Convex Cost Multicommodity Flow Problems : Applications and Algorithms

We review and analyze nonlinear programming approaches to model-ing and solving certain flow problems in telecommunications, trans-portation and supply chain management. We emphasize the commonaspects of telecommunications and road networks, and indicate the im-portance of game theoretic and equilibrium approaches. Algorithmsbased on the Frank-Wolfe method are developed in depth and theirimplementations on sequential and parallel machines are discussed andevaluated for large-scale real-world networks. Several research direc-tions are also stated.SUMMER COURSE ON OPERATIONAL RESEARCH AND APPLICATIONS, May 15-18, 2013LABORATORY OF ALGORITHMS AND TECHNOLOGIES FOR NETWORK ANALYSIS (LATNA)HIGHER SCOOL OF ECONOMICS (HSE)NATIONAL RESEARCH UNIVERSITYNizhny Novgorod, Russian Federationhttp://nnov.hse.ru/en/latna/conferences/school2013Upprättat; 2013; 20150306 (athmig

Open problems in optimization and data analysis

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.

Discrete compettitive facility location : Modelling and optimization approaches

Competitive facility location problems are concerned with thefollowing situation: a firm wants to locate a predefined num-ber of facilities to serve customers locate in some region wherethere already exist (or will be) other firms offering the sameservice. Both new and existing firms compete for optimizingtheir market share of profit. A discrete version of such problemsarises when it is assumed that there is a ( rather small ) finitenumber of candidate locations and the markets consist of pointdemands. We review modelling and optimization approaches forthis type of problems and we emphasize and develop the bi-levelprogramming methodology.Godkänd; 2014; 20141124 (athmig

Discrete bi-level facility models with competing customers

The research work dealing with the bi-level formulation of location problems is limited only to the competition among the locators, that is it is supposed that either both the locator and the allocator are the same or the customer (i.e., the user as a whole) knows the optimality criterion of the locator and agrees assively with it. Customers' preferences as well as externalities (such as roadcongestion, facility congestion, emissions etc) caused by the location decisions are either ignored or controlled by incorporating constraints in order to ensure the achievement of a predetermined target. However, this approach treats customers as irresolute beings. Thus, if, for example, the customers travel to the facilities to obtain the offered service, then there is no compulsion or intensive for them to attend the designated facility. This means that, once the facilities are open, what the locator wishes the customers to do may not coincide with their own wish and behavior. We suppose that the customers are involved in a Nash game in order to ensure what they conceive as the best level of services for themselves. In order to take into consideration the effects of such competition in the facilities location decisions we propose a bi-level programming approach to the problem.Godkänd; 2015; 20150306 (athkar

Discrete bi-level facility models with competing customers

The research work dealing with the bi-level formulation of location problems is limited only to the competition among the locators, that is it is supposed that either both the locator and the allocator are the same or the customer (i.e., the user as a whole) knows the optimality criterion of the locator and agrees assively with it. Customers' preferences as well as externalities (such as roadcongestion, facility congestion, emissions etc) caused by the location decisions are either ignored or controlled by incorporating constraints in order to ensure the achievement of a predetermined target. However, this approach treats customers as irresolute beings. Thus, if, for example, the customers travel to the facilities to obtain the offered service, then there is no compulsion or intensive for them to attend the designated facility. This means that, once the facilities are open, what the locator wishes the customers to do may not coincide with their own wish and behavior. We suppose that the customers are involved in a Nash game in order to ensure what they conceive as the best level of services for themselves. In order to take into consideration the effects of such competition in the facilities location decisions we propose a bi-level programming approach to the problem.Godkänd; 2015; 20150306 (athkar

Concentrator Location and Terminal Layout in Local Access Networks : Integrated Models

Upprättat; 1994; 20150507 (andbra

Concentrator Location and Terminal Layout in Local Access Networks : Integrated Models

Upprättat; 1994; 20150507 (andbra

Multilevel optimization algorithms and applications

Researchers working with nonlinear programming often claim "the word is non­ linear" indicating that real applications require nonlinear modeling. The same is true for other areas such as multi-objective programming (there are always several goals in a real application), stochastic programming (all data is uncer­ tain and therefore stochastic models should be used), and so forth. In this spirit we claim: The word is multilevel. In many decision processes there is a hierarchy of decision makers, and decisions are made at different levels in this hierarchy. One way to handle such hierar­ chies is to focus on one level and include other levels' behaviors as assumptions. Multilevel programming is the research area that focuses on the whole hierar­ chy structure. In terms of modeling, the constraint domain associated with a multilevel programming problem is implicitly determined by a series of opti­ mization problems which must be solved in a predetermined sequence. If only two levels are considered, we have one leader (associated with the upper level) and one follower (associated with the lower level)

An Adaptive Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

A new hybridized algorithm based on Particle Swarm Optimization is proposed for the solution of the Vehicle Routing Problem with Time Windows. The algorithm uses a relative new topology, the Combinatorial Neighborhood Topology and, thus, a solution is not needed to be transformed in continuous values during the iterations, which makes Particle Swarm Optimization a competitive algorithm in solving routing problems. Also, in the proposed algorithm all the parameters (acceleration coefficients, iterations, local search iterations, upper and lower bounds of the velocities and of the positions and number of particles) are optimized during the procedure and, thus, the algorithm works independently and without any interference from the user. All parameters are randomly initialized and, afterwards, during the iterations the parameters are adapted based on a number of different conditions. The algorithm uses a number of different velocities’ equations and each particle selects randomly its velocity equation and during the iterations the particle has the possibility to change the velocity equation based on the produced quality of the solution. The algorithm is tested in known benchmark instances from the literature and gives very good results. It is also compared with other algorithms from the literature.Godkänd; 2014; 20141124 (athmig