methods categorize and rank objects based on their vector of attribute values. The categorization is ordered and the ranking of objects is monotonic with respect to the underlying order of attribute scales (criteria). This article provides a theoretical perspective of the field by describing a general framework for multiple criteria sorting models and positioning existing models within this framework. It also addresses issues related to imperfect or insufficient information and discusses questions that arise in the final phase of a decision aiding process, such as explaining recommendations or suggesting ways to improve object assignments.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Ashkan Ayough, Setareh Boshrouei Shargh, Behrooz Khorshidvand
Summary: Supplier selection is a major challenge for manufacturing companies due to increasing competition. This research proposes an integrated Multi-Criteria Decision-Making (MCDM) method for supplier selection based on Base Criterion (BC) and Utility Additive (UA) methods. The model calculates criteria weights and alternative rankings simultaneously, resulting in fewer pairwise comparisons and increased consistency.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Felipe Barrera, Marina Segura, Concepcion Maroto
Summary: This research designs and validates a multicriteria model to support decision making for customer segmentation in a business to business context. The model extends the transactional customer behavior by considering factors such as customer collaboration and growth rates. The proposed multicriteria system can segment thousands of customers into ordered categories by preferences, providing more homogeneous, robust, and understandable segments compared to alternative methods. This contributes significantly to automating supply chain management and offering detailed analysis tools for decision making.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Operations Research & Management Science
Ozgur Ozpeynirci, Selin Ozpeynirci, Vincent Mousseau
Summary: This paper investigates the inverse multiple criteria sorting problem and solves it by decomposing the problem into two phases. In the first phase, a preprocessing step calculates the minimum cost required for each object-class pair. In the second phase, an interactive assignment model is generated to analyze the trade-offs between classification and budget. The paper also presents a modified version of a regret-based approach, reducing computation time by using a mixed integer program.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Management
Muberra Ozmen, Gulsah Karakaya, Murat Koksalan
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2018)
Article
Computer Science, Interdisciplinary Applications
Banu Lokman, Murat Koksalan, Pekka J. Korhonen, Jyrki Wallenius
COMPUTERS & OPERATIONS RESEARCH
(2018)
Article
Management
G. Karakaya, M. Koksalan, S. D. Ahipasaoglu
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2018)
Article
Management
Gokhan Ceyhan, Murat Koksalan, Banu Lokman
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Article
Management
Gulsah Karakaya, Murat Koksalan
Summary: The study develops interactive approaches to help decision makers find satisfactory alternatives in a quasiconvex preference function environment, continuously searching alternative sets and estimating the decision maker's preference function to converge on the preferred alternative. Testing on multi-item, multi-round auction problems shows the approaches work well in obtaining solutions with preferred preference function values and minimal preference information needed.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2021)
Article
Management
Diclehan Tezcaner Ozturk, Murat Koksalan
Summary: The study introduces interactive algorithms to find a strict total order for a set of discrete alternatives based on linear and quasiconcave value functions. The algorithms iteratively update preference matrix to elicit preference information from the decision maker until termination conditions are met, showing efficient convergence to the exact total order for both value functions.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2021)
Article
Engineering, Industrial
Gulsah Karakaya, Murat Koksalan
Article
Computer Science, Interdisciplinary Applications
Erdi Dasdemir, Murat Koksalan, Diclehan Tezcaner Ozturk
COMPUTERS & OPERATIONS RESEARCH
(2020)
Article
Operations Research & Management Science
Selin Ozpeynirci, Ozgur Ozpeynirci, Vincent Mousseau
Summary: In this study, an interactive approach is proposed for a resource allocation problem, aimed at helping the decision maker generate a portfolio with high return and balanced resource distribution through pairwise comparisons among alternative portfolios. The algorithm's incumbent solution is found to be either equal or very close to the best solution in the majority of instances.
Article
Operations Research & Management Science
Ozgur Ozpeynirci, Selin Ozpeynirci, Vincent Mousseau
Summary: This paper investigates the inverse multiple criteria sorting problem and solves it by decomposing the problem into two phases. In the first phase, a preprocessing step calculates the minimum cost required for each object-class pair. In the second phase, an interactive assignment model is generated to analyze the trade-offs between classification and budget. The paper also presents a modified version of a regret-based approach, reducing computation time by using a mixed integer program.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Selin Ozpeynirci, Ozgur Ozpeynirci, Vincent Mousseau
Summary: This article focuses on a resource allocation problem, where the decision maker needs to select a group of projects to create a portfolio that maximizes the total benefit while balancing the budget across different categories. The augmented epsilon-constraint method and variable neighborhood search algorithm are proposed to find the nondominated solutions and estimate the nondominated frontier accurately, respectively. Computational experiments are conducted to validate the proposed methods.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Baybars Ibroska, Selin Ozpeynirci, Ozgur Ozpeynirci
Summary: The use of unmanned aerial vehicles plays a key role in the development of new technologies in various fields. With the growth of e-commerce and the impact of the pandemic, cargo transportation has become more complex. By utilizing unmanned aerial vehicles in cargo transportation and reducing reliance on trucks through proper planning, some of the challenges can be addressed.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Multidisciplinary Sciences
Burak Gokgur, Selin Ozpeynirci
Summary: This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. Mathematical programming and constraint programming models are proposed, along with two heuristic approaches. The constraint programming models perform well in solution quality and execution time, while the greedy approach shows potential in reaching the optimal solution. The study demonstrates the effectiveness of the proposed method in manufacturing settings requiring sudden adjustments.
GAZI UNIVERSITY JOURNAL OF SCIENCE
(2022)
Article
Management
Ozgur Ozpeynirci, Selin Ozpeynirci, Vincent Mousseau
Summary: The article discusses the determination of action sets in inverse multiple criteria sorting problems and the trade-off between cost and classification. More actions may result in better classifications, and the most preferred solution is determined based on the decision maker's preferences.
JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS
(2021)
Article
Operations Research & Management Science
Vincent Mousseau, Ozgur Ozpeynirci, Selin Ozpeynirci
ANNALS OF OPERATIONS RESEARCH
(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)
