Relevant bibliographies by topics / Fair combinatorial optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Fair combinatorial optimization'

Author: Grafiati
Published: 7 September 2024
Last updated: 31 July 2025

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Journal articles on the topic "Fair combinatorial optimization"

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Bourdache, Nadjet, and Patrice Perny. "Active Preference Learning Based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7741–48. http://dx.doi.org/10.1609/aaai.v33i01.33017741.

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We consider the problem of actively eliciting preferences from a Decision Maker supervising a collective decision process in the context of fair multiagent combinatorial optimization. Individual preferences are supposed to be known and represented by linear utility functions defined on a combinatorial domain and the social utility is defined as a generalized Gini Social evaluation Function (GSF) for the sake of fairness. The GSF is a non-linear aggregation function parameterized by weighting coefficients which allow a fine control of the equity requirement in the aggregation of individual util
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Wang, Kai, Haoyu Liu, Zhipeng Hu, et al. "EnMatch: Matchmaking for Better Player Engagement via Neural Combinatorial Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9098–106. http://dx.doi.org/10.1609/aaai.v38i8.28760.

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Matchmaking is a core task in e-sports and online games, as it contributes to player engagement and further influences the game's lifecycle. Previous methods focus on creating fair games at all times. They divide players into different tiers based on skill levels and only select players from the same tier for each game. Though this strategy can ensure fair matchmaking, it is not always good for player engagement. In this paper, we propose a novel Engagement-oriented Matchmaking (EnMatch) framework to ensure fair games and simultaneously enhance player engagement. Two main issues need to be add
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MOULIN, HERVÉ. "COST SHARING IN NETWORKS: SOME OPEN QUESTIONS." International Game Theory Review 15, no. 02 (2013): 1340001. http://dx.doi.org/10.1142/s021919891340001x.

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The fertile application of cooperative game techniques to cost sharing problems on networks has so far concentrated on the Stand Alone core test of fairness and/or stability, and ignored many combinatorial optimization problems where this core can be empty. I submit there is much room for an axiomatic discussion of fair division in the latter problems, where Stand Alone objections are not implementable. But the computational complexity of optimal solutions is still a very severe obstacle to this approach.
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Adubi, Stephen A., Olufunke O. Oladipupo, and Oludayo O. Olugbara. "Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems." Algorithms 15, no. 11 (2022): 405. http://dx.doi.org/10.3390/a15110405.

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Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based i
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Sue, Jing Ren, Teong Chee Chuah, and Ying Loong Lee. "Binary Particle Swarm Optimization for Fair User Association in Network Slicing-Enabled Heterogeneous O-RANs." Journal of Engineering Technology and Applied Physics 6, no. 2 (2024): 16–24. http://dx.doi.org/10.33093/jetap.2024.6.2.3.

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The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for a
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Maleš, Uroš, Dušan Ramljak, Tatjana Jakšić Krüger, Tatjana Davidović, Dragutin Ostojić, and Abhay Haridas. "Controlling the Difficulty of Combinatorial Optimization Problems for Fair Proof-of-Useful-Work-Based Blockchain Consensus Protocol." Symmetry 15, no. 1 (2023): 140. http://dx.doi.org/10.3390/sym15010140.

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The wide range of Blockchain (BC) applications and BC’s ubiquity come from the fact that BC, as a collection of records linked to each other, is strongly resistant to alteration, protected using cryptography, and maintained autonomously. All these benefits come with a cost, which in BC is expressed by a very high use of energy needed to execute consensus protocols. Traditionally, consensus protocols based on Proof-of-Work (PoW) ensure fairness, but are not very useful. The paradigm proposed in the recent literature, known as Proof-of-Useful-Work (PoUW), assumes the completion of additional use
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Wang, Zhenzhong, Qingyuan Zeng, Wanyu Lin, Min Jiang, and Kay Chen Tan. "Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21690–98. http://dx.doi.org/10.1609/aaai.v38i19.30168.

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A plethora of fair graph neural networks (GNNs) have been proposed to promote algorithmic fairness for high-stake real-life contexts. Meanwhile, explainability is generally proposed to help machine learning practitioners debug models by providing human-understandable explanations. However, seldom work on explainability is made to generate explanations for fairness diagnosis in GNNs. From the explainability perspective, this paper explores the problem of what subgraph patterns cause the biased behavior of GNNs, and what actions could practitioners take to rectify the bias? By answering the two
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Rokbani, Nizar, Pavel Kromer, Ikram Twir, and Adel M. Alimi. "A Hybrid Hierarchical Heuristic-ACO With Local Search Applied to Travelling Salesman Problem, AS-FA-Ls." International Journal of System Dynamics Applications 9, no. 3 (2020): 58–73. http://dx.doi.org/10.4018/ijsda.2020070104.

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The combinatorial optimization problem is attracting research because they have a wide variety of applications ranging from route planning and supply chain optimization to industrial scheduling and the IoT. Solving such problems using heuristics and bio-inspired techniques is an alternative to exact solutions offering acceptable solutions at fair computational costs. In this article, a new hierarchical hybrid method is proposed as a hybridization of Ant Colony Optimization (ACO), Firefly Algorithm (FA), and local search (AS-FA-Ls). The proposed methods are compared to similar techniques on the
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Kisel, Ivan, Robin Lakos та Gianna Zischka. "Deep-Learning-Based Optimization of the Signal/Background Ratio for Λ Particles in the CBM Experiment at FAIR". Algorithms 18, № 4 (2025): 229. https://doi.org/10.3390/a18040229.

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Machine learning algorithms have become essential tools in modern physics experiments, enabling the precise and efficient analysis of large-scale experimental data. The Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) demands innovative methods for processing the vast data volumes generated at high collision rates of up to 10 MHz. This study presents a deep-learning-based approach to enhance the signal/background (S/B) ratio for Λ particles within the Kalman Filter (KF) Particle Finder framework. Using the Artificial Neural Networks for First L
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Ostojić, Dragutin, Dušan Ramljak, Andrija Urošević, et al. "Systematic Literature Review of Optimization Algorithms for PCmax Problem||." Symmetry 17, no. 2 (2025): 178. https://doi.org/10.3390/sym17020178.

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In the era of open data and open science, it is important that, before announcing their new results, authors consider all previous studies and ensure that they have competitive material worth publishing. To save time, it is popular to replace the exhaustive search of online databases with the utilization of generative Artificial Intelligence (AI). However, especially for problems in niche domains, generative AI results may not be precise enough and sometimes can even be misleading. A typical example is P||Cmax, an important scheduling problem studied mainly in a wider context of parallel machi
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Dissertations / Theses on the topic "Fair combinatorial optimization"

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Vo, Thi Quynh Trang. "Algorithms and Machine Learning for fair and classical combinatorial optimization." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0035.

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L'optimisation combinatoire est un domaine des mathématiques dans lequel un problème consiste à trouver une solution optimale dans un ensemble fini d'objets. Elle a des applications cruciales dans de nombreux domaines. Le branch-and-cut est l'un des algorithmes les plus utilisés pour résoudre exactement des problèmes d'optimisation combinatoire. Dans cette thèse, nous nous concentrons sur les aspects informatiques du branch-and-cut et plus particulièrement, sur deux aspects importants de l'optimisation combinatoire: l'équité des solutions et l'intégration de l'apprentissage automatique. Dans l
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Gliesch, Alex Zoch. "A genetic algorithm for fair land allocation." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174950.

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O objetivo de projetos de reforma agrária é redistribuir terras de grandes latifúndios para terrenos menores, com destino à agricultura familiar. Um dos principais problemas do Instituto Nacional de Colonização e Reforma Agrária (INCRA) é subdividir uma parcela grande de terra em lotes menores que são balanceados com relação a certos atributos. Este problema é difícil por que precisa considerar diversas restrições legais e éticas. As soluções atuais são auxiliadas por computador, mas manuais, demoradas e suscetíveis a erros, tipicamente produzindo lotes retangulares de áreas similares mas que
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Book chapters on the topic "Fair combinatorial optimization"

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Armaselu, Bogdan, and Ovidiu Daescu. "Algorithms for Fair Partitioning of Convex Polygons." In Combinatorial Optimization and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12691-3_5.

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Jia, Xinrui, Kshiteej Sheth, and Ola Svensson. "Fair Colorful k-Center Clustering." In Integer Programming and Combinatorial Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45771-6_17.

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Nguyen, Viet Hung, and Paul Weng. "An Efficient Primal-Dual Algorithm for Fair Combinatorial Optimization Problems." In Combinatorial Optimization and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71150-8_28.

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Hansen, Thomas Dueholm, and Orestis A. Telelis. "Improved Bounds for Facility Location Games with Fair Cost Allocation." In Combinatorial Optimization and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02026-1_16.

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Phillipson, Frank. "Fair Benchmarking Combinatorial Optimization Solvers in the Era of Emerging Computing Paradigms." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94263-1_6.

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Blum, Christian, and Pedro Pinacho-Davidson. "Application of Negative Learning Ant Colony Optimization to the Far from Most String Problem." In Evolutionary Computation in Combinatorial Optimization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30035-6_6.

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Blum, Christian, and Paola Festa. "A Hybrid Ant Colony Optimization Algorithm for the Far From Most String Problem." In Evolutionary Computation in Combinatorial Optimisation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44320-0_1.

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Bederina, Mohammed Bachir, Djamal Chaabane, and Thibaut Lust. "Generating Fair Solutions of Minimal Cost." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240977.

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In this work, we consider combinatorial multi-agent optimization problems, i.e., problems presenting a combinatorial set of solutions, and each solution is evaluated through a vector. An element of the vector corresponds to the utility that an individual agent receives from the solution. Given potential conflicts, it is improbable that a single feasible solution will be optimal for all agents. Consequently, a relevant objective is to identify solutions that are fair to all agents. There are several approaches to defining fairness in the context of optimization problems, and here we focus on Lo
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García Garino Carlos, Mateos Cristian, and Pacini Elina. "ACO-based dynamic job scheduling of parametric computational mechanics studies on Cloud Computing infrastructures." In Advances in Parallel Computing. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-322-3-103.

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Parameter Sweep Experiments (PSEs) allow scientists to perform simulations by running the same code with different input data, which typically results in many CPU-intensive jobs and thus computing environments such as Clouds must be used. Job scheduling is however challenging due to its inherent NP-completeness. Therefore, some Cloud schedulers based on Swarm Intelligence (SI) techniques, which are good at approximating combinatorial problems, have arisen. We describe a Cloud scheduler based on Ant Colony Optimization (ACO), a popular SI technique, to allocate Virtual Machines to physical reso
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"Combinatorial Materials and Catalysts Development: Where Are We and How Far Can We Go?" In Combinatorial and High-Throughput Discovery and Optimization of Catalysts and Materials. CRC Press, 2006. http://dx.doi.org/10.1201/9781420005387-7.

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Conference papers on the topic "Fair combinatorial optimization"

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Golrezaei, Negin, Rad Niazadeh, Kumar Kshitij Patel, and Fransisca Susan. "Online Combinatorial Optimization with Group Fairness Constraints." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/44.

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As digital marketplaces and services continue to expand, it is crucial to maintain a safe and fair environment for all users. This requires implementing fairness constraints into the sequential decision-making processes of these platforms to ensure equal treatment. However, this can be challenging as these processes often need to solve NP-complete problems with exponentially large decision spaces at each time step. To overcome this, we propose a general framework incorporating robustness and fairness into NP-complete problems, such as optimizing product ranking and maximizing sub-modular funct
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Martin, Hugo, and Patrice Perny. "BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/252.

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We study the biOWA model for preference aggregation and multicriteria decision making from bipolar rating scales. A biOWA is an ordered doubly weighted averaging extending standard ordered weighted averaging (OWA) and allowing a finer control of the importance attached to positive and negative evaluations in the aggregation. After establishing some useful properties of biOWA to generate balanced Pareto-optimal solutions, we address fair biOWA-optimization problems in combinatorial domains. We first consider the use of biOWA in multi-winner elections for aggregating graded approval and disappro
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Xu, Huanle, Yang Liu, Wing Cheong Lau, and Rui Li. "Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/354.

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The problem of multi-armed bandit (MAB) with fairness constraint has emerged as an important research topic recently. For such problems, one common objective is to maximize the total rewards within a fixed round of pulls, while satisfying the fairness requirement of a minimum selection fraction for each individual arm in the long run. Previous works have made substantial advancements in designing efficient online selection solutions, however, they fail to achieve a sublinear regret bound when incorporating such fairness constraints. In this paper, we study a combinatorial MAB problem with conc
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Comlek, Yigitcan, Liwei Wang, and Wei Chen. "Mixed-Variable Global Sensitivity Analysis With Applications to Data-Driven Combinatorial Materials Design." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-110756.

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Abstract Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate the n
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Dai, Zuo, and Jianzhong Cha. "A Hybrid Approach of Heuristic and Neural Network for Packing Problems." In ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0119.

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Abstract Artificial Neural Networks, particularly the Hopfield-Tank network, have been effectively applied to the solution of a variety of tasks formulated as large scale combinatorial optimization problems, such as Travelling Salesman Problem and N Queens Problem [1]. The problem of optimally packing a set of geometries into a space with finite dimensions arises frequently in many applications and is far difficult than general NP-complete problems listed in [2]. Until now within accepted time limit, it can only be solved with heuristic methods for very simple cases (e.g. 2D layout). In this p
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Petkov, Hristo, Colin Hanley, and Feng Dong. "DAG-WGAN: Causal Structure Learning with Wasserstein Generative Adversarial Networks." In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120611.

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The combinatorial search space presents a significant challenge to learning causality from data. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint, allowing for the exploration of deep generative models to better capture data sample distributions and support the discovery of Directed Acyclic Graphs (DAGs) that faithfully represent the underlying data distribution. However, so far no study has investigated the use of Wasserstein distance for causal structure learning via generative models. This paper proposes a new model named DAG-W
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Huang, Mingyu, and Ke Li. "Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/621.

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One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works for one problem instance, we expect it will also be effective for other instances whose fitness landscapes essentially share structural similarities with each other. However, due to the black-box nature of combinatorial optimization, it is far from trivial to infer such similarity in real-world scenarios. To bridge this gap, by using local optima network as a proxy of fitness landscapes, this paper proposed to leverage
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Jiang, Chunheng, Jianxi Gao, and Malik Magdon-Ismail. "Inferring Degrees from Incomplete Networks and Nonlinear Dynamics." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/457.

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Inferring topological characteristics of complex networks from observed data is critical to understand the dynamical behavior of networked systems, ranging from the Internet and the World Wide Web to biological networks and social networks. Prior studies usually focus on the structure-based estimation to infer network sizes, degree distributions, average degrees, and more. Little effort attempted to estimate the specific degree of each vertex from a sampled induced graph, which prevents us from measuring the lethality of nodes in protein networks and influencers in social networks. The current
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Liao, Yanfen, Jiejin Cai, and Xiaoqian Ma. "Study and Application on Real Time Optimum Operation for Plant Units." In ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50311.

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The optimum unit commitment is to determine an optimal scheme which can minimize the system operating cost during a period while the load demand, operation constrains of the individual unit are simultaneously satisfied. Since it is characterized as a nonlinear, large scale, discrete, mixed-integer combinatorial optimization problem with constrains, it is always hard to find out the theoretical optimal solution. In this paper, a method combining the priority-order with dynamic comparison is brought out to obtain an engineering optimal solution, and is validated in a power plant composed of thre
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Barros, E. G. D., S. P. Szklarz, J. Hopman, et al. "Well Swapping and Conversion Optimization Under Uncertainty Based on Extended Well Priority Parametrization." In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32960-ms.

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Abstract Offshore field development activities are commonly constrained by the capacity of production facilities available at the platform. It is a challenge for practitioners to find the best development and operational strategies to maximize field production in the presence of such constraints. Additional difficulties are raised when the often large geological uncertainties inherent to field development activities need to be accounted for throughout the search for the best strategy. In this work we present how computer-assisted optimization can help practitioners tackle these challenges. In
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