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Efficient presolving methods for solving maximal covering and partial set covering location problems

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期311卷 73-87页

作者： Chen, Liang Chen, Sheng-Jie Chen, Wei-Kun Dai, Yu-Hong Quan, Tao Chen, Juan Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACI Beijing 100081 Peoples R China Huawei Technol Com Ltd Global Tech Serv Dept Algorithm & Technol Dev Dongguan 523808 Peoples R China

The maximal covering location problem (MCLP) and the partial set covering location problem (PSCLP) are two fundamental problems in facility location and have widespread applications in practice. The MCLP determines a subset of facilities to open to maximize the demand of covered customers subject to a budget constraint on the cost of open facilities;and the PSCLP aims to minimize the cost of open facilities while requiring a certain amount of customer demand to be covered. Both problems can be modeled as mixed integer programming (MIP) formulations. Due to the intrinsic N P -hardness nature, however, it is a great challenge to solve them to optimality by MIP solvers, especially for large-scale cases. In this paper, we present five customized presolving methods to enhance the capability of employing MIP solvers in solving the two problems. The five presolving methods are designed to reduce the sizes of the problem formulation and the search tree of the branch-and-cut procedure. For planar problems with an extremely huge number of customers under realistic types of facility coverage, we show that the number of customers in the reduced problems can be bounded above by a quadratic polynomial of the number of facilities. By extensive numerical experiments, the five presolving methods are demonstrated to be effective in accelerating the solution process of solving the MCLP and PSCLP. Moreover, they enable to solve problems with billions of customers, which is at least one order of magnitude larger than those that can be tackled by previous approaches. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Location mixed integer programming Presolving methods Branch-and-cut

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Appointment scheduling for medical diagnostic centers considering time-sensitive pharmaceuticals: A dynamic robust optimization approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期305卷 1018-1031页

作者： Namakshenas, Mohammad Mazdeh, Mohammad Mahdavi Braaksma, Aleida Heydari, Mehdi Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Univ Twente Ctr Healthcare Operat Improvement & Res CHOIR Enschede Netherlands

This paper studies optimal criteria for the appointment scheduling of outpatients in a medical imag-ing center. The main goal of this study is to coordinate the assignments of radiopharmaceuticals and the scheduling of outpatients on imaging scanners. We study a case of a molecular imaging center that offers services for various diagnostic procedures for outpatient requests. Most procedures in molecular imag-ing involve several steps limited by strict time windows and require a time-sensitive chemical element, technetium-99m (99mTc) with a limited half-life, to produce the radiopharmaceuticals. We investigate the mathematical dynamics of 99mTc dosages to construct optimal schedules for preparing the radio -pharmaceuticals. We develop a rigorous mixed-integer programming model to coordinate the assignment of the radiopharmaceuticals and the scheduling of outpatients on the scanners. The objective is to min-imize the total deviation from the scheduled scanning times. We also develop a novel, less conservative robust optimization approach to capture the uncertainty raised by the availability of 99mTc. We propose an uncertainty handling mechanism to reduce the uncertainty interval over time recursively. The pro-posed mechanism avoids over-conservatism and increases the reliability of mathematical robust models. We evaluate the proposed models by multiple criteria. The final results suggest that the robust model is able to schedule up to 40 outpatients with at most 20 percent of deviation from the scheduled scan times with a decent degree of the constraint violation versus 30 outpatients with at most 50 percent according to the current practice.(c) 2022 Elsevier B.V. All rights reserved.

关键词： OR in health services Medical imaging Scheduling appointment mixed integer programming Robust optimization

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MLD-MPC Approach for Three-Tank Hybrid Benchmark Problem

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Computers, Materials & Continua 2023年第5期75卷 3657-3675页

作者： Hanen Yaakoubi Hegazy Rezk Mujahed Al-Dhaifallah Joseph Haggège Research Laboratory in Automatic Control(LARA) National Engineering School of Tunis(ENIT)University of Tunis El ManarTunis1002Tunisia Department of Electrical Engineering College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11911Saudi Arabia Control and Instrumentation Engineering Department King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center(IRC)for Renewable Energy and Power Systems King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The present paper aims at validating a Model Predictive Control(MPC),based on the mixed Logical Dynamical(MLD)model,for Hybrid Dynamic Systems(HDSs)that explicitly involve continuous dynamics and discrete *** proposed benchmark system is a three-tank process,which is a typical case study of *** MLD-MPC controller is applied to the level control of the considered tank *** study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating *** feature of MLD modeling is very advantageous when an MPC controller synthesis for the HDSs is *** the MLD model of the system is well-posed,then the MPC law synthesis can be developed based on the mixed integer programming(MIP)optimization *** solving this MIP problem,a Branch and Bound(B&B)algorithm is proposed to determine the optimal control ***,a comparative study is carried out to illustrate the effectiveness of the proposed hybrid controller for the HDSs compared to the standard MPC *** results show that the MLD-MPC approach outperforms the standardMPCone that doesn’t consider the hybrid aspect of the *** paper also shows a behavioral test of the MLDMPC controller against disturbances deemed as liquid leaks from the *** results are very satisfactory and show that the tracking error is minimal less than 0.1%in nominal conditions and less than 0.6%in the presence of *** results confirm the success of the MLD-MPC approach for the control of the HDSs.

关键词： Hybrid dynamic system model predictive control mixed logical dynamical model mixed integer programming three-tank hybrid system

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Attractiveness factors in retail category space location-allocation problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第16期61卷 5566-5584页

作者： Babaee, Sara Araghi, Mojtaba Castillo, Ignacio Rostami, Borzou Wilfrid Laurier Univ Lazaridis Sch Business 75 Univ Ave W Waterloo ON N2L 3C5 Canada Univ Alberta Alberta Sch Business Edmonton AB Canada

We study the problem of category space location-allocation in the retail industry. We introduce a new attractiveness factor to reflect the product-based visibility level in designing the optimal allocation policy. This factor will be determined for each aisle by the lineup of product categories allocated to that aisle and all other aisles sharing a shopping path with it. We explore how considering the classical location-based attractiveness and the proposed product-based attractiveness can improve a retailer's overall space profitability. We develop a modelling framework that integrates both location-based and product-based attractiveness factors in a mixed-integer nonlinear program. Due to the non-linearity and non-convexity of the proposed model, large-scale instances are computationally challenging to solve using the state-of-the-art commercial solvers. We thus introduce a two-stage heuristic solution method that generates a near-optimal solution in a reasonable amount of time. Using the two-stage model, we explore the optimal store design for an illustrative case study. The results couple the optimal category space allocation to customers' shopping paths and create a profitability-maximising balance between the placement of high-demand and high-impulse product categories. We show that focussing on product-based attractiveness exposes the store to congestion risks, which can be prevented by adding constraints limiting congestion in different aisles of the store.

关键词： Store design space planning mixed integer programming retail analytics shopping path consideration

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Interactive Approaches to Multiple Criteria Sorting Problems: Entropy-Based Question Selection Methods

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2023年第1期22卷 279-312页

作者： Ozarslan, Ali Karakaya, Gulsah Middle East Tech Univ Dept Business Adm TR-06800 Ankara Turkey

In this study, interactive approaches for sorting alternatives evaluated on multiple criteria are developed. The possible category ranges of alternatives are defined by mathematical models iteratively under the assumption that the preferences of the decision maker (DM) are consistent with an additive utility function. Simulation-based and model-based parameter generation methods are proposed to hypothetically assign the alternatives to categories. A practical approach to solve the incompatibility problem of the randomly generated parameters is developed. Based on the hypothetical assignments, the assignment frequencies of alternatives for each possible category are defined. Then, an information theoretic measure, relative entropy, is used in the selection of the alternative that will be assigned into a category by the DM. The performance of our approaches is tested on different problems with/without initial assignments and category size restrictions. The results show that relative entropy-based alternative selection methods work well in decreasing the assessment burden of DM.

关键词： Multiple criteria sorting additive utility function linear programming mixed integer programming relative entropy category size restriction

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Economic evaluation of a power-to-hydrogen system providing frequency regulation reserves: a case study of Denmark

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第67期48卷 26046-26057页

作者： Zheng, Yi Huang, Chunjun You, Shi Zong, Yi Tech Univ Denmark Dept Wind & Energy Syst DK-2800 Lyngby Denmark

Operating costs are dominant in the hydrogen production of a power-to-hydrogen system. An optimal operational strategy or bidding framework is effective in reducing these costs. However, it is still found that the production cost of hydrogen is high. As the electrolysis unit is characterized by high flexibility, providing ancillary service to the grid becomes a potential pathway for revenue stacking. Recent research has demonstrated the feasibility of providing such a service, but the related economics have not been well evaluated. In this work, we propose a comprehensive operation model to enable participation in the day head, balancing and reserve markets. Three types of reserves are considered by using different operational constraints. Based on the proposed operation framework, we assess the economic performance of a power-to-hydrogen system in Denmark using plentiful actual market data. The results reveal that providing frequency containment reserve and automatic frequency restoration reserve efficiently raises the operational contribution margins. In parallel, by investing in the cash flows, net present value, and break-even hydrogen prices, we conclude that providing reserves makes the power-to-hydrogen project more profitable in the studied period and region.& COPY;2023 The Author(s). Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://***/ licenses/by/4.0/).

关键词： Power to hydrogen Frequency regulation reserve Automatic frequency restoration & nbsp reserve Manual frequency restoration & nbsp mixed integer programming Indices Parameters

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Versatile Weight Attack via Flipping Limited Bits

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 13653-13665页

作者： Bai, Jiawang Wu, Baoyuan Li, Zhifeng Xia, Shu-Tao Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Res Ctr Artificial Intelligence Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Sch Data Sci Shenzhen CUHK Shenzhen Shenzhen 518172 Peoples R China Tencent Shenzhen 518064 Peoples R China

Many attack paradigms against deep neural networks have been well studied, such as the backdoor attack in the training stage and the adversarial attack in the inference stage. In this article, we study a novel attack paradigm, the bit-flip based weight attack, which directly modifies weight bits of the attacked model in the deployment stage. To meet various attack scenarios, we propose a general formulation including terms to achieve effectiveness and stealthiness goals and a constraint on the number of bit-flips. Furthermore, benefitting from this extensible and flexible formulation, we present two cases with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA). SSA which aims at misclassifying a specific sample into a target class is a binary optimization with determining the state of the binary bits (0 or 1);TSA which is to misclassify the samples embedded with a specific trigger is a mixed integer programming (MIP) with flipped bits and a learnable trigger. Utilizing the latest technique in integer programming, we equivalently reformulate them as continuous optimization problems, whose approximate solutions can be effectively and efficiently obtained by the alternating direction method of multipliers (ADMM) method. Extensive experiments demonstrate the superiority of our methods.

关键词： Training integer programming Training data Data models Behavioral sciences Artificial neural networks Trojan horses Bit-flip weight attack deep neural networks vulnerability binary optimization mixed integer programming

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An integrated batching problem for steel plate manufacturing with bi-strand casting

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第3期61卷 955-975页

作者： Lee, Myungho Hong, Juntaek Kim, Hyunjoon Bang, Inhye Lee, Kangbok Kim, Byung-In Kim, Jungkyu Pohang Univ Sci & Technol 77 Cheongam Ro Pohang Si 37673 Gyeongsangbuk D South Korea POSCO Pohang Si Gyeongsangbuk D South Korea

We consider an integrated batching problem for steel-making and continuous casting process of steel plate manufacturing. For steel-making, slab batching problem is defined to make charges from slabs, and for continuous casting, charge batching problem is defined to make casts from charges. Our batching problem is regarded as a combination of slab batching and charge batching problems. Since steel-making and continuous casting are processes that must be synchronised, two batching problems must be integratedly handled. However, because of their difficulties, most papers solved each batching problem sequentially. We deal with a batching problem under bi-strand casting environment where slabs are cast with two strands. We also consider practical aspects that were barely considered before such as slab length difference constraints between strands and casts having few charges. We propose two mixed integer programming models. The first one is to create frames to define the overall structure of the solution and the second one is to fill the created frames with slabs to concretise the solution. The proposed matheuristic algorithm solves the problem in an integrated manner and finds a near-optimal solution in a reasonable time. It outperforms the company's current practice, as shown in experiments with real and randomly generated data.

关键词： Production planning iron and steel industry matheuristics mixed integer programming bi-strand casting

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The Novel Data-Driven Robust Maximum Expert mixed integer Consensus Models Under Multirole's Opinions Uncertainty by Considering Noncooperators

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第6期10卷 3454-3470页

作者： Wei, Jinpeng Qu, Shaojian Wang, Qiuhan Luan, Dongqing Zhao, Xiaohui Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Shanghai Univ Engn Sci Sch Managment Shanghai 201620 Peoples R China

In group decision-making, ignoring the existence of uncertain factors causes the decision-making problem to lose its practical significance. Based on the maximum expert consensus model (MECM), we considered the uncertainty of the opinions of the three participating roles by introducing noncooperators. Additionally, three different opinion uncertainty sets were constructed to describe the characteristics of opinion uncertainty more accurately. Furthermore, by applying a robust optimization (RO) method to process uncertain sets, we propose mixed-integer robust MECMs, which reduce the risk of uncertain opinions to decision-makers (DMs). Moreover, numerical experiments used in the passenger satisfaction survey of the Shanghai Metro verified the validity of the models proposed in this article. The characteristics of the models were revealed through sensitivity analysis. Finally, to overcome the relatively highly conservative results of the classic RO method, we construct data-driven opinion uncertainty sets and propose data-driven RO models. Hence, DMs with different risk preferences can choose RO models with different risk levels according to the situation.

关键词： Uncertainty Costs Numerical models Decision making Cost function Social sciences Sensitivity analysis Consensus model data-driven group decision-making mixed integer programming

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Linear regression with partially mismatched data: local search with theoretical guarantees

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MATHEMATICAL programming 2023年第2期197卷 1265-1303页

作者： Mazumder, Rahul Wang, Haoyue MIT Cambridge MA 02139 USA

Linear regression is a fundamental modeling tool in statistics and related fields. In this paper, we study an important variant of linear regression in which the predictor-response pairs are partially mismatched. We use an optimization formulation to simultaneously learn the underlying regression coefficients and the permutation corresponding to the mismatches. The combinatorial structure of the problem leads to computational challenges. We propose and study a simple greedy local search algorithm for this optimization problem that enjoys strong theoretical guarantees and appealing computational performance. We prove that under a suitable scaling of the number of mismatched pairs compared to the number of samples and features, and certain assumptions on problem data;our local search algorithm converges to a nearly-optimal solution at a linear rate. In particular, in the noiseless case, our algorithm converges to the global optimal solution with a linear convergence rate. Based on this result, we prove an upper bound for the estimation error of the parameter. We also propose an approximate local search step that allows us to scale our approach to much larger instances. We conduct numerical experiments to gather further insights into our theoretical results, and show promising performance gains compared to existing approaches.

关键词： Linear regression Mismatched data Local search method Permutation learning mixed integer programming Polynomial time algorithm

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