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binary programming formulations for the upper domination problem

MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2023年第2期98卷 155-168页

作者： Burdett, Ryan Haythorpe, Michael Newcombe, Alex Flinders Univ S Australia South Rd Tonsley Pk SA 1284 Australia

We consider Upper Domination, the problem of finding the minimal dominating set of maximum cardinality. Very few exact algorithms have been described for solving Upper Domination. In particular, no binary programming formulations for Upper Domination have been described in literature, although such formulations have proved quite successful for other kinds of domination problems. We introduce two such binary programming formulations, and show that both can be improved with the addition of extra constraints which reduce the number of feasible solutions. We compare the performance of the formulations on various kinds of graphs, and demonstrate that (a) the additional constraints improve the performance of both formulations, and (b) the first formulation outperforms the second in most cases, although the second performs better for very sparse graphs. Also included is a short proof that the upper domination number of any generalized Petersen graph P(n, k) is equal to n.

关键词： Upper domination Minimal dominating set binary programming Formulation Graphs

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An efficient binary programming method for black-box optimization and its application in processor design

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Science China(Information Sciences) 2024年第12期67卷 140-156页

作者： Xiaoliang LV Qiaozhu ZHAI Jianchen HU Yuhang ZHU Jinhui LIU Xiaohong GUAN Faculty of Electronic and Information Engineering Xi'an Jiaotong University Department of Automation Tsinghua University

Optimizing the parameter settings in a large design space for the processor with limited simulation resources is a challenging task. The current black-box optimization algorithms for processor design space exploration(DSE) problems usually require a large amount of simulation resources for high-dimensional and discrete problems. Besides, the constraints handling techniques in these algorithms need to be improved. To address the issues, we propose an efficient binary integer programming(BIP) approach for the DSE of the processor with strictly guaranteed constraints. Our approach involves adopting the separability assumption to establish a surrogate objective function that is ordinal consistent, thus avoiding the complex non-linearity of the real objective function. Moreover, the design rules can be taken simply as constraints in BIP model to further reduce the design space. Thus, the efforts spent in the infeasible exploration space can be *** experimental results show that the proposed algorithm outperforms the state-of-the-art Bayesian optimization and evolutionary algorithms in terms of exploration efficiency, required simulation points and performance of the recommended points.

关键词： black-box optimization binary programming design space exploration processor design expensive simulation system

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binary programming for Allocation of Players in Soccer Competitions by a Canadian Sports Event Management Company 1

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29th International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2023

作者： Santos, Leandro José Tranzola Moreira, Miguel Ângelo Lellis de Araújo Costa, Igor Pinheiro dos Santos, Marcos da Silva, Ricardo Franceli SP São Paulo Brazil Fluminense Federal University RJ Niterói Brazil Naval System Analysis Center RJ Rio de Janeiro Brazil Military Institute of Engineering RJ Rio de Janeiro Brazil FIA Business School SP São Paulo Brazil

ISBN: (数字)9783031470585

ISBN: (纸本)9783031470578

The resource allocation problem becomes more complex as resources and activities are added to the scenario as the possibility of combinations grows exponentially, since by adding only one resource or activity, the number of variables increases as a function of the product of available resources per activity. With that complexity factor in mind, this work develops a binary programming model with the objective of allowing a Canadian sports event management company to optimize the allocation of individually enrolled players in 14 different soccer competitions. The problem considers constraints such as players’ availability on different days of the week, teams mix, minimum and maximum number of players per team, player skill level and different competitions on the same day. The aim of the model was to maximize the number of competitions each player will participate in, and although not all competitions had enough players allocated to them, the result was satisfactory as all players were allocated to at least one competition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： binary programming Competition Resource allocation Soccer

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An improved binary programming formulation for the secure domination problem

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ANNALS OF OPERATIONS RESEARCH 2020年第2期295卷 561-573页

作者： Burdett, Ryan Haythorpe, Michael Flinders Univ S Australia 1284 South RoadTonsley Pk Adelaide SA Australia

The secure domination problem, a variation of the domination problem with some important real-world applications, is considered. Very few algorithmic attempts to solve this problem have been presented in literature, and the most successful to date is a binary programming formulation which is solved using CPLEX. A new binary programming formulation is proposed here which requires fewer constraints and fewer binary variables than the existing formulation. It is implemented in CPLEX, and tested on certain families of graphs that have previously been considered in the context of secure domination. It is shown that the runtime required for the new formulation to solve the instances is significantly less than that of the existing formulation. An extension of our formulation that solves the related, but further constrained, secure connected domination problem is also given;to the best of the authors' knowledge, this is the first such formulation in literature.

关键词： Secure Domination Graph binary programming Formulation Secure connected

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A robust design for a Colombian dairy cooperative's milk storage and refrigeration logistics system using binary programming

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2017年第PartC期183卷 710-720页

作者： Tordecilla-Madera, Rafael Polo, Andres Munoz, Dairo Gonzalez-Rodriguez, Leonardo Fdn Univ Agr Colombia Ind Engn Program Innovar Tecnol Res Grp Calle 170 54A-10 Bogota Colombia Univ La Sabana Fac Engn Logist Syst Res Grp Grp Invest Sistemas Logist Chia Colombia

This article describes a robust design for a milk storage and refrigeration logistics system for a dairy cooperative located in the Ubate region of Colombia using a binary programming model. Such design included cooling tank location, assigning producers to the tanks and calculating system capacity. This research was aimed at identifying the most robust and cost-efficient configuration (given the design's parameters). Perturbation parameters, robustness requirements and performance features were thus qualitatively and quantitatively identified;the perturbation parameters' impact on robustness requirements and performance features was also determined. A supply chain involves a variety of configurations, along with variations in milk supply (perturbation parameter). The most robust configuration and associated costs were thus identified through robustness-cost characterization and used for designing the dairy cooperative's logistical Storage and refrigeration system. A tactical plan was also drawn up for optimizing cooling tank use. Our main theoretical conclusion regarding this matter (depending on how robustness requirements and perturbation parameters are defined and calculated) suggested that the most robust system does not necessarily represent higher costs because the most robust configurations were in fact the least costly concerning high milk supply values.

关键词： Robustness Cost binary programming Milk cooling tanks Location

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Fast and accurate solution for the SCUC problem in large-scale power systems using adapted binary programming and enhanced dual neural network

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ENERGY CONVERSION AND MANAGEMENT 2014年 78卷 477-485页

作者： Shafie-khah, M. Moghaddam, M. P. Sheikh-El-Eslami, M. K. Catalao, J. P. S. Univ Beira Interior P-6201001 Covilha Portugal Tarbiat Modares Univ Tehran 14115111 Iran INESC ID P-1000029 Lisbon Portugal Univ Lisbon IST P-1049001 Lisbon Portugal

This paper presents a novel hybrid method for solving the security constrained unit commitment (SCUC) problem. The proposed formulation requires much less computation time in comparison with other methods while assuring the accuracy of the results. Furthermore, the framework provided here allows including an accurate description of warmth-dependent startup costs, valve point effects, multiple fuel costs, forbidden zones of operation, and AC load flow bounds. To solve the nonconvex problem, an adapted binary programming method and enhanced dual neural network model are utilized as optimization tools, and a procedure for AC power flow modeling is developed for including contingency/security issues, as new contributions to earlier studies. Unlike classical SCUC methods, the proposed method allows to simultaneously solve the unit commitment problem and comply with the network limits. In addition to conventional test systems, a real-world large-scale power system with 493 units has been used to fully validate the effectiveness of the novel hybrid method proposed. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： AC power flow binary programming Dual neural network Security constrained unit commitment

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Modelling and solving the university course timetabling problem with hybrid teaching considerations

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JOURNAL OF SCHEDULING 2024年 1-21页

作者： Davison, Matthew Kheiri, Ahmed Zografos, Konstantinos G. Univ Lancaster STOR i Ctr Doctoral Training Lancaster England Univ Lancaster Dept Management Sci Lancaster England Univ Lancaster Ctr Transport & Logist Cent Dept Management Sci Lancaster England

The university course timetabling problem is a challenging problem to solve. As universities have evolved, the features of this problem have changed. One emerging feature is hybrid teaching where classes can be taught online, in-person or a combination of both in-person and online. This work presents a multi-objective binary programming model that includes common university timetabling features, identified from the literature, as well as hybrid teaching features. A lexicographic solution method is outlined and computational experiments using benchmark data are used to demonstrate the key aspects of the model and explore trade-offs among the objectives considered. The results of these experiments demonstrate that the model can be used to find demand-driven schedules for universities that include hybrid teaching. They also show how the model could be used to inform practitioners who are involved in strategic decision-making at universities.

关键词： University timetabling Hybrid teaching binary programming Multi-objective

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A binary Structured Sparsity Approach for Multi-Anchor Direct Localization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14055-14070页

作者： Akbari, Shiva Valaee, Shahrokh Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn 10 Kings Coll Rd Toronto ON M5S 3G8 Canada

Structured sparsity improves on traditional sparse modeling by suggesting that only a limited number of input variables is needed to describe the output variable. These methods go a step further by incorporating structured patterns in variable selection, such as groups or networks of input variables. This paper introduces a novel binary approximation method leveraging structured sparsity to enhance multi-anchor direct localization performance. By reformulating the sparse recovery as a quadratic unconstrained binary optimization (QUBO) problem, we address the significant computational complexity inherent in NP-hard problems. Employing compressed sensing and binary programming, our method reduces approximation errors and ensures that the line-of-sight component is consistently identified from a coherent grid point among anchors, thus enhancing localization accuracy. Our findings demonstrate a significant improvement in the accuracy of direct localization methods, underscoring the potential of structured sparsity and QUBO formulation to advance localization technologies in multipath environments.

关键词： Location awareness Accuracy Wireless fidelity Computational modeling OFDM Wireless networks programming binary programming direct localization group LASSO sparse modeling structured sparsity

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Smart Scheduling to Minimize Downtime on Automated Dispensing Machines

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Operations Research Forum 2025年第2期6卷 1-16页

作者： Munson, Charles L. Shahsavand, Shirin Department of Finance and Management Science Carson College of Business Washington State University Pullman WA United States

This paper addresses the scheduling problem of filling customer pill orders on a set of automated dispensing machines. Each order consists of a 1-month supply of multiple pill types with varying quantities that can be assigned to and processed by the machines. The client company had previously used a first-come-first-served (FCFS) rule to assign orders to machines, resulting in frequent stoppages to refill canisters. We introduced a smart scheduling program with the objective of reducing machine downtime by having as many pill canisters as possible become nearly empty at the same time. In comparison with the FCFS approach, our smart scheduling program reduces the number of machine stoppages (i.e., downtime) by an average of 83% and increases daily effective capacity by approximately 60%, without adding labor or equipment. This work brings new perspectives and tools to the management of differentiated customers in complicated order-filling environments such as those seen in online pharmacies and health supplement companies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： binary programming Case study Dispensing machines Machine scheduling Order assignment Workload balancing

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binary GROUP SPARSITY IN MULTI ANCHOR DIRECT LOCALIZATION 9

BINARY GROUP SPARSITY IN MULTI ANCHOR DIRECT LOCALIZATION

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9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Akbari, Shiva Valaee, Shahrokh Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9798350344523

Structured sparsity is a set of methods in sparse modeling that extend and generalize sparsity regularization techniques. These methods leverage the assumption that the output variable can be effectively described using a small number of input variables. In addition, structured sparsity regularization methods take an additional stride by enabling the optimal selection of structured patterns, including groups or networks of input variables. In this paper, we proposed a binary approximation for the Group Structured Sparsity problem to increase the accuracy and robustness of the support recovery solution. Motivated by factors like interpretability and computational efficiency, structured sparsity methods find applications in various fields, including localization. Therefore, we investigate the effectiveness of our method in the field of multianchor direct localization. Our results demonstrate a significant improvement in the accuracy of the direct localization method.

关键词： Sparse Modeling Structured Sparsity Group LASSO binary programming Direct Localization

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