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Lecture Notes in Computer Science 9869
Commenced Publication in 1973Founding and Former Series Editors:Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen
Editorial Board
David HutchisonLancaster University, Lancaster, UK
Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA
Josef KittlerUniversity of Surrey, Guildford, UK
Jon M. KleinbergCornell University, Ithaca, NY, USA
Friedemann MatternETH Zurich, Zürich, Switzerland
John C. MitchellStanford University, Stanford, CA, USA
Moni NaorWeizmann Institute of Science, Rehovot, Israel
C. Pandu RanganIndian Institute of Technology, Madras, India
Bernhard SteffenTU Dortmund University, Dortmund, Germany
Demetri TerzopoulosUniversity of California, Los Angeles, CA, USA
Doug TygarUniversity of California, Berkeley, CA, USA
Gerhard WeikumMax Planck Institute for Informatics, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/7407
Yury Kochetov • Michael KhachayVladimir Beresnev • Evgeni NurminskiPanos Pardalos (Eds.)
Discrete Optimizationand Operations Research9th International Conference, DOOR 2016Vladivostok, Russia, September 19–23, 2016Proceedings
123
EditorsYury KochetovSobolev Institute of MathematicsNovosibirskRussia
Michael KhachayKrasovsky Institute of Mathematics andMechanics
EkaterinburgRussia
Vladimir BeresnevSobolev Institute of MathematicsNovosibirskRussia
Evgeni NurminskiFar Eastern Federal UniversityVladivostikRussia
Panos PardalosUniversity of FloridaGainesville, FLUSA
ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Computer ScienceISBN 978-3-319-44913-5 ISBN 978-3-319-44914-2 (eBook)DOI 10.1007/978-3-319-44914-2
Library of Congress Control Number: 2016948223
LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues
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Preface
This volume contains the proceedings of the 9th International Conference on DiscreteOptimization and Operations Research (DOOR 2016), held in Vladivostok, Russia,during September 19–23, 2016. It was organized by the Far Eastern Federal University,Sobolev Institute of Mathematics, Krasovsky Institute of Mathematics and Mechanics,Novosibirsk State University, and the Higher School of Economics in NizhnyNovgorod.
Previous conferences took place at the Sobolev Institute of Mathematics, Novosi-birsk, in 1996, 1998, 2000, 2002, and 2004. The 6th conference was held in theRussian Far East in a picturesque setting on the shore of the Japanese Sea nearVladivostok in 2007. The 7th one, in 2010, was held in the Altay Mountains. The 8thevent took place in Novosibirsk again. DOOR is part of a series of annual internationalconferences on optimization and operations research that covers a wide range of topicsin mathematical programming and its applications, integer programming and polyhe-dral combinatorics, bi-level programming and multi-criteria optimization, optimizationproblems in machine learning and data mining, discrete optimization in scheduling,routing, bin packing, locations, and optimization problems on graphs, computationalcomplexity, and polynomial time approximation. The main purpose of the conferenceis to provide a forum where researchers can exchange ideas, identify promisingdirections for research and application domains, and foster new collaborations.
In response to the call for papers, DOOR 2016 received 181 submissions. Papersincluded in this volume were carefully selected by the Program Committee on the basisof reports from two or more reviewers. Only 39 submissions were selected for inclu-sion in this volume. Nine invited talks by eminent speakers are also included here.
We thank all the Program Committee members and external reviewers for theircooperation. We also thank the Organizing Committee members for their efforts.Finally, we thank our sponsors, the Russian Foundation for Basic Research, the FarEastern Federal University, Novosibirsk State University, the Laboratory of Algorithmsand Technologies for Networks Analysis (LATNA), the Higher School of Economicsin Nizhny Novgorod, and Alfred Hofmann from Springer for supporting our project.
September 2016 Yury KochetovMichael KhachayVladimir BeresnevEvgeni Nurminski
Panos Pardalos
Organization
Program Chairs
Evgeni Nurminski Far Eastern Federal University, RussiaVladimir Beresnev Sobolev Institute of Mathematics, RussiaPanos Pardalos University of Florida, USA
Program Committee
Ekaterina Alekseeva Sobolev Institute of Mathematics, RussiaEdilkhan Amirgaliev Suleyman Demirel University, KazakhstanOleg Burdakov Linköping University, SwedenIgor Bykadorov Sobolev Institute of Mathematics, RussiaEmilio Carrizosa Universidad de Sevilla, SpainYair Censor University of Haifa, IsraelIvan Davydov Sobolev Institute of Mathematics, RussiaVladimir Deineko The University of Warwick, UKStefan Dempe TU Bergakademie Freiberg, GermanyAnton Eremeev Sobolev Institute of Mathematics, RussiaAdil Erzin Sobolev Institute of Mathematics, RussiaYury Evtushenko Dorodnicyn Computing Centre, RussiaEdward Gimadi Sobolev Institute of Mathematics, RussiaAlexandr Grigoriev Maastricht University, The NetherlandsFlorian Jaehn Universität Augsburg, GermanyJosef Kallrath TU Darmstadt, GermanyValery Kalyagin Higher School of Economics, RussiaAlexander Kelmanov Sobolev Institute of Mathematics, RussiaMichael Khachay Krasovsky Institute of Mathematics and Mechanics, RussiaOleg Khamisov Melentiev Energy Systems Institute, RussiaAndrey Kibzun Moscow Aviation Institute, RussiaYury Kochetov Sobolev Institute of Mathematics, RussiaAlexander Kolokolov Sobolev Institute of Mathematics, RussiaAlexander Kononov Sobolev Institute of Mathematics, RussiaMikhail Kovalev Belarusian State University, BelarusNikolay Kuzyurin Institute for System Programming, RussiaBertrand Lin National Chiao Tung University, TaiwanBertrand Mareschal Université Libre de Bruxelles, BelgiumAthanasios Migdalas Luleå University of Technology, SwedenNenad Mladenović University of Valenciennes, FranceUrfat Nuriyev Ege University, Turkey
Alexandr Plyasunov Sobolev Institute of Mathematics, RussiaArtem Pyatkin Sobolev Institute of Mathematics, RussiaSoumyendu Raha Indian Institute of Science, IndiaKonstantin Rudakov Dorodnicyn Computing Centre, RussiaYaroslav Sergeev Università della Calabria, ItalySergey Sevastianov Sobolev Institute of Mathematics, RussiaVadim Shmyrev Sobolev Institute of Mathematics, RussiaPetro Stetsuk Institute of Cybernetics, UkraineAlexander Strekalovsky Matrosov Institute for System Dynamics and Control
Theory, RussiaMaxim Sviridenko Yahoo, USAEl-Ghazali Talbi University of Lille, CNRS, Inria, FranceYury Zhuravlev Dorodnicyn Computing Centre, Russia
Organizing Committee
Natalia Shamry Far Eastern Federal University, RussiaYury Kochetov Sobolev Institute of Mathematics, RussiaMikhail Khachay Krasovsky Institute of Mathematics and Mechanics, RussiaTimur Medvedev Higher School of Economics, RussiaEvgeniya Vorontsova Far Eastern Federal University, RussiaNina Kochetova Sobolev Institute of Mathematics, RussiaPolina Kononova Sobolev Institute of Mathematics, RussiaAndrey Velichko Far Eastern Federal University, Russia
Sponsors
Russian Foundation for Basic ResearchFar Eastern Federal University, VladivostokNovosibirsk State UniversityHigher School of Economics, Nizhny Novgorod
Additional Reviewers
Adamczyk, MarekAgeev, AlexanderAgo, TakanoriAizenberg, NataliaAlaev, PavelAlekseeva, EkaterinaAleskerov, FuadAntipin, AnatolyAntsyz, SergeyBagirov, AdilBaklanov, Artem
Basturk, NalanBerg, KimmoBevern, René VanBroersma, HajoBuzdalov, MaximChernykh, IlyaChubanov, SergeiCustic, AnteDavidović, TatjanaDavydov, IvanDempe, Stephan
Dessevre, GuillaumeDi Pillo, GianniDudko, OlgaDuplinskiy, ArtemDzhafarov, VakifEllero, AndreaFilatov, AlexanderFomin, FedorFunari, StefaniaGaudioso, ManlioGlebov, Aleksey
VIII Organization
Golikov, AlexanderGorelik, VictorGornov, AleksanderGoubko, MikhailGriewank, AndreasGrigoriev, AlexanderGrishagin, VladimirGruzdeva, TatianaHung, Hui-ChihIgnatov, DmitryIvanko, EvgenyIvanov, SergeyJung, VerenaKalashnykova, NataliyaKarakitsiou, AthanasiaKateshov, AndreyKatueva, YaroslavaKazakov, AlexanderKazakovtsev, LevKazansky, AlexanderKhachay, MichaelKhamidullin, SergeyKhamisov, OlegKhandeev, VladimirKhromova, OlgaKobylkin, KonstantinKokovin, SergeyKonnov, IgorKononov, AlexanderKononova, PolinaKonstantinova, ElenaKoshel, KonstantinKovalenko, JuliaKreuzen, VincentKutucu, HakanKuzjurin, NikolayKvasov, Dmitri
Lagutaeva, DariaLetsios, DimitriosLevanova, TatianaLin, Bertrand M.T.Lucarelli, GiorgioMalyshev, DmitriyMareschal, BertrandMazalov, VladimirMelnikov, AndreyMigdalas, AthanasiosMondrus, OlgaMorozov, AndreiNamm, RobertNaumov, AndreyNishihara, KoNorkin, VladimirNurminski, EvgeniOosterwijk, TimOrlovich, YuryOron, DanielPanin, ArtemPekarskii, SergeyPhilpott, AndyPlyasunov, AlexanderPopov, LeonidPopova, EwgeniyaPredtetchinski, ArkadiiRaha, SoumyenduRapoport, ErnstRomanovsky, JosephSandomirskaia, MarinaSavvateev, AlexeiSemenikhin, KonstantinSemenov, VladimirServakh, VladimirShafransky, YakovShary, Sergey
Shenmaier, VladimirShikhman, VladimirSidorov, AlexanderSidorov, DenisSidorov, SergeiSimanchev, RuslanSkarin, VladimirSloev, IgorSobol, VitalyStetsyuk, PetroStrusevich, VitalySudhölter, PeterSukhorukova, NadiaSuslov, NikitaSuvorov, AntonTakhonov, IvanTimmermans, VeerleTiskin, AlexanderTrionfetti, FedericoTsai, Yen-ShingTsidulko, OxanaTsoy, YuryUgurlu, OnurValeeva, AidaVasilyev, IgorVasin, AlexanderVegh, LaszloWijler, EtiënneWinokurow, AndrejYanovskaya, ElenaZabotin, IgorZalyubovskiy, SlavaZambalaeva, DolgorZhurbenko, NickolayZolotykh, Nikolai
Organization IX
Abstracts of Invited Talks
Algorithmic Issues in Energy-EfficientComputation
Evripidis Bampis
Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, Paris, [email protected]
Abstract. Energy efficiency has become a crucial issue in Computer Science.New hardware and system-based approaches are explored for saving energy inportable battery-operated devices, personal computers, or large server farms.The main mechanisms that have been developed for saving energy are the abilityof transitioning the device among multiple power states, and the use of dynamicvoltage scaling (speed scaling). These last years, there is also an increasinginterest in the development of algorithmic techniques for finding tradeoff-solutions between energy consumption and performance. In this talk, we willfocus on algorithmic techniques with provably good performances for funda-mental power management problems. Among the different models that havebeen developed in the literature, we will focus on the speed scaling model, thepower-down model and the combination of these two models that we will callthe power-down with speed scaling model.
Linear Superiorization for InfeasibleLinear Programming
Yair Censor and Yehuda Zur
Department of Mathematics, University of Haifa, Mt. Carmel, 3498838,Haifa, Israel
Abstract. Linear superiorization (abbreviated: LinSup) considers linear pro-gramming (LP) problems wherein the constraints as well as the objectivefunction are linear. It allows to steer the iterates of a feasibility-seeking iterativeprocess toward feasible points that have lower (not necessarily minimal) valuesof the objective function than points that would have been reached by the samefeasiblity-seeking iterative process without superiorization. Using a feasibility-seeking iterative process that converges even if the linear feasible set is empty,LinSup generates an iterative sequence that converges to a point that minimizesa proximity function which measures the linear constraints violation. In addition,due to LinSup’s repeated objective function reduction steps such a point willmost probably have a reduced objective function value. We present anexploratory experimental result that illustrates the behavior of LinSup on aninfeasible LP problem.
Modern Trends in Parameterized Algorithms
Fedor Fomin
University of Bergen, Bergen, [email protected]
We overview the recent progress in solving intractable optimization problems on planargraphs as well as other classes of sparse graphs. In particular, we discuss how toolsfrom Graph Minors theory can be used to obtain
– subexponential parameterized algorithms– approximation algorithms, and– preprocessing and kernelization algorithms
on these classes of graphs.
Short Survey on Graph Correlation Clusteringwith Minimization Criteria
Victor Il’ev1,2, Svetlana Il’eva2, and Alexander Kononov1
1 Sobolev Institute of Mathematics, Novosibirsk, Russia2 Omsk State University, Omsk, Russia
[email protected], [email protected]
Abstract. In clustering problems one has to partition a given set of objects intosome subsets (called clusters) taking into consideration only similarity of theobjects. One of the most visual formalizations of clustering is the graph clus-tering, that is, grouping the vertices of a graph into clusters taking into con-sideration the edge structure of the graph whose vertices are objects and edgesrepresent similarities between the objects.
In this short survey, we consider the graph correlation clustering problemswhere the goal is to minimize the number of edges between clusters and thenumber of missing edges inside clusters. We present a number of results ongraph correlation clustering including results on computational complexity andapproximability of different variants of the problems, and performance guar-antees of approximation algorithms for graph correlation clustering. Someresults on approximability of weighted versions of graph correlation clusteringare also presented.
Wardrop Equilibrium for Networkswith the BPR Latency Function
Jaimie W. Lien1, Vladimir V. Mazalov2, Anna V. Melnik3,and Jie Zheng4
1 Department of Decision Sciences and Managerial Economics,The Chinese University of Hong Kong, Shatin, Hong Kong, China
[email protected] Institute of Applied Mathematical Research,
Karelian Research Center, Russian Academy of Sciences,11, Pushkinskaya Street, Petrozavodsk, 185910, Russia
[email protected] Saint-Petersburg State University, Universitetskii Prospekt 35,
Saint-petrsburg, 198504, [email protected]
4 Department of Economics, School of Economics and Management,Tsinghua University, Beijing, 100084, [email protected]
Abstract. This paper considers a network comprised of parallel routes with theBureau of Public Road (BPR) latency function and suggests an optimal distri-bution method for incoming traffic flow. The authors analytically derive asystem of equations defining the optimal distribution of the incoming flow withminimum social costs, as well as a corresponding system of equations for theWardrop equilibrium in this network. In particular, the Wardrop equilibrium isapplied to the competition model with rational consumers who use the carrierswith minimal cost, where cost is equal to the price for service plus the waitingtime for the service. Finally, the social costs under the equilibrium and under theoptimal distribution are compared. It is shown that the price of anarchy can beinfinitely large in the model with strategic pricing.
Location Modeling in the Presence of Firmand Customer Competition
Athanasios Migdalas
ETS Institute, Lulea University of Technology, 971 87 Lulea, [email protected]
Location problems form a wide class of mathematical programming models, of greatinterest of both in practice and from the point of view of optimization theory. Facilitylocation problem aims at determining the optimal sites to locate facilities such as plants,warehouses, and/or distribution centers. Competitive location models (CFL) addition-ally incorporate the fact that location decisions have been or will be made by inde-pendent decision-makers who will subsequently compete with each other for marketshare, profit maximization etc. In addition decisions such as customers’ allocation andpricing policies may also be incorporated to the basic model.
The first paper dealing with the effect of competition in the location decisions is dueHotelling. Since then, a vast number of publications have been devoted to the subject.Sequential CFL problems are usually modeled as hierarchical or multi-level pro-gramming models. Such models are concerned with decision making problems thatinvolve multiple decision makers ordered within a hierarchical structure. The mostwell-known case is the so-called Stackelberg game in which decision makers of twodifferent levels with different, often conflicting, objectives are involved.
The research work dealing with the bi-level formulation of location problems islimited only to the competition among the locators. Customers are passively assignedto the facilities according to some criteria. A first attempt to study the influence ofmarket competition on location decisions is due to Tobin and Friesz.
In this talk we formulate and study a class of location problems where theautonomous decisions of the customers regarding the facilities from which they will beserved influence the locations decisions. The conditions under which customers maketheir choice of facilities to be served are in general complicated. We assume here thatevery customer will choose the facilities that minimize their own total transportationand waiting for service cost. Thus, concerning mathematical modeling we investigatefacility location problems not only in the presence of firm competition but also in thepresence of customer competition with respect to the quality level of the providedservices. We derive bi-level programming models which are interpreted and analyzedin game theoretic terms. The issues of optimality conditions, computational complexityand solution algorithms are also discussed.
References
1. Karakitsiou, A., Migdalas, A.: Locating facilities in a competitive environment, Optim. Lett.(2016). doi:10.1007/s11590-015-0963-7
2. Karakitsiou, A., Migdalas, A.: Nash type games in competitive facilities location. Int.J. Decision Support Syst. (2016, in print)
3. Karakitsiou, A.: Modeling Discrete Competitive Facilities Location. Springer Briefs inOptimization (2015)
Location Modeling in the Presence of Firm and Customer Competition XIX
A Review on Network Robustnessfrom an Information Theory Perspective
Tiago Schieber1,2, Martín Ravetti1, and Panos M. Pardalos3,4
1 Departamento de Engenharia de Produção, Universidade Federal de MinasGerais, Belo Horizonte, MG, Brazil
[email protected] Departamento de Engenharia de Produção,
Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte, MG, Brazil3 Center for Applied Optimization, Industrial and Systems Engineering,
University of Florida, Gainesville, FL, USA4 Laboratory of Algorithms and Technologies for Network Analysis,
National Research University Higher School of Economics,Nizhny Novgorod, Russia
Abstract. The understanding of how a networked system behaves and keeps itstopological features when facing element failures is essential in several appli-cations ranging from biological to social networks. In this context, one of themost discussed and important topics is the ability to distinguish similaritiesbetween networks. A probabilistic approach already showed useful in graphcomparisons when representing the network structure as a set of probabilitydistributions, and, together with the Jensen-Shannon divergence, allows toquantify dissimilarities between graphs. The goal of this article is to comparethese methodologies for the analysis of network comparisons and robustness.
An Iterative Approach for Searchingan Equilibrium in Piecewise Linear
Exchange Model
Vadim I. Shmyrev
Sobolev Institute of Mathematics, Novosibirsk, [email protected]
Abstract. The exchange model with piecewise linear separable concave utilityfunctions is considered. This consideration extends the author’s original approachto the equilibrium problem in a linear exchange model and its variations. Theconceptual base of this approach is the scheme of polyhedral complementarity. Ithas no analogs and made it possible to obtain the finite algorithms for somevariations of the exchange model. Especially simple algorithms arise for linearexchange model with fixed budgets (Fisher’s model). This is due to monotonicityproperty inherent in the models and potentiality of arising mappings. The algo-rithms can be considered as a procedure similar to the simplex-method of linearprogramming. It is natural to study applicability of the approach for more generalmodels. The considered piecewise linear version of the model reduces to a specialexchange model with upper bounds on variables and the modified conditions of thegoods’ balances. For such a model the monotonicity property is violated. But itremains, if upper bounds are substituted by financial limits on purchases. This is theidea of proposed iterative algorithm for initial problem. It is a generalization of ananalogue for linear exchange model.
Handling Scheduling Problemswith Controllable Parameters by Methods
of Submodular Optimization
Akiyoshi Shioura1, Natalia V. Shakhlevich2,and Vitaly A. Strusevich3
1 Tokyo Institute of Technology, Tokyo, Japan2 University of Leeds, Leeds, UK
3 Univeristy of Greenwich, London, [email protected]
Abstract. In this paper, we demonstrate how scheduling problems withcontrollable processing times can be reformulated as maximization linear pro-gramming problems over a submodular polyhedron intersected with a box.We explain a decomposition algorithm for solving the latter problem and discussits implications for the relevant problems of preemptive scheduling on a singlemachine and parallel machines.
Contents
Invited Talks
Algorithmic Issues in Energy-Efficient Computation . . . . . . . . . . . . . . . . . . 3Evripidis Bampis
Linear Superiorization for Infeasible Linear Programming . . . . . . . . . . . . . . 15Yair Censor and Yehuda Zur
Short Survey on Graph Correlation Clustering with Minimization Criteria . . . 25Victor Il’ev, Svetlana Il’eva, and Alexander Kononov
Wardrop Equilibrium for Networks with the BPR Latency Function . . . . . . . 37Jaimie W. Lien, Vladimir V. Mazalov, Anna V. Melnik, and Jie Zheng
A Review on Network Robustness from an Information Theory Perspective . . . 50Tiago Schieber, Martín Ravetti, and Panos M. Pardalos
An Iterative Approach for Searching an Equilibrium in Piecewise LinearExchange Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Vadim I. Shmyrev
Handling Scheduling Problems with Controllable Parameters by Methodsof Submodular Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Akiyoshi Shioura, Natalia V. Shakhlevich, and Vitaly A. Strusevich
Discrete Optimization
Constant-Factor Approximations for Cycle Cover Problems . . . . . . . . . . . . . 93Alexander Ageev
Precedence-Constrained Scheduling Problems Parameterizedby Partial Order Width . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
René van Bevern, Robert Bredereck, Laurent Bulteau,Christian Komusiewicz, Nimrod Talmon, and Gerhard J. Woeginger
A Scheme of Independent Calculations in a Precedence ConstrainedRouting Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Alexander G. Chentsov and Alexey M. Grigoryev
On Asymptotically Optimal Approach to the m-Peripatetic SalesmanProblem on Random Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Edward Kh. Gimadi, Alexey M. Istomin, and Oxana Yu. Tsidulko
Efficient Randomized Algorithm for a Vector Subset Problem . . . . . . . . . . . 148Edward Gimadi and Ivan Rykov
An Algorithm with Approximation Ratio 5/6 for the Metric Maximumm-PSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Aleksey N. Glebov and Anastasiya V. Gordeeva
An Approximation Algorithm for a Problem of Partitioning a Sequenceinto Clusters with Restrictions on Their Cardinalities . . . . . . . . . . . . . . . . . . 171
Alexander Kel’manov, Ludmila Mikhailova, Sergey Khamidullin,and Vladimir Khandeev
A Fully Polynomial-Time Approximation Scheme for a Special Caseof a Balanced 2-Clustering Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
Alexander Kel’manov and Anna Motkova
PTAS for the Euclidean Capacitated Vehicle Routing Problem in Rd . . . . . . . 193Michael Khachay and Roman Dubinin
On Integer Recognition over Some Boolean Quadric Polytope Extension . . . . 206Andrei Nikolaev
Variable Neighborhood Search-Based Heuristics for Min-Power SymmetricConnectivity Problem in Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . 220
Roman Plotnikov, Adil Erzin, and Nenad Mladenovic
On the Facets of Combinatorial Polytopes . . . . . . . . . . . . . . . . . . . . . . . . . 233Ruslan Simanchev and Inna Urazova
A Branch and Bound Algorithm for a Fractional 0-1 Programming Problem. . . 244Irina Utkina, Mikhail Batsyn, and Ekaterina Batsyna
Scheduling Problems
Approximating Coupled-Task Scheduling Problems with EqualExact Delays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Alexander Ageev and Mikhail Ivanov
Routing Open Shop with Unrelated Travel Times . . . . . . . . . . . . . . . . . . . . 272Ilya Chernykh
The 2-Machine Routing Open Shop on a Triangular TransportationNetwork. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
Ilya Chernykh and Ekaterina Lgotina
Mixed Integer Programming Approach to Multiprocessor Job Schedulingwith Setup Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
Anton V. Eremeev and Yulia V. Kovalenko
XXIV Contents
On Speed Scaling Scheduling of Parallel Jobs with Preemption . . . . . . . . . . 309Alexander Kononov and Yulia Kovalenko
Facility Location
Facility Location in Unfair Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . 325Vladimir Beresnev and Andrey Melnikov
Variable Neighborhood Descent for the Capacitated Clustering Problem . . . . 336Jack Brimberg, Nenad Mladenović, Raca Todosijević,and Dragan Urošević
A Leader-Follower Hub Location Problem Under Fixed Markups . . . . . . . . . 350Dimitrije D. Čvokić, Yury A. Kochetov, and Aleksandr V. Plyasunov
Tabu Search Approach for the Bi-Level Competitive Base StationLocation Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364
Ivan Davydov, Marceau Coupechoux, and Stefano Iellamo
Upper Bound for the Competitive Facility Location Problemwith Quantile Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373
Andrey Melnikov and Vladimir Beresnev
Mathematical Programming
Fast Primal-Dual Gradient Method for Strongly Convex MinimizationProblems with Linear Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
Alexey Chernov, Pavel Dvurechensky, and Alexander Gasnikov
An Approach to Fractional Programming via D.C. Constraints Problem:Local Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404
Tatiana Gruzdeva and Alexander Strekalovsky
Partial Linearization Method for Network Equilibrium Problemswith Elastic Demands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418
Igor Konnov and Olga Pinyagina
Multiple Cuts in Separating Plane Algorithms. . . . . . . . . . . . . . . . . . . . . . . 430Evgeni Nurminski
On the Parameter Control of the Residual Method for the Correctionof Improper Problems of Convex Programming . . . . . . . . . . . . . . . . . . . . . 441
Vladimir D. Skarin
On the Merit and Penalty Functions for the D.C. Optimization . . . . . . . . . . . 452Alexander S. Strekalovsky
Contents XXV
Mathematical Economics and Games
Application of Supply Function Equilibrium Model to Describe theInteraction of Generation Companies in the Electricity Market . . . . . . . . . . . 469
Natalia Aizenberg
Chain Store Against Manufacturers: Regulation Can Mitigate MarketDistortion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480
Igor Bykadorov, Andrea Ellero, Stefania Funari, Sergey Kokovin,and Marina Pudova
On the Existence of Immigration Proof Partition into Countriesin Multidimensional Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494
Valeriy M. Marakulin
Search of Nash Equilibrium in Quadratic n-person Game . . . . . . . . . . . . . . . 509Ilya Minarchenko
Applications of Operational Research
Convergence of Discrete Approximations of Stochastic ProgrammingProblems with Probabilistic Criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525
Andrey I. Kibzun and Sergey V. Ivanov
A Robust Leaky-LMS Algorithm for Sparse System Identification . . . . . . . . 538Cemil Turan and Yedilkhan Amirgaliev
Extended Separating Plane Algorithm and NSO-Solutionsof PageRank Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547
Evgeniya Vorontsova
Short Communications
Location, Pricing and the Problem of Apollonius . . . . . . . . . . . . . . . . . . . . 563André Berger, Alexander Grigoriev, Artem Panin,and Andrej Winokurow
Variable Neighborhood Search Approach for the Locationand Design Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570
Tatyana Levanova and Alexander Gnusarev
On a Network Equilibrium Problem with Mixed Demand . . . . . . . . . . . . . . 578Olga Pinyagina
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585
XXVI Contents