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LOCATION OF DEPOTS IN A SUGAR-BEET DISTRIBUTION-SYSTEM

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1984年第3期18卷 396-402页

作者： JASINSKA, E WOJTYCH, E Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warszawa Poland

Two solution procedures developed for the problem of optimally designing a sugar-beet distribution system for a Polish sugar enterprise are described. The solution involved determining the optimal number, size, and location for distribution depots and coordinating sugar-beet flows from farms. Flows were delivered directly to sugar mills or indirectly through depots to minimize the costs of transportation and depot investment and operation. Piecewise linear functions were used to estimate operating costs for candidate depots according to their minimum and maximum throughput limits. Both solution procedures - one based on a heuristic method and the other using MPSX (Mathematical programming System Extended) and MIP (Mixed integer programming) systems for a reduced version of the problem - were found to efficiently yield high-quality solutions.

关键词： Depot linear integer programming location

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A novel power quality monitoring allocation algorithm

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IEEE TRANSACTIONS ON POWER DELIVERY 2006年第2期21卷 768-777页

作者： Eldery, MA El-Saadany, EF Salama, MMA Vannelli, A Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Distributed measurement architecture, due to its efficiency, is used to monitor the power quality in the electric systems. This paper introduces a novel algorithm to determine the optimum allocations of power quality monitors to reduce the cost of the distributed monitoring system taking into account the data redundancy. The optimization problem is formulated as a covering and packing one which can be manipulated by integer linear programm algorithms. The appropriate constraints are deduced by the electrical circuit topology independent of the load parameters. In addition, the problem is adopted to a general form which can be implemented by any optimization package. The proposed algorithm is applied to five different systems for validation. Then, the results are discussed in terms of the cost and data redundancy.

关键词： covering and packing deregulation linear integer programming power quality power quality info nodes (PQIN) power quality monitoring TOMLAB

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A combinatorial model and algorithm for globally searching community structure in complex networks

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2012年第4期23卷 425-442页

作者： Zhang, Xiang-Sun Li, Zhenping Wang, Rui-Sheng Wang, Yong Chinese Acad Sci Acad Math & Syst Sci Beijing 100080 Peoples R China Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China Penn State Univ Dept Phys University Pk PA 16802 USA

Community structure is one of the important characteristics of complex networks. In the recent decade, many models and algorithms have been designed to identify communities in a given network, among which there is a class of methods that globally search the best community structure by optimizing some modularity criteria. However, it has been recently revealed that these methods may either fail to find known qualified communities (a phenomenon called resolution limit) or even yield false communities (the misidentification phenomenon) in some networks. In this paper, we propose a new model which is immune to the above phenomena. The model is constructed by restating community identification as a combinatorial optimization problem. It aims to partition a network into as many qualified communities as possible. This model is formulated as a linear integer programming problem and its NP-completeness is proved. A qualified min-cut based bisecting algorithm is designed to solve this model. Numerical experiments on both artificial networks and real-life complex networks show that the combinatorial model/algorithm has promising performance and can overcome the limitations in existing algorithms.

关键词： Complex network Community structure Heuristic algorithm linear integer programming NP-hard

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Mathematical programming Approach for Revenue Maximization in Cloud Federations

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2017年第1期5卷 99-111页

作者： Hadji, Makhlouf Zeghlache, Djamal IRT SystemX Palaiseau France Telecom SudParis Inst Mines Telecom Evry Essonne France CNRS UMR 5157 Paris France Dept Telecom SudParis Wireless Networks Serv Inst Mines Telecom Evry Essonne France Dept Telecom SudParis Multimedia Serv Inst Mines Telecom Evry Essonne France Telecom SudParis Evry Essonne France

This paper assesses the benefits of cloud federation for cloud providers. Outsourcing and insourcing are explored as means to maximize the revenues of the providers involved in the federation. An exact method using a linear integer program is proposed to optimize the partitioning of the incoming workload across the federation members. A pricing model is suggested to enable providers to set their offers dynamically and achieve highest revenues. The conditions leading to highest gains are identified and the benefits of cloud federation are quantified.

关键词： Cloud federation resource allocation mathematical programming linear integer programming pricing

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Gated recurrent unit based demand response for preventing voltage collapse in a distribution system

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第6期28卷 3319-3334页

作者： Gundu, Venkateswarlu Simon, Sishaj Pulikottil Sundareswaran, Kinattingal Panugothu, Srinivasa Rao Nayak Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

This paper presents the application of deep learning algorithms towards demand response management. Demand limit violation and voltage stability are the major problems associated with a secondary distribution system. These problems are solved using demand response models by day ahead scheduling loads at every 15 min interval through linear integer programming and based on short term forecasting of load (kW). A new architecture for short term load forecasting is presented namely gated recurrent unit in which statistical analysis is carried out to get the optimal architecture of the neural network model. Reliability indices such as loss of load probability (LOLP) is evaluated to handle uncertainties that may occur in forecasting due to overestimation and underestimation. Here a novel dynamic power flow is carried out to check limit violation of the voltage. Also, scheduling is performed for two types of loads namely deferrable with interruptible and deferrable with uninterruptable, when either of maximum demand or voltage limit violation occurs. Finally, the suggested model is validated on a modified 12 bus radial distribution system. The result analysis shows that the suggested gated recurrent unit minimizes the forecast error and demand response program schedules household appliances without a demand limit violation and ensures the prevention of voltage collapse.

关键词： Long short-term memory load forecasting gated recurrent unit linear integer programming voltage stability analysis deep learning

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Developing model-based software to optimise wheat storage and transportation: A real-world application

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APPLIED SOFT COMPUTING 2013年第2期13卷 1074-1084页

作者： Asgari, Nasrin Farahani, Reza Zanjirani Rashidi-Bajgan, Hannaneh Sajadieh, Mohsen S. Kingston Univ Kingston Business Sch Dept Informat & Operat Management Kingston Upon Thames KT1 2EE Surrey England Univ Portsmouth Dept Math Logist & Management Math Grp Portsmouth PO1 2UP Hants England Islamic Azad Univ Karaj Branch Young Researchers Club Alborz Iran Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper investigates a real-world case of a logistical management problem. We determine the optimal amounts of wheat to be transported from each producing province to each consuming province per month across the year. The problem was formulated as a linear integer programming (LIP) model, which could then be solved using LINGO optimisation software. As the LIP model needs to be run each month, a genetic algorithm (GA) was developed to solve the real-size problems in a reasonable time period. The solutions obtained by LINGO are compared with those obtained from the GA and the results show that the developed GA is efficient in terms of computational time and the quality of the solutions obtained. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： Agriculture Inventory Transportation linear integer programming Genetic algorithm

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Tripartite Active Learning for Interactive Anomaly Discovery

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IEEE ACCESS 2019年 7卷 63195-63203页

作者： Zhu, Yanqiao Yang, Kai Tongji Univ Sch Software Engn Shanghai 201800 Peoples R China Tongji Univ Dept Comp Sci Shanghai 201800 Peoples R China

Most existing approaches to anomaly detection focus on statistical features of the data. However, in many cases, users are merely interested in a subset of the statistical outliers depending on the specific domain of interest, e.g., network attacks or financial fraud. The instruction from human experts is therefore indispensable in building predictive models in such applications. However, obtaining labels from human experts is time-consuming and expensive. Obtaining labels from nonexpert labelers are relatively easy and cost-effective. However, the labeling accuracy of a nonexpert is usually difficult to assess. Therefore, it remains open to leverage both the machine intelligence and the knowledge from labelers with diverse backgrounds to construct a machine learning model for domain-specific anomaly detection. To this end, this paper proposes a framework of tripartite active learning for interactive anomaly discovery in large datasets based on crowdsourced labels. This tripartite active learning method consists of two stages. In the first stage, an unsupervised learning algorithm is employed to extract statistical outliers from the dataset. This algorithm is of low computational complexity as well as memory requirement and thus well suited for large datasets. We then develop an iterative algorithm consisting of two steps. The algorithm first evaluates and trains labelers based on gold instances provided by the expert labelers. Then, it assigns the most informative samples to its most confident labeler for relabeling and update the detector based on new labels. The capacity constraints are taken into account in the active learning approach to guarantee the fair allocation of labeling instances as well as robustness against erroneous labels. It is seen through experiments that the proposed algorithm provides an effective means for interactive anomaly detection. As far as we are aware of, this is the first work that considers designing a tripartite machine learning sy

关键词： Active learning interactive artificial intelligence anomaly detection linear integer programming human training

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Balanced treatment incomplete block designs through integer programming

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第8期46卷 3728-3737页

作者： Mandal, B. N. Gupta, V. K. Parsad, Rajender ICAR Indian Agr Stat Res Inst Lib Ave New Delhi 110012 India

An algorithm is presented to construct balanced treatment incomplete block (BTIB) designs using a linear integer programming approach. Construction of BTIB designs using the proposed approach is illustrated with an example. A list of efficient BTIB designs for 2 v 12, v + 1 b 50, 2 k min(10, v), r 10, r(0) 20 is provided. The proposed algorithm is implemented as part of an R package.

关键词： Balanced treatment incomplete block design efficiency linear integer programming

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A fuzzy hybrid project portfolio selection method using Data Envelopment Analysis, TOPSIS and integer programming

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第22期42卷 8432-8444页

作者： Tavana, Madjid Keramatpour, Mehdi Santos-Arteaga, Francisco J. Ghorbaniane, Esmail La Salle Univ Business Syst & Analyt Dept Business Analyt Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Islamic Azad Univ Qazvin Branch Dept Ind Engn Qazvin Iran Univ Complutense Madrid Inst Complutense Estudios Int Pozuelo 28223 Spain

Project selection and resource allocation are critical issues in project-based organizations. These organizations are required to plan, evaluate, and control their projects in accordance with the organizational mission and objectives. In this study, we propose a three-stage hybrid method for selecting an optimal combination of projects. We obtain the maximum fitness between the final selection and the project initial rankings while considering various organizational objectives. The proposed model is comprised of three stages and each stage is composed of several steps and procedures. We use Data Envelopment Analysis (DEA) for the initial screening, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking the projects, and linear integer programming (IP) for selecting the most suitable project portfolio in a fuzzy environment according to organizational objectives. Finally, a case study is used to demonstrate the applicability of the proposed method and exhibit the efficacy of the algorithms and procedures. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Project portfolio selection Data Envelopment Analysis Fuzzy TOPSIS linear integer programming Augmented scores

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The Clustered Prize-Collecting Arc Routing Problem

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TRANSPORTATION SCIENCE 2009年第3期43卷 287-300页

作者： Araoz, Julian Fernandez, Elena Franquesa, Carles Tech Univ Catalonia Stat & Operat Res Dept Barcelona 08034 Spain Univ Simon Bolivar Caracas 89000 Venezuela Tech Univ Catalonia Dept Comp Sci Barcelona 08034 Spain

Prize-collecting arc routing problems are arc routing problems where, in addition to the cost function, there is a profit function on the edges that must only be taken into account the first time that an edge is traversed. This work presents the clustered prize-collecting arc routing problem where there are clusters of arcs and it is required that all or none of the edges of a cluster be serviced. The paper studies properties and dominance conditions used for formulating the problem as a linear integer program. An exact algorithm for finding an optimal solution to the problem is also proposed. At the root node of the enumeration tree, the algorithm generates upper and lower bounds obtained from solving an iterative linear programming-based algorithm in which violated cuts are generated when possible. A simple heuristic that generates feasible solutions provides lower bounds at each iteration. The numerical results from a series of computational experiments with various types of instances illustrate the good behavior of the algorithm. Over 75% of the instances were solved at the root node, and the remaining instances were solved with a small additional computational effort.

关键词： linear integer programming arc routing problems

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