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A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and integer programming

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ALGORITHMS 2020年第5期13卷

作者： Zhu, Jianshen Wang, Chenxi Shurbevski, Aleksandar Nagamochi, Hiroshi Akutsu, Tatsuya Kyoto Univ Dept Appl Math & Phys Kyoto 6068501 Japan Kyoto Univ Inst Chem Res Bioinformat Ctr Uji 6110011 Japan

Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method.

关键词： mixed integer linear programming QSAR/QSPR molecular design

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Stealthy Cyberattacks on Loads and Distributed Generation Aimed at Multi Transmission Line Congestions in Smart Grids

Stealthy Cyberattacks on Loads and Distributed Generation Ai...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Khazaei, Javad Penn State Univ Elect Engn Harrisburg PA 17057 USA

ISBN: (纸本)9781665405072

Developing attack models is the first step to understand cyberattacks in smart grids and develop countermeasures. In this paper, a three-level nonlinear programming formulation is proposed for false data injection (FDI) cyberattacks that could result in multiple transmission line congestions without being detected by conventional bad data detection (BDD) algorithms. The model is then converted to a mixed integer linear programming (MILP) formulation to guarantee a global optimum exists. A detection framework based on recursive least-square estimation (RLSE) is developed that can successfully detect the stealthy FDIs. The developed model with the detection framework is validated through various case studies in IEEE 118-bus benchmark.

关键词： Power transmission lines Estimation programming Benchmark testing Smart grids mixed integer linear programming Distributed power generation

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Sustainable, multimodal and reliable supply chain design

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ANNALS OF OPERATIONS RESEARCH 2020年第1期292卷 47-70页

作者： Kabadurmus, Ozgur Erdogan, Mehmet S. Yasar Univ Dept Int Logist Management TR-35100 Izmir Turkey

Emerging issues and new challenges of globalization have forced companies to design their supply chains for not only minimizing cost but also considering other factors. Supply chains are exposed to new environmental regulations to reduce their carbon emissions and compelled to consider other overlooked factors, such as risk. In this paper, we consider a multi-echelon multimodal supply chain network design problem with multiple products and components that take economic, environmental and risk factors into account. The problem is modeled as a mixed integer linear programming model and constrained by a carbon cap-and-trade scheme and a risk threshold. This novel problem realistically portrays the supply chain network design considering sustainability and reliability factors simultaneously. The proposed model has been tested on randomly generated hypothetical but realistic test instances. The impacts of different risk thresholds and unit carbon prices on the supply chain cost, risk and emissions are analyzed. The effects of multimodal transportation modes on cost, risk and emissions are also tested. Results prove that using multimodal transportation decreases supply chain cost and carbon emission. In addition, the total supply chain cost and carbon emission increase if the decision maker is risk-averse. The choice of transportation modes is sensitive only to emission levels.

关键词： Supply chain design Sustainability Supply chain risk Multimodal transportation mixed integer linear programming

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Solving parallel machine problems with delivery times and tardiness objectives

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ANNALS OF OPERATIONS RESEARCH 2020年第1-2期285卷 315-334页

作者： Maecker, Soehnke Shen, Liji WHU Otto Beisheim Sch Management Chair Operat Management Burgpl 2 D-56179 Vallendar Germany

This paper studies the NP-hard problem of scheduling jobs on identical parallel machines with machine-dependent delivery times to minimize the total weighted tardiness. A mixed integer linear programming formulation is presented that does not require machine-indexed variables due to a transformation of the problem. A variable neighborhood search (VNS) algorithm is proposed incorporating a local search that utilizes fast evaluation techniques (FET) to significantly improve computational efficiency of the search in four different neighborhoods. In experiments, the VNS is compared with other solution approaches on a large set of randomly generated test instances. Additionally, results for the computational benefits of our FETs are reported.

关键词： Parallel machine scheduling Total weighted tardiness Delivery times mixed integer linear programming Metaheuristics

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Two-stage bond portfolio optimization and its application to Saudi Sukuk Market

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ANNALS OF OPERATIONS RESEARCH 2020年第1期288卷 1-43页

作者： Alreshidi, Nasser Aedh Mrad, Mehdi Subasi, Ersoy Subasi, Munevver Mine Northern Border Univ Dept Math Ar Ar Northern Boarde Saudi Arabia Florida Inst Technol Dept Math Sci 150 W Univ Blvd Melbourne FL 32901 USA King Saud Univ Dept Ind Engn POB 800 Riyadh 11421 Saudi Arabia Florida Inst Technol Dept Comp Engn & Sci 150 W Univ Blvd Melbourne FL 32901 USA

We consider a two-stage stochastic bond portfolio optimization problem, where an investor aims to optimize the cost of bond portfolio under different scenarios while ensuring predefined liabilities during a given planning horizon. The investor needs to optimally decide whether to buy, hold, or sell bonds based upon present market conditions under different scenarios and varying assumptions, where the scenarios are determined based on interest rates and buying prices of the bonds. Three stochastic integer programming models are proposed and tested using real data from Saudi Sukuk (Bond) Market. The obtained results demonstrate the varying optimal decisions made to manage bond portfolio over the two stages. In addition, the three stochastic programming models for bond portfolio optimization are tested on a large set of randomly generated instances similar to the Saudi Sukuk (Bond) Market. The results of computational experiments attest the efficiency of the proposed models.

关键词： Two-stage programming Bond portfolio optimization mixed integer linear programming

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Jaccard Median for Ego-network Segmentation

Jaccard Median for Ego-network Segmentation

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IEEE International Conference on Data Mining (ICDM)

作者： Haodi Zhong Grigorios Loukides Alessio Conte Solon P. Pissis Xidian University China King’s College London United Kingdom University of Pisa Italy CWI The Netherlands Vrije Universiteit The Netherlands

An ego-network is a graph representing the interactions of a node (ego) with its neighbors and the interactions among those neighbors. A sequence of ego-networks having the same ego can thus model the evolution of these interactions over time. We introduce the problem of segmenting a sequence of ego-networks into k segments, for any given integer k. Each segment is represented by a summary network, and the goal is to minimize the total loss of representing k segments by k summaries. The problem allows partitioning the sequence into homogeneous segments with respect to the activities or properties of the ego (e.g., to identify time periods when a user acquired different circles of friends in a social network) and to compactly represent each segment with a summary. The main challenge is to construct a summary that represents a collection of ego-networks with minimum loss. To address this challenge, we employ Jaccard Median (JM), a well-known NP-hard problem for summarizing sets, for which, however, no effective and efficient algorithms are known. We develop a series of algorithms for JM offering different effectiveness/efficiency trade-offs: (I) an exact exponential-time algorithm, based on mixed integer linear programming and (II) exact and approximation polynomial-time algorithms for minimizing an upper bound of the objective function of JM. By building upon these results, we design two algorithms for segmenting a sequence of ego-networks that are effective, as shown experimentally.

关键词： Upper bound Social networking (online) NP-hard problem Buildings Approximation algorithms linear programming mixed integer linear programming

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Performance Modeling and Analysis of WOM Optical Networks under Wavelength Continuity Constraint using MILP

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2020年第2期13卷 203-211页

作者： Bhalaik, Swati Sharma, Ashutosh Kumar, Rajiv Sharma, Neeru Jaypee Univ Informat & Technol Dept Elect & Commun Solan India

Objective: Optical networks exploit the Wavelength Division Multiplexing (WDM) to meet the ever-growing bandwidth demands of upcoming communication applications. This is achieved by dividing the enormous transmission bandwidth of fiber into smaller communication channels. The major problem with WDM network design is to find an optimal path between two end users and allocate an available wavelength to the chosen path for the successful data transmission. Methods: This communication over a WDM network is carried out through lightpaths. The merging of all these lightpaths in an optical network generates a virtual topology which is suitable for the optimal network design to meet the increasing traffic demands. But, this virtual topology design is an NP -hard problem. This paper aims to explore mixed integer linear programming (MILP) framework to solve this design issue. Results: The comparative results of the proposed and existing mathematical models show that he proposed algorithm outperforms with the various performance parameters. Conclusion: Finally, it is concluded that network congestion is reduced marginally in the overall perfortnance of the network.

关键词： Network design mixed integer linear programming quality of service virtual topology mixed integer linear programming constraint mixed integer linear programming

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A Stochastic Model for Prioritized Outpatient Scheduling in a Radiology Center

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INTERNATIONAL JOURNAL OF ENGINEERING 2020年第4期33卷 598-606页

作者： Abtahi, Z. Sahraeian, R. Rahmani, D. Shahed Univ Coll Engn Dept Ind Engn Tehran Iran KN Toosi Univ Technol Dept Ind Engn Tehran Iran

This paper discussed the scheduling problem of outpatients in a radiology center with an emphasis on priority. For more compatibility to real-world conditions, we assume that the elapsed times in different stages to be uncertain that follow from the specific distribution function. The objective is to minimize outpatients' total spent time in a radiology center. The problem is formulated as a flexible open shop scheduling problem and a stochastic programming model. By considering the specific distribution function for uncertain variables, deterministic mixed integer linear programming (MILP) is developed such that the proposed problem can be solved by a linear programming solver in small size. Besides an effective heuristic method is proposed for the moderate size problem. To indicate the applicability of the proposed model, it has been applied to a real radiology center. The results from the proposed optimization models indicate an increase in outpatients' satisfaction, as well as the improvement of the efficiency and productivity of the radiology center.

关键词： Scheduling Stochastic Flexible Open Shop Prioritized Outpatient Heuristic mixed integer linear programming

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Logical analysis of multiclass data with relaxed patterns

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ANNALS OF OPERATIONS RESEARCH 2020年第1期287卷 11-35页

作者： Bain, Travaughn C. Avila-Herrera, Juan F. Subasi, Ersoy Subasi, Munevver Mine Florida Inst Technol Dept Math Sci 150 W Univ Blvd Melbourne FL 32901 USA Univ Nacl Escuela Matemat Escuela Informat San Jose Costa Rica Florida Inst Technol Dept Comp Engn & Sci 150 W Univ Blvd Melbourne FL 32901 USA

An efficient and robust algorithm based on mixed integer linear programming is proposed to extend the Logical Analysis of Data (LAD) methodology to solve multiclass classification problems, where One-vs-Rest learning models are constructed to classify observations in predefined classes. The proposed algorithm uses two control parameters, homogeneity and prevalence, for identifying relaxed (fuzzy) patterns in multiclass datasets. The utility of the proposed method is demonstrated through experiments on multiclass benchmark datasets. Numerical experiments show that the efficiency and performance of the proposed multiclass LAD method with relaxed patterns is comparable to, if not better than, those of the previously developed LAD based multiclass classification as well as other well-known supervised learning methods.

关键词： Supervised learning Multiclass classification Logical analysis of data mixed integer linear programming

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A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks

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ALGORITHMS 2020年第1期13卷

作者： D'Andreagiovanni, Fabio Lakhlef, Hicham Nardin, Antonella CNRS Natl Ctr Sci Res F-75016 Paris France Univ Technol Compiegne Sorbonne Univ CNRS Heudiasyc UMR 7253 CS 60319 F-60203 Compiegne France Univ Roma Tre Dept Engn I-00146 Rome Italy

Because of the introduction and spread of the second generation of the Digital Video Broadcasting-Terrestrial standard (DVB-T2), already active television broadcasters and new broadcasters that have entered in the market will be required to (re)design their networks. This is generating a new interest for effective and efficient DVB optimization software tools. In this work, we propose a strengthened binary linear programming model for representing the optimal DVB design problem, including power and scheduling configuration, and propose a new matheuristic for its solution. The matheuristic combines a genetic algorithm, adopted to efficiently explore the solution space of power emissions of DVB stations, with relaxation-guided variable fixing and exact large neighborhood searches formulated as integer linear programming (ILP) problems solved exactly. Computational tests on realistic instances show that the new matheuristic performs much better than a state-of-the-art optimization solver, identifying solutions associated with much higher user coverage.

关键词： telecommunications DVB-T antenna design network optimization mixed integer linear programming tight linear relaxations matheuristics ILP heuristics

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