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13th international conference on Evolutionary Multi-Criterion optimization, EMO 2025

作者： Rudolph, Günter Wagner, Markus Department of Computer Science TU Dortmund University Dortmund Germany Department of Data Science and AI Monash University Clayton Australia

ISBN: (纸本)9789819635054

Parameter control involves dynamically adjusting the parameter values of the evolutionary algorithm throughout the optimization process, including parameters like mutation rate and operator selection. Self-adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. In this article, we review the cumulative step size adaptation method originally proposed for single-objective optimization over continuous variables and recast it for deployment in multiobjective optimization over unbounded integer space. We contribute a systematic investigation of its hyperparameters which shows that while (1) the very best configurations remain problem-specific, (2) the performance of the algorithm is largely independent of the self-adaptation scheme’s parameterization and initial configuration. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： integer linear programming

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A Framework for Solving Quadratic Knapsack Problem Based on Deep Reinforcement Learning

A Framework for Solving Quadratic Knapsack Problem Based on ...

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international conference on Intelligent Control and Information Processing (ICICIP)

作者： Jiajia Huang Shijie Zhao Xiaoyu Fu Shenshen Gu School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

the quadratic knapsack problem (QKP) with constraints has been a focus of attention in combinatorial optimization, where traditional exact algorithms face difficulties in solving large-scale problems due to the problem’s NP-hard nature, and heuristic algorithms have difficulty in generating high-quality solutions in effective time. To solve QKP quickly and accurately, this paper proposes a graph pointer network (GPN) framework with hierarchical reinforcement learning (HRL). the first stage of the method trains low-level neural network to find feasible solutions that satisfy the constraint as much as possible. the second stage trains the high-level neural network based on the first stage using the objective function as a penalty term. After the HRL, our trained hierarchical GPN (HGPN) model can effectively solve the QKP. In addition, we add a greedy strategy in the decoding process to further improve the solution accuracy of the neural network solver. Experiments show that our proposed method can approximate the optimal solution produced by the Docplex solver and outperforms most end-to-end neural network solvers.

关键词： Accuracy Heuristic algorithms Computational modeling Neural networks Linear programming Encoding optimization Long short term memory Testing Intelligent control

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integer programming Framework for Pangenome-Based Genome Inference

Integer Programming Framework for Pangenome-Based Genome In...

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29th international conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Chandra, Ghanshyam Helal Hossen, Md Scholz, Stephan Dilthey, Alexander T. Gibney, Daniel Jain, Chirag Department of Computational and Data Sciences Indian Institute of Science KA Bangalore560012 India Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States Institute of Medical Microbiology and Hospital Hygiene Heinrich Heine University Düsseldorf Düsseldorf Germany Center for Digital Medicine Heinrich Heine University Düsseldorf Düsseldorf Germany

ISBN: (纸本)9783031902512

Affordable genotyping methods are essential in genomics. Commonly used genotyping methods primarily support single nucleotide variants and short indels but neglect structural variants. Additionally, accuracy of read alignments to a reference genome is unreliable in highly polymorphic and repetitive regions, further impacting genotyping performance. Recent works highlight the advantage of haplotype-resolved pangenome graphs in addressing these challenges. Building on these developments, we propose a rigorous alignment-free genotyping framework. Our formulation seeks a path through the pangenome graph that maximizes the matches between the path and substrings of sequencing reads (e.g., k-mers) while minimizing recombination events (haplotype switches) along the path. We prove that this problem is NP-hard and develop efficient integer-programming solutions. We benchmarked the algorithm using downsampled short-read datasets from homozygous human cell lines with coverage ranging from 0.1× to 10×. Our algorithm accurately estimates complete major histocompatibility complex (MHC) haplotype sequences with small edit distances from the ground-truth sequences, providing a significant advantage over existing methods on low-coverage inputs. While the current design of the algorithm is applicable to haploid samples, we outline potential directions for extending it to diploid samples. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： combinatorial optimization

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the mixing set with divisible capacities

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13th international conference on integer programming and combinatorial optimization

作者： Conforti, Michele Di Summa, Marco Wolsey, Laurence A. Univ Padua Dipartimento Matemat Pura & Applicata Via Trieste 63 I-35121 Padua Italy Catholic Univ Louvain CORE B-1348 Louvain Belgium

Given rational numbers C-0,...,C-m and b(0),...,b(m), the mixing set with arbitrary capacities is the mixed-integer set defined by conditions s + C(t)z(t) >= b(t), 0 = 0, z(t) integer, 0 <= t <= m. Such a set... 详细信息

ISBN: (纸本)9783540688860

Given rational numbers C-0,...,C-m and b(0),...,b(m), the mixing set with arbitrary capacities is the mixed-integer set defined by conditions s + C(t)z(t) >= b(t), 0 <= t <= m, s >= 0, z(t) integer, 0 <= t <= m. Such a set has applications in lot-sizing problems. We study the special case of divisible capacities, i.e. C-t/Ct-1 is a positive integer for 1 < t <= m. Under this assumption, we give an extended formulation for the convex hull of the above set that uses a quadratic number of variables and constraints.

关键词： mixed-integer programming compact extended formulations mixing sets

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Mechanism designing of multi-unit combinatorial online double auction

Mechanism designing of multi-unit combinatorial online doubl...

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13th international conference on Management Science and Engineering

作者： Fu Li-fang Feng Yu-qiang Wu Gang Harbin Inst Technol Sch Management Harbin 150001 Peoples R China

ISBN: (纸本)7560323553

combinatorial (double) auctions have recently been used in business-to-business trading of centralized marketplace or other multi-agent coordination systems. When the goods are indivisible, winner determination problem (WDP) can be described as an integer-programming model and NP-hard in its general form, so a novel multi-round combinatorial double auction mechanism is proposed to solve it. A multi-objection integer programming is set up to study special properties of online combinatorial double auctions so as to search for the optimal solutions of the model. Comparing to previous researches on double Auction, the combinatorial double auctions mechanism expatiated here shows some advantages in respect that running for several rounds to matching bids as more as possible and permitting agents applying online learning algorithms to adjust bidding strategy adaptively to pursue more bargain opportunities. Agents trade identical bundles of goods with. discriminatory prices, which embodied the diversity and flexibility of practical market. Result of a typical example shows that the mechanism increased the overall revenue and improved market efficiency greatly relative to single-round combinatorial (double) auctions.

关键词： double auction mechanism design multi-round integer programming

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Binary positive semidefinite matrices and associated integer polytopes

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13th international conference on integer programming and combinatorial optimization

作者： Letchford, Adam N. Sorensen, Michael M. Univ Lancaster Dept Management Sci Lancaster LA1 4YW England Univ Aarhus Aarhus Sch Business CORAL & Dept Business Studies Aarhus Denmark

ISBN: (纸本)9783540688860

We consider the positive semidefinite (psd) matrices with binary entries. We give a characterisation of such matrices, along with a graphical representation. We then move on to consider the associated integer polytopes. Several important and well-known integer polytopes - the cut, boolean quadric, multicut and clique partitioning polytopes - are shown to arise as projections of binary psd polytopes. Finally, we present various valid inequalities for binary psd polytopes, and show how they relate to inequalities known for the simpler polytopes mentioned. Along the way, we answer an open question in the literature on the max-cut problem, by showing that the so-called k-gonal inequalities define a polytope.

关键词： polyhedral combinatorics semidefinite programming

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the air traffic flow management problem: An integer optimization approach

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13th international conference on integer programming and combinatorial optimization

作者： Bertsimas, Dimitris Lulli, Guglielmo Odoni, Amedeo MIT Alfred P Sloan Sch Management Cambridge MA 02139 USA Univ Milan Dept Informat Syst & Commun Milan Italy MIT Ctr Operat Res Dept Aeronaut & Astronaut Cambridge MA USA

ISBN: (纸本)9783540688860

In this paper, we present a new integer Program (IP) for the Air Traffic Flow Management (ATFM) problem. the model we propose provides a complete representation of all the phases of each flights, i.e., the phase of taking-off, of cruising and of landing;suggesting all the actions to be implemented to achieve the goal of safe, efficient, and expeditious aircraft movement. the distinctive feature of the model is that it allows rerouting decisions. these decisions are formulated by means of "local" conditions, which allow us to represent such decisions in a very compact way by only introducing new constraints. Moreover, to strengthen the polyhedral structure of the underlying relaxation, we also present three classes of valid inequalities. We report short computational times (less than 15 minutes) on instances of the size of the US air traffic control system that make it realistic that our approach can be used as the main engine of managing air traffic in the US.

关键词： integer programming

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PETA: Privacy Enabled Task Allocation 13

PETA: Privacy Enabled Task Allocation

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13th IEEE international conference on Services Computing (SCC) / 13th IEEE international conference on Web Services (ICWS)

作者： Phuke, Nitin Saurabh, Saket Gharote, Mangesh Lodha, Sachin Tata Consultancy Serv TCS Res Pune Maharashtra India

ISBN: (纸本)9781728187891

Service organizations need to comply with numerous data regulations to protect and preserve their customers' privacy. Any misuse of data and privacy breach can affect the organizations' reputation and brand image. In service delivery scenarios, such as IT support help desk, agents need to access customer data to serve them effectively. this data often includes sensitive and personally identifiable information of the customer. While some amount of data exposure is needed to serve a customer, however, exposure to more data than required could be a threat to an individual's privacy. Hence, organizations need to design methodologies to ensure customer privacy while achieving minimal cost of operations. In this paper, we propose the Privacy Enabled Task Allocation (PETA) model for assigning customer requests to agents so that the overall cost of operations and data exposure is minimal. Data exposure is minimized by restricting the amount of data exposure per agent and by regulating the assignment of tasks. the PETA problem is modelled as an integer linear program, which is NP-hard. To solve this combinatorial hard problem, we have designed an allocation algorithm based on the linear programming relaxation for finding a quick feasible solution.

关键词： Task Allocation Data Privacy Workforce optimization Service Organization integer Linear programming Feasibility Pump

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Supplier Selection by Multi-Attribute combinatorial Bidding

Supplier Selection by Multi-Attribute Combinatorial Bidding

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47th Annual Hawaii international conference on System Sciences

作者： Ribeiro Giacon, Joice Cavalheiro Yoshida Yoshizaki, Hugo T. Montibeller, Gilberto Univ Sao Paulo BR-05508 Sao Paulo Brazil London Sch Econ & Polit Sci London England

ISBN: (纸本)9781479925049

Supplier selection is a challenging decision that has strategic importance for organizations. Cost is no longer the sole factor in the selection of suppliers, and the complexity of this issue arises from the interplay of several situation-specific criteria (such as total cost, CO2, emissions, development time, lead time) as well as the combinatorial nature of this problem. this paper proposes an approach based on combinatorial optimization (integer linear programming) combined with multi-criteria value analysis to establish priorities and trade-offs among the defined criteria for combinatorial bidding. the approach was employed in a real-world decision, the selection of a supplier for a cosmetics packaging set for a new product line. the obtained solution is compared against standard multi-criteria optimization (without a combinatorial auction formulation) and also against single criterion optimization. the paper also reports on the challenges and advantages of applying the framework in the case study.

关键词： combinatorial mathematics cosmetics integer programming linear programming packaging supply chain management combinatorial optimization cosmetics packaging set development time emissions integer linear programming lead time multiattribute combinatorial bidding multicriteria value analysis organizations product line standard multicriteria optimization supplier selection supply chain management Adaptation models Analytical models Companies Computational modeling Equations Mathematical model optimization MCDA combinatorial optimization integer programming multi-attribute analysis supplier selection

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Shapes and recession cones in mixed-integer convex representability

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MAthEMATICAL programming 2024年第1-2期204卷 739-752页

作者： Zadik, Ilias Lubin, Miles Vielma, Juan Pablo MIT Dept Math Boston MA 02139 USA

Mixed-integer convex representable (MICP-R) sets are those sets that can be represented exactly through a mixed-integer convex programming formulation. Following up on recent work by Lubin et al. (in: Eisenbrand (ed) integer programming and combinatorial optimization - 19th international conference, Springer, Waterloo), (Math. Oper. Res. 47:720-749, 2022) we investigate structural geometric properties of MICP-R sets, which strongly differentiate them from the class of mixed-integer linear representable (MILP-R) sets. First, we provide an example of an MICP-R set which is the countably infinite union of convex sets with countably infinitely many different recession cones. this is in sharp contrast with MILP-R sets which are (countable) unions of polyhedra that share the same recession cone. Second, we provide an example of an MICP-R set which is the countably infinite union of polytopes all of which have different shapes (no pair is combinatorially equivalent, which implies they are not affine transformations of each other). Again, this is in sharp contrast with MILP-R sets which are (countable) unions of polyhedra that are all translations of a finite subset of themselves.

关键词： Mixed-integer convex optimization Mixed-integer programming formulations Mixed-integer programming representability

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