Constraint Generation for Network Reliability Problems

This paper presents a constraint generation approach to the network reliability problem of adding spare capacity at minimum cost that allows the traffic on a failed link to be rerouted to its destination. Any number of non-simultaneous link failures can be part of the requirements on the spare capacity. The key result is a necessary and sufficient condition for a multicommodity flow to exist, which is derived in the appendix. Computational results on large numbers of random networks are presented.     

Separable Augmented Lagrangian Algorithm with Multidimensional Scaling for Monotropic Programming

We analyze a new decomposition approach for convex structured programs based on augmented Lagrangian functions with multiple scaling parameters. We obtain global convergence results with weak hypotheses. Numerical results are presented on a class of multicommodity flow problems; empirical choices of the scaling parameters updates are discussed. The authors gratefully acknowledge the help of J.-P. Crouzeix in simplifying the proof of the main convergence result.     

A Slope Scaling/Lagrangean Perturbation Heuristic with Long-Term Memory for Multicommodity Capacitated Fixed-Charge Network Design

This paper describes a slope scaling heuristic for solving the multicomodity capacitated fixed-charge network design problem. The heuristic integrates a Lagrangean perturbation scheme and intensification/diversification mechanisms based on a long-term memory. Although the impact of the Lagrangean perturbation mechanism on the performance of the method is minor, the intensification/diversification components of the algorithm are essential for the approach to achieve good performance. The computational results on a large set of randomly generated instances from the literature show that the proposed method is competitive with the best known heuristic approaches for the problem. Moreover, it generally provides better solutions on larger, more difficult, instances.     

Telecommunication Network Capacity Design for Uncertain Demand

The expansion of telecommunication services has increased the number of users sharing network resources. When a given service is highly demanded, some demands may be unmet due to the limited capacity of the network links. Moreover, for such demands, telecommunication operators should pay penalty costs. To avoid rejecting demands, we can install more capacities in the existing network. In this paper we report experiments on the network capacity design for uncertain demand in telecommunication networks with integer link capacities. We use Poisson demands with bandwidths given by normal or log-normal distribution functions. The expectation function is evaluated using a predetermined set of realizations of the random parameter. We model this problem as a two-stage mixed integer program, which is solved using a stochastic subgradient procedure, the Barahona's volume approach and the Benders decomposition.     

A Comparison of Heuristics for the Discrete Cost Multicommodity Network Optimization Problem

In this paper, approximate solutions algorithms for discrete cost multicommodity network optimization problems are presented and compared. Firstly, extensions of classical greedy heuristics, based on link-rerouting and flow-rerouting heuristics, are presented in details. Secondly, a new approximate solution algorithm, which basically consists of a heuristic implementation of the exact Benders-type cutting plane generation method, is proposed. All these algorithms are extensively compared on randomly generated graphs up to 50 nodes and 90 links. It clearly appears that this new Benders-type approach is very promising since it produces the best heuristic solutions.     

Evolving System Architectures for Multimedia Network Design

This paper discusses implementation issues concerning a telecommunications planning system for networks with hypothetical multimedia stochastic traffic. Stochasticity of the traffic and probabilistic statements about service commitment are captured by a chance-constrained stochastic programming formulation of the complex network dimensioning and traffic management problem treated in our previous work. Our approach involves a hierarchy of design objectives associated with the respective network layers. These are met by constituting a set of models – the Integrated Network Design System (INDS) – with common data, solvers and a graphical user interface (GUI) operating under the interactive control of the network planner.     

Network synthesis under survivability constraints

Telecommunication networks are subject to link and equipment failures. Since failures cannot be entirely avoided, networks have to be designed so as to survive failure situations. In this paper, we are interested in designing low cost survivable networks. Given point-to-point traffic demands and a cost/capacity function for each link, we aim at finding the minimum cost capacities satisfying the given demands and survivability requirements. A survivability model that reroutes interrupted traffic using all the available capacities on the network is presented and studied. In the proposed model, capacity and flow assignments for each network operating state are jointly optimized. We prove the -hardness of the optimisation problem defined by dual constraints. Then, we propose a polynomial relaxation along with a fast heuristic to compute a feasible solution of the problem from its relaxed optimal solution. Our solution approaches are tested on a set of problem instances.Received: September 2002, Revised: July 2003, AMS classification: 90C05     

A Comparison of Optimal Methods for Local Access Uncapacitated Network Design

We compare some optimal methods addressed to a problem of local access network design. We see this problem arising in telecommunication as a flow extension of the Steiner problem in directed graphs, thus including as particular cases some alternative approaches based on the spanning tree problem. We work with two equivalent flow formulations for the problem, the first referring to a single commodity and the second being a multicommodity flow model. The objective in both cases is the cost minimization of the sum of the fixed (structural) and variable (operational) costs of all the arcs composing an arborescence that links the origin node (switching center) to every demand node. The weak single commodity flow formulation is solved by a branch-and-bound strategy that applies Lagrangian relaxation for computing the bounds. The strong multicommodity flow model is solved by a branch-and-cut algorithm and by Benders decomposition. The use of a linear programming solver to address both the single commodity and the multicommodity models has also been investigated. Our experience suggests that a certain number of these modeling and solution strategies can be applied to the frequently occurring problems where basic optimal solutions to the linear program are automatically integral, so it also solves the combinatorial optimization problem right away. On the other hand, our main conclusion is that a well tailored Benders partitioning approach emerges as a robust method to cope with that fabricated cases where the linear programming relaxation exhibits a gap between the continuous and the integral optimal values.     

Nonsmooth optimization methods for parallel decomposition of multicommodity flow problems

We develop an iterative algorithm based on right-hand side decomposition for the solution of multicommodity network flow problems. At each step of the proposed iterative procedure the coupling constraints are eliminated by subdividing the shared capacity resource among the different commodities and a master problem is constructed which attempts to improve sharing of the resources at each iteration.As the objective function of the master problem is nonsmooth, we apply to it a new optimization technique which does not require the exact solutions of the single commodity flow subproblems. This technique is based on the notion of - subgradients instead of subgradients and is suitable for parallel implementation. Extensions to the nonlinear, convex separable case are also discussed.The work of this author has been supported by the Air Force Office of Scientific Research Grant AFOSR-89-0410.     

A Primal-Dual Algorithm for Monotropic Programming and its Application to Network Optimization

This paper presents a new primal-dual algorithm for solving a class of monotropic programming problems. This class involves many problems arising in a number of important applications in telecommunications networks, transportation and water distribution. The proposed algorithm is inspired by Kallio and Ruszczyski approach for linear programming [M. Kallio and A. Ruszczyski, WP-94-15, IIASA, 1994]. The problem is replaced by a game using two different augmented Lagrangian functions defined for the primal and the dual problems. It is then possible to develop a block-wise Gauss-Seidel method to reach an equilibrium of the game with alternating steps made in each component of the primal and dual variables. Finally, we show how this algorithm may be applied to some important problems in Network Optimization such as the minimum quadratic cost single flow problems and convex multicommodity flow problems.     

A Concavity Result for Network Design Problems

The Network Design Problem has been studied extensively and in many of these models the cost is assumed to be a concave function of the loads on the links. In this paper we investigate under which conditions this is indeed the case for the communication networks. The result is presented as a theorem, the Concavity Theorem, and a list of conditions that can easily be verified. It is also shown how the theorem can be extended to other applications, like in the area of road transportation.     

Capacity and flow assignment of data networks by generalized Benders decomposition

A mixed-integer non-linear model is proposed to optimize jointly the assignment of capacities and flows (the CFA problem) in a communication network. Discrete capacities are considered and the cost function combines the installation cost with a measure of the Quality of Service (QoS) of the resulting network for a given traffic. Generalized Benders decomposition induces convex subproblems which are multicommodity flow problems on different topologies with fixed capacities. These are solved by an efficient proximal decomposition method. Numerical tests on small to medium-size networks show the ability of the decomposition approach to obtain global optimal solutions of the CFA problem.     

Heuristics for Distribution Network Design in Telecommunication

A distribution network problem arises in a lower level of an hierarchical modeling approach for telecommunication network planning. This paper describes a model and proposes a lagrangian heuristic for designing a distribution network. Our model is a complex extension of a capacitated single commodity network design problem. We are given a network containing a set of sources with maximum available supply, a set of sinks with required demands, and a set of transshipment points. We need to install adequate capacities on the arcs to route the required flow to each sink, that may be an intermediate or a terminal node of an arborescence. Capacity can only be installed in discrete levels, i.e., cables are available only in certain standard capacities. Economies of scale induce the use of a unique higher capacity cable instead of an equivalent set of lower capacity cables to cover the flow requirements of any link. A path from a source to a terminal node requires a lower flow in the measure that we are closer to the terminal node, since many nodes in the path may be intermediate sinks. On the other hand, the reduction of cable capacity levels across any path is inhibited by splicing costs. The objective is to minimize the total cost of the network, given by the sum of the arc capacity (cables) costs plus the splicing costs along the nodes. In addition to the limited supply and the node demand requirements, the model incorporates constraints on the number of cables installed on each edge and the maximum number of splices at each node. The model is a NP-hard combinatorial optimization problem because it is an extension of the Steiner problem in graphs. Moreover, the discrete levels of cable capacity and the need to consider splicing costs increase the complexity of the problem. We include some computational results of the lagrangian heuristics that works well in the practice of computer aided distribution network design.     

Parking Capacity and Pricing in Park'n Ride Trips: A Continuous Equilibrium Network Design Problem

In this paper we consider the problem of designing parking facilities for park'n ride trips. We present a new continuous equilibrium network design problem to decide the capacity and fare of these parking lots at a tactical level. We assume that the parking facilities have already been located and other topological decisions have already been taken.The modeling approach proposed is mathematical programming with equilibrium constraints. In the outer optimization problem, a central Authority evaluates the performance of the transport network for each network design decision. In the inner problem a multimodal traffic assignment with combined modes, formulated as a variational inequality problem, generates the share demand for modes of transportation, and for parking facilities as a function of the design variables of the parking lots. The objective is to make optimal parking investment and pricing decisions in order to minimize the total travel cost in a subnetwork of the multimodal transportation system.We present a new development in model formulation based on the use of generalized parking link cost as a design variable.The bilevel model is solved by a simulated annealing algorithm applied to the continuous and non-negative design decision variables. Numerical tests are reported in order to illustrate the use of the model, and the ability of the approach to solve applications of moderate size.     

On cuts and matchings in planar graphs

We study the max cut problem in graphs not contractible toK ₅, and optimum perfect matchings in planar graphs. We prove that both problems can be formulated as polynomial size linear programs.Supported by the joint project Combinatorial Optimization of the Natural Sciences and Engineering Research Council of Canada and the German Research Association (Deutsche Forschungsgemeinschaft, SFB 303).     

一种新的交通网络设计优化算法

交通网络设计问题是研究如何用定量的方法在已有交通网络上添加或扩容某些路段的问题.文章在回顾交通网络设计问题文献的基础上,提出了基于图论网络优化思想的解决该类问题的一种新思路,给出了启发式算法,并进行了算法复杂性分析,最后通过算例验证了其有效性.     

Scatter Search for Network Design Problem

A fixed charge capacitated multicommodity network design problem on undirected networks is addressed. At the present time, there exists no algorithm that can solve large instances, common in several applications, in a reasonable period of time. This paper presents an efficient procedure using a scatter search framework. Computational experiments on a large set of randomly generated problems show that this procedure is capable of finding good solutions to large-scale problems within a reasonable amount of time.     

Epsilon-proximal decomposition method

We propose a modification of the proximal decomposition method investigated by Spingarn [30] and Mahey et al. [19] for minimizing a convex function on a subspace. For the method to be favorable from a computational point of view, particular importance is the introduction of approximations in the proximal step. First, we couple decomposition on the graph of the epsilon-subdifferential mapping and cutting plane approximations to get an algorithmic pattern that falls in the general framework of Rockafellar inexact proximal-point algorithms [26]. Recently, Solodov and Svaiter [27] proposed a new proximal point-like algorithm that uses improved error criteria and an enlargement of the maximal monotone operator defining the problem. We combine their idea with bundle mecanism to devise an inexact proximal decomposition method with error condition which is not hard to satisfy in practice. Then, we present some applications favorable to our development. First, we give a new regularized version of Benders decomposition method in convex programming called the proximal convex Benders decomposition algorithm. Second, we derive a new algorithm for nonlinear multicommodity flow problems among which the message routing problem in telecommunications data networks.