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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期308卷 1056-1073页

作者： Abraham, Gyula Dosa, Gyorgy Hvattum, Lars Magnus Olaj, Tomas Attila Tuza, Zsolt Univ Pannonia Veszprem Hungary Molde Univ Coll Fac Logist Molde Norway Alfred Reny Inst Math H-1053 Budapest Hungary

We introduce the board packing problem (BoPP). In this problem we are given a rectangular board with a given number of rows and columns. Each position of the board has an integer value representing a gain, or revenue, that is obtained if the position is covered. A set of rectangles is also given, each with a given size and cost. The objective is to purchase some rectangles to place on the board so as to maximize the profit, which is the sum of the gain values of the covered cells minus the total cost of purchased rectangles. This problem subsumes several natural optimization problems that arise in practice. A mixedinteger programming model for the BoPP problem is provided, along with a proof that BoPP is NP -hard by reduction from the satisfiability problem. An evolutionary algorithm is also developed that can solve large instances of BoPP. We introduce benchmark instances and make extensive computer examinations.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Combinatorial optimization Genetic algorithm binary integer programming Covering Location problems

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Lifetime improvement of visual sensor networks for target coverage through uniform energy consumption

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2013年第4期14卷 249-266页

作者： Hosseini, Mojtaba Dehghan, Mehdi Pedram, Hossein Amirkabir Univ Technol Comp Engn & Informat Technol Dept Tehran Iran

In this paper, a novel method is proposed for inducing uniformity in energy consumption across the entire network area. This method prolongs the durability of the target coverage task. Moreover, it is shown that the optimised selection of the sensors in each timeslot does not lead to an optimum selection across the lifetime of the network due to non-uniformity in energy consumption pattern. Uniformity control is achieved using the sensors' panning capability to select the sensing area. The major notion of energy uniformity in this paper is typified with the network area sections level. For this criterion, sensor selection has been modeled as a binary integer programming (BIP) problem, and optimal solutions have been obtained for both point target model and circular target model which are used in k-coverage and full-angle coverage, respectively. Furthermore, a distributed algorithm has also been proposed. Experimental results demonstrate that exerting uniformity control can significantly increase the lifetime of the network.

关键词： VSNs visual sensor networks directional sensor sensor selection sensor reconfiguration binary integer programming lifetime maximisation energy consumption uniformity k-coverage full angle coverage

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A single-machine scheduling problem with maintenance activities to minimize makespan

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APPLIED MATHEMATICS AND COMPUTATION 2010年第11期215卷 3929-3935页

作者： Hsu, Chou-Jung Low, Chinyao Su, Chwen-Tzeng Natl Yunlin Univ Sci & Technol Inst Ind Engn & Management Yunlin Taiwan

We study a single-machine scheduling problem with periodic maintenance activity under two maintenance stratagems. Although the scheduling problem with single or periodic maintenance and nonresumable jobs has been well studied, most of past studies considered only one maintenance stratagem. This research deals with a single-machine scheduling problem where the machine should be stopped for maintenance after a fixed periodic interval (T) or after a fixed number of jobs (K) have been processed. The objective is to minimize the makespan for the addressed problem. A two-stage binary integer programming (BIP) model is provided for driving the optimal solution up to 350-job instances. For the large-sized problems, two efficient heuristics are provided for the different combinations of T and K. Computational results show that the proposed algorithm Best-Fit-Butterfly (BBF) performs well because the total average percentage error is below 1%. Once the constraint of the maximum number of jobs (K) processed in the machine's available time interval (T) is equal or larger than half of jobs, the heuristic Best-Fit-Decreasing (DBF) is strongly recommended. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Scheduling Periodic maintenance binary integer programming

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Analytical method to estimate accident duration using archived speed profile and its statistical analysis

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KSCE JOURNAL OF CIVIL ENGINEERING 2012年第6期16卷 1064-1070页

作者： Chung, Younshik Yoon, Byoung-Jo Univ Incheon Inchon 406840 South Korea Korea Transport Inst Goyang 411701 South Korea

Data on the duration of traffic accidents have been collected by using a manual recoding method based on the statement of freeway patrol officers or eyewitness. Accordingly, the data may include cost and safety problems due to the difficulty of observing an accident from its occurrence to its clearance. To reduce these problems, this study proposes an analytical method to estimate accident duration based on the archived speed profile. The proposed method is demonstrated by a case study using data on approximately 6,200 accidents that occurred on freeways in Orange County, California, in 2001. In addition, a statistical analysis based on the Accelerated Failure Time (AFT) metric survival models was performed to identify the relationship between estimated accident duration and its causal factors. The analysis results were consistent with the theoretical expectation and prior studies. Ultimately, the proposed method will contribute to reducing observation burdens when collecting data on accident duration.

关键词： freeway accident archived speed profile accident duration binary integer programming censored data Accelerated Failure Time (AFT) model

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Learning Label Preserving binary Codes for Multimedia Retrieval: A General Approach

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2018年第1期14卷 1–23页

作者： Li, Kai Qi, Guo-Jun Hua, Kien A. Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

Learning-based hashing has been researched extensively in the past few years due to its great potential in fast and accurate similarity search among huge volumes of multimedia data. In this article, we present a novel multimedia hashing framework, called Label Preserving Multimedia Hashing (LPMH) for multimedia similarity search. In LPMH, a general optimization method is used to learn the joint binary codes of multiple media types by explicitly preserving semantic label information. Compared with existing hashing methods which are typically developed under and thus restricted to some specific objective functions, the proposed optimization strategy is not tied to any specific loss function and can easily incorporate bit balance constraints to produce well-balanced binary codes. Specifically, our formulation leads to a set of binary integer programming (BIP) problems that have exact solutions both with and without bit balance constraints. These problems can be solved extremely fast and the solution can easily scale up to large-scale datasets. In the hash function learning stage, the boosted decision trees algorithm is utilized to learn multiple media-specific hash functions that can map heterogeneous data sources into a homogeneous Hamming space for cross-media retrieval. We have comprehensively evaluated the proposed method using a range of large-scale datasets in both single-media and cross-media retrieval tasks. The experimental results demonstrate that LPMH is competitive with state-of-the-art methods in both speed and accuracy.

关键词： Learning to hash supervised learning multimedia retrieval large-scale similarity search discrete optimization binary integer programming

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A threshold function for harmonic update

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SIAM JOURNAL ON DISCRETE MATHEMATICS 1997年第3期10卷 482-498页

作者： Fang, SC Venkatesh, SS Department of Electrical Engineering University of Pennsylvania Philadelphia

Harmonic update is a randomized on-line algorithm which, given a random rn-set of vertices U(m) subset of or equal to {-1, 1}(n) in the n-dimensional cube, generates a random vertex w is an element of {-1, 1}(n) as a putative solution to the system of linear inequalities: Sigma(i=1)(n) w(i)u(i) greater than or equal to 0 for each u is an element of U(m). Using tools from large deviation multivariate normal approximation and Poisson approximation, we show that root n/root log n is a threshold function for the property that the vertex w generated by harmonic update has positive inner product with each vertex in U(m). More explicitly, let P(n, rn) denote the probability that Sigma(i=1)(n) w(i)u(i) greater than or equal to 0 for each u is an element of U(m). Then, as n --> infinity, P(n, m) --> 0 or 1 according to whether m = m(n) varies with n such that >> root n/root log n or m << root n/root log n, respectively. The analysis also exposes the fine structure of the threshold function.

关键词： polytopes threshold function randomized algorithm harmonic update binary integer programming neural networks large deviations normal approximation Poisson approximation

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The capacity of majority rule

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RANDOM STRUCTURES & ALGORITHMS 1998年第1期12卷 83-109页

作者： Fang, SC Venkatesh, SS Univ Penn Dept Elect Engn Philadelphia PA 19104 USA

Let B-n = {-1,1}(n) denote the vertices of the n-dimensional cube. Let U(m) be a random m-element subset of B-n and suppose w is an element of B-n is a vertex closest to the centroid of U(m). Using a large deviation, multivariate local limit theorem due to Richter, we show that n/pi log n is a threshold function for the property that the convex hull of U(m) is contained in the positive half-space determined by w. The decision problem considered here is an instance of binary integer programming, and the algorithm selecting w as the vertex closest to the centroid of U(m) has been previously dubbed majority rule in the context of learning binary weights for a perceptron. (C) 1998 John Wiley & Sons, Inc.

关键词： random polytopes capacity threshold function neuron perceptron majority rule binary integer programming

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Design of a WSN for the Sampling of Environmental Variability in Complex Terrain

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SENSORS 2014年第11期14卷 21826-21842页

作者： Martin-Tardio, Miguel A. Felicisimo, Angel M. Univ Extremadura Ctr Univ Merida Merida 06800 Spain

In-situ environmental parameter measurements using sensor systems connected to a wireless network have become widespread, but the problem of monitoring large and mountainous areas by means of a wireless sensor network (WSN) is not well resolved. The main reasons for this are: (1) the environmental variability distribution is unknown in the field;(2) without this knowledge, a huge number of sensors would be necessary to ensure the complete coverage of the environmental variability and (3) WSN design requirements, for example, effective connectivity (intervisibility), limiting distances and controlled redundancy, are usually solved by trial and error. Using temperature as the target environmental variable, we propose: (1) a method to determine the homogeneous environmental classes to be sampled using the digital elevation model (DEM) and geometric simulations and (2) a procedure to determine an effective WSN design in complex terrain in terms of the number of sensors, redundancy, cost and spatial distribution. The proposed methodology, based on geographic information systems and binary integer programming can be easily adapted to a wide range of applications that need exhaustive and continuous environmental monitoring with high spatial resolution. The results show that the WSN design is perfectly suited to the topography and the technical specifications of the sensors, and provides a complete coverage of the environmental variability in terms of Sun exposure. However these results still need be validated in the field and the proposed procedure must be refined.

关键词： wireless sensor network geographic information systems binary integer programming environmental monitoring viewshed analysis network optimization

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A Generalized Assessment of the Impact of Regionalization and Provider Learning on Patient Outcomes

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MEDICAL DECISION MAKING 2016年第8期36卷 990-998页

作者： Zhang, Yun Lee, Shoou-Yih D. Gilleskie, Donna B. Sun, Yepeng Padakandla, Arun Jacobs, Bruce L. Montgomery, Jeffery S. Montie, James E. Wei, John T. Hollenbeck, Brent K. Univ Michigan Hlth Syst Ann Arbor MI USA Univ Michigan Sch Publ Hlth Room 113wBldg 162800 Plymouth Rd Ann Arbor MI 48109 USA Univ North Carolina Chapel Hill Chapel Hill NC USA LLamasoft Inc Ann Arbor MI USA Univ Michigan Room 113wBldg 162800 Plymouth Rd Ann Arbor MI 48109 USA Univ Pittsburgh Pittsburgh PA 15260 USA

We present a generalized model to assess the impact of regionalization on patient care outcomes in the presence of heterogeneity in provider learning. The model characterizes best regionalization policies as optimal allocations of patients across providers with heterogeneous learning abilities. We explore issues that arise when solving for best regionalization, which depends on statistically estimated provider learning curves. We explain how to maintain the problem's tractability and reformulate it into a binary integer program problem to improve solvability. Using our model, best regionalization solutions can be computed within reasonable time using current-day computers. We apply the model to minimally invasive radical prostatectomy and estimate that, in comparison to current care delivery, within-state regionalization can shorten length of stay by at least 40.8%.

关键词： volume-outcome relationship learning care regionalization binary integer programming

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Co-Optimization of Control and Actuator Selection for Cyber-Physical Systems

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IFAC-PapersOnLine 2018年第23期51卷 118-123页

作者： Chang, Chin-Yao Martínez, Sonia Cortés, Jorge Department of Mechanical and Aerospace Engineering University of California San Diego United States

This paper considers actuator selection problems which aim to maintain control performance of dynamical systems, and minimize operational costs or wear-and-tear of the actuators. The logical controls of actuators make the problem combinatorial, which make exhaustive search impractical. An actuator selection problem can be cast as a binary-integer programming with bilinear matrix inequalities (BIBMIs). In this paper, we first show that such non-convex optimization can be equivalently reformulated as an optimization problem with non-convexities restricted to binary decision variables. We next consider a continuous optimization which is equivalent to the BIBMIs, and leverage the continuous reformulation to derive a branch-and-bound method employing bound refinement. Numerical simulations demonstrate the effectiveness of the proposed approach. © 2018

关键词： integer programming Actuators Branch and bound method Convex optimization Cyber Physical System Dynamical systems Embedded systems Matrix algebra Actuator selections Bilinear matrix inequality binary integer programming Co optimization Continuous optimization Control performance Nonconvex optimization Optimization problems

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