Algorithms for the single-source uncapacitated minimum concave-cost network flow problem

We investigate algorithms, applications, and complexity issues for the single-source uncapacitated (SSU) version of the minimum concave-cost network flow problem (MCNFP). We present applications arising from production planning, and prove complexity results for both global and local search. We formally state the local search algorithm of Gallo and Sodini [5], and present alternative local search algorithms. Computational results are provided to compare the various local search algorithms proposed and the effects of initial solution techniques.     

An algorithm to solve the proportional network flow problem

The proportional network flow problem is a generalization of the equal flow problem on a generalized network in which the flow on arcs in given sets must all be proportional. This problem appears in several natural contexts, including processing networks and manufacturing networks. This paper describes a transformation on the underlying network that reduces the problem to the equal flow problem; this transformation is used to show that algorithms that solve the equal flow problem can be directly applied to the proportional network flow problem as well, with no increase in asymptotic running time. Additionally, computational results are presented for the proportional network flow problem demonstrating equivalent performance to the same algorithm for the equal flow problem.     

Global search algorithms for minimum concave-cost network flow problems

We present algorithms for the single-source uncapacitated version of the minimum concave cost network flow problem. Each algorithm exploits the fact that an extreme feasible solution corresponds to a sub-tree of the original network. A global search heuristic based on random extreme feasible initial solutions and local search is developed. The algorithm is used to evaluate the complexity of the randomly generated test problems. An exact global search algorithm is developed, based on enumerative search of rooted subtrees. This exact technique is extended to bound the search based on cost properties and linear underestimation. The technique is accelerated by exploiting the network structure.     

Survivable capacitated network design problem: new formulation and Lagrangean relaxation

This work is focused on the analysis of the survivable capacitated network design problem. This problem can be stated as follows: Given a supply network with point-to-point traffic demands, specific survivability requirements, a set of available capacity ranges and their corresponding discrete costs for each arc, find minimum cost capacity expansions such that these demands can be met even if a network component fails. Solving this problem consists of selecting the links and their capacity, as well as the routings for each demand in every failure situation. This type of problem can be shown to be NP-hard. A new linear mixed-integer mathematical programming formulation is presented. An effective solution procedure based on Lagrangean relaxation is developed. Comparison heuristics and improvement heuristics are also described. Computational results using these procedures on different sizes of randomly generated networks are reported.     

New multi-objective method to solve reentrant hybrid flow shop scheduling problem

This paper focuses on the multi-objective resolution of a reentrant hybrid flow shop scheduling problem (RHFS). In our case the two objectives are: the maximization of the utilization rate of the bottleneck and the minimization of the maximum completion time. This problem is solved with a new multi-objective genetic algorithm called L-NSGA which uses the Lorenz dominance relationship. The results of L-NSGA are compared with NSGA2, SPEA2 and an exact method. A stochastic model of the system is proposed and used with a discrete event simulation module. A test protocol is applied to compare the four methods on various configurations of the problem. The comparison is established using two standard multi-objective metrics. The Lorenz dominance relationship provides a stronger selection than the Pareto dominance and gives better results than the latter. The computational tests show that L-NSGA provides better solutions than NSGA2 and SPEA2; moreover, its solutions are closer to the optimal front. The efficiency of our method is verified in an industrial field-experiment.     

Minimum concave-cost network flow problems: Applications,complexity, and algorithms

We discuss a wide range of results for minimum concave-cost network flow problems, including related applications, complexity issues, and solution techniques. Applications from production and inventory planning, and transportation and communication network design are discussed. New complexity results are proved which show that this problem is NP-hard for cases with cost functions other than fixed charge. An overview of solution techniques for this problem is presented, with some new results given regarding the implementation of a particular branch-and-bound approach.     

A mathematical approach to solve the network reconstruction problem

The reconstruction of biochemical and genetic networks from experimental data is an important challenge in biology and medical basic research. We formalize this problem mathematically and present an exact algorithm for its solution. Our procedure yields either a complete list of all alternative network structures that explain the observed phenomena or proves that no solution exists using the given data set. This work was supported by the German Ministry of Education and Research (BMBF) through the FORSYS initiative.     

A neural network approach to solve the stable matching problem

In this paper two types of neurons, the maximum selection neuron and the maximum cut-off neuron are introduced. They are used to construct a neural network to represent and solve the stable matching problem. The neural network approach allows the matching to be processed dynamically in a distributed parallel processing environment.     

How to solve a semi-infinite optimization problem

After an introduction to main ideas of semi-infinite optimization, this article surveys recent developments in theory and numerical methods for standard and generalized semi-infinite optimization problems. Particular attention is paid to connections with mathematical programs with complementarity constraints, lower level Wolfe duality, semi-smooth approaches, as well as branch and bound techniques in adaptive convexification procedures. A section on recent genericity results includes a discussion of the symmetry effect in generalized semi-infinite optimization.     

How to solve a design centering problem

This work considers the problem of design centering. Geometrically, this can be thought of as inscribing one shape in another. Theoretical approaches and reformulations from the literature are reviewed; many of these are inspired by the literature on generalized semi-infinite programming, a generalization of design centering. However, the motivation for this work relates more to engineering applications of robust design. Consequently, the focus is on specific forms of design spaces (inscribed shapes) and the case when the constraints of the problem may be implicitly defined, such as by the solution of a system of differential equations. This causes issues for many existing approaches, and so this work proposes two restriction-based approaches for solving robust design problems that are applicable to engineering problems. Another feasible-point method from the literature is investigated as well. The details of the numerical implementations of all these methods are discussed. The discussion of these implementations in the particular setting of robust design in engineering problems is new.     

A Lagrangian relaxation approach to solve the second phase of the exam scheduling problem

The problem of scheduling final exams at a large university can be viewed as a three phase process. The first phase consists of grouping the exams into sets called exam blocks. The second phase deals with the assignment of exam blocks to exam days and the third phase consists of arranging the exam days and also arranging the blocks within days.In this paper, we present new integer programming formulations for the second phase of the scheduling problem. We present an integer program with a single objective of minimizing the number of students with two or more exams per day. We then present a Lagrangian relaxation based solution procedure to solve this problem. Further, we present a bicriterion integer programming formulation to minimize the number of students with two exams per day and the number of students with three exams per day. Finally, we present some computational experience using randomly generated problems as well as real world data obtained from the State University of New York at Buffalo.     

A MIP-based approach to solve the prize-collecting local access network design problem

This paper presents a new combinatorial optimization problem that can be used to model the deployment of broadband telecommunications systems in which optical fiber cables are installed between a central office and a number of end-customers. In this capacitated network design problem the installation of optical fiber cables with sufficient capacity is required to carry the traffic from the central office to the end-customers at minimum cost. In the situation motivating this research the network does not necessarily need to connect all customers (or at least not with the best available technology). Instead, some nodes are potential customers. The aim is to select the customers to be connected to the central server and to choose the cable capacities to establish these connections. The telecom company takes the strategic decision of fixing a percentage of customers that should be served, and aims for minimizing the total cost of the network providing this minimum service. For that reason the underlying problem is called the Prize-Collecting Local Access Network Design problem (PC-LAN).     

A graphical method to solve a maximin allocation problem

This note extends a simple graphical method of a solving resource allocation problem, the objective function being the sum of returns developed by Vidal to the case where the objective function is of a maximin type.     

A multi-commodity flow formulation for the generalized pooling problem

The pooling problem is an extension of the minimum cost network flow problem where the composition of the flow depends on the sources from which it originates. At each source, the composition is known. In all other nodes, the proportion of any component is given as a weighted average of its proportions in entering flow streams. The weights in this average are simply the arc flow. At the terminals of the network, there are bounds on the relative content of the various components. Such problems have strong relevance in e.g. planning models for oil refining, and in gas transportation models with quality constraints at the reception side. Although the pooling problem has bilinear constraints, much progress in solving a class of instances to global optimality has recently been made. Most of the approaches are however restricted to networks where all directed paths have length at most three, which means that there is no connection between pools. In this work, we generalize one of the most successful formulations of the pooling problem, and propose a multi-commodity flow formulation that makes no assumptions on the network topology. We prove that our formulation has stronger linear relaxation than previously suggested formulations, and demonstrate experimentally that it enables faster computation of the global optimum.     

Network flow formulation of optimal perimeter sensory coverage problem

In this article, the perimeter detection optimization problem in field surveillance and target tracking are discussed. The detection range of sensors is assumed to be circular or elliptical. Sensors are also assumed to be associated with a cost factor reflecting their operational characteristics and power usage. We show that the problem of optimal sensor selection can be reduced to a network flow problem and can then be solved using any existing classical methodology. This significantly reduces the computational time of sensory selection problem which in many cases needs to be solved in almost real time basis, every time that the dynamics of the field changes. The field dynamics could change due to such events as wind direction change and sensor failures.     

Methods to solve multi-skill project scheduling problem

This is a summary of the author’s PhD thesis supervised by P. Martineau and E. Néron and defended on 28 November 2006 at the Université François-Rabelais de Tours. The thesis is written in French, and is available upon request from the author. This work deals with the problem of scheduling a project. The activities of this project requires skills that may not be mastered by all persons involved. First of all, the problem is defined in the introduction part. Then we propose different methods to solve it: lower bounds in part 2, different heuristics and meta-heuristics in part 3, and finally a branch-and-bound procedure in the last part.     

Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation

Annals of Operations Research - This work proposes a binary nonlinear bi-objective optimization model for the problem of planning the sustainable cultivation of crops. The solution to the problem...     

Monte Carlo method via a numerical algorithm to solve a parabolic problem

This paper is intended to provide a numerical algorithm consisted of the combined use of the finite difference method and Monte Carlo method to solve a one-dimensional parabolic partial differential equation. The numerical algorithm is based on the discretize governing equations by finite difference method. Due to the application of the finite difference method, a large sparse system of linear algebraic equations is obtained. An approach of Monte Carlo method is employed to solve the linear system. Numerical tests are performed in order to show the efficiency and accuracy of the present work.     

Integral dispersion equation method to solve a nonlinear boundary eigenvalue problem

In this work a nonlinear eigenvalue problem for a nonlinear autonomous ordinary differential equation of the second order is considered. This problem describes the process of propagation of transverse-electric electromagnetic waves along a plane dielectric waveguide with nonlinear permittivity. We demonstrate, as far as we know, a new method that allows one to derive an equation w.r.t. spectral parameter (the dispersion equation) which contains all necessary information about the eigenvalues. The method is based on a simple idea that the distance between zeros of a periodic solution to the differential equation is the same for the adjacent zeros. This method has no connections with the perturbation theory or the notion of a bifurcation point. Theorem of equivalence between the eigenvalue problem and the dispersion equation is proved. Periodicity of the eigenfunctions is proved, a formula for the period is found, and zeros of the eigenfunctions are determined. The formula for the distance between adjacent zeros of any eigenfunction is given. Also theorems of existence and localization of the eigenvalues are proved.