Network flow interdiction on planar graphs

The network flow interdiction problem asks to reduce the value of a maximum flow in a given network as much as possible by removing arcs and vertices of the network constrained to a fixed budget. Although the network flow interdiction problem is strongly NP-complete on general networks, pseudo-polynomial algorithms were found for planar networks with a single source and a single sink and without the possibility to remove vertices. In this work, we introduce pseudo-polynomial algorithms that overcome various restrictions of previous methods. In particular, we propose a planarity-preserving transformation that enables incorporation of vertex removals and vertex capacities in pseudo-polynomial interdiction algorithms for planar graphs. Additionally, a new approach is introduced that allows us to determine in pseudo-polynomial time the minimum interdiction budget needed to remove arcs and vertices of a given network such that the demands of the sink node cannot be completely satisfied anymore. The algorithm works on planar networks with multiple sources and sinks satisfying that the sum of the supplies at the sources equals the sum of the demands at the sinks. A simple extension of the proposed method allows us to broaden its applicability to solve network flow interdiction problems on planar networks with a single source and sink having no restrictions on the demand and supply. The proposed method can therefore solve a wider class of flow interdiction problems in pseudo-polynomial time than previous pseudo-polynomial algorithms and is the first pseudo-polynomial algorithm that can solve non-trivial planar flow interdiction problems with multiple sources and sinks. Furthermore, we show that the k-densest subgraph problem on planar graphs can be reduced to a network flow interdiction problem on a planar graph with multiple sources and sinks and polynomially bounded input numbers.     

Network simplex algorithm for the general equal flow problem

In this paper the general equal flow problem is considered. This is a minimum cost network flow problem with additional side constraints requiring the flow of arcs in some given sets of arcs to take on the same value. This model can be applied to approach water resource system management problems or multiperiod logistic problems in general involving policy restrictions which require some arcs to carry the same amount of flow through the given study period. Although the bases of the general equal flow problem are no longer spanning trees, it is possible to recognize a similar structure that allows us to take advantage of the practical computational capabilities of network models. After characterizing the bases of the problem as good (r+1)-forests, a simplex primal algorithm is developed that exploits the network structure of the problem and requires only slight modifications of the well-known network simplex algorithm.     

Solving the resource constrained deadline scheduling problem via reduction to the network flow problem

In this paper the problem of the equivalence of scheduling tasks on processors with the presence of deadlines and additional resources and the network flow problem is studied. This equivalence permits it to be proven that the problem of finding a maximal flow in a binary network with multipliers equal to 1 or 2 is NP-complete.     

Solving the optimal network problem

A heuristic method for solving the optimal network problem is proposed and shown to yield high quality results. Solution methods based on Branch-and-Bound techniques are also considered in some detail. The effects of making various approximations, when calculating lower bounds, is discussed and the concept of forced moves introduced.The various methods are applied to a series of problems which include networks with link construction cost not proportional to length and with trip demands tij not all equal.     

A network simplex based algorithm for the minimum cost proportional flow problem with disconnected subnetworks

In this study, we present a variant of the minimum cost network flow problem where the associated graph contains several disconnected subgraphs and it is required that the flows on arcs belonging to same arc subsets to be proportional. This type of network is mostly observed in large supply chains of assemble-to-order products. It is shown that any feasible solution of a reformulation of this problem has a special characteristic. By taking into account this fact, a network simplex based primal simplex algorithm is developed and its details are provided.     

Solving the minimum toll revenue problem in real transportation networks

As a means to relieve traffic congestion, toll pricing has recently received significant attention by transportation planners. Inappropriate use of transportation networks is one of the major causes of network congestion. Toll pricing is a method of traffic management in which traffic flow is guided to proper time and path in order to reduce the total delay in the network. This article investigates a method for solving the minimum toll revenue problem in real and large-scale transportation networks. The objective of this problem is to find link tolls that simultaneously cause users to efficiently use the transportation network and to minimize the total toll revenues to be collected. Although this model is linear, excessive number of variables and constraints make it very difficult to solve for large-scale networks. In this paper, a path-generation algorithm is proposed for solving the model. Implementation of this algorithm for different networks indicates that this method can achieve the optimal solution after a few iterations and a proper CPU time.     

An optimal capacity assignment for the robust design problem in capacitated flow networks

This paper proposes an exact algorithm to solve the robust design problem in a capacitated flow network in which each edge has several possible capacities. A capacitated flow network is popular in our daily life. For example, the computer network, the power transmission network, or even the supply chain network are capacitated flow networks. In practice, such network may suffer failure, partial failure or maintenance. Therefore, each edge in the network should be assigned sufficient capacity to keep the network functioning normally. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure. However, how to optimally assign the capacity to each edge is not an easy task. Although this kind of problem was known of NP-hard, this paper proposes an efficient exact algorithm to search for the optimal solutions for such a network and illustrates the efficiency of the proposed algorithm by numerical examples.     

Network flow assignment as a fixed point problem

This paper deals with the user equilibrium problem (flow assignment with equal journey time by alternative routes) and system optimum (flow assignment with minimal average journey time) in a network consisting of parallel routes with a single origin-destination pair. The travel time is simulated by arbitrary smooth nondecreasing functions. We prove that the equilibrium and optimal assignment problems for such a network can be reduced to the fixed point problem expressed explicitly. A simple iterative method of finding equilibriumand optimal flow assignment is developed. The method is proved to converge geometrically; under some fairly natural conditions the method is proved to converge quadratically.     

Maximum flow problem on dynamic generative network flows with time-varying bounds

This paper considers a new class of network flows, called dynamic generative network flows in which, the flow commodity is dynamically generated at a source node and dynamically consumed at a sink node and the arc-flow bounds are time dependent. Then the maximum dynamic flow problem in such networks for a pre-specified time horizon T is defined and mathematically formulated in both arc flow and path flow presentations. By exploiting the special structure of the problem, an efficient algorithm is developed to solve the general form of the dynamic problem as a minimum cost static flow problem.     

Computational experience with a difficult mixedinteger multicommodity flow problem

The following problem arises in the study of lightwave networks. Given a demand matrix containing amounts to be routed between corresponding nodes, we wish to design a network with certain topological features, and in this network, route all the demands, so that the maximum load (total flow) on any edge is minimized. As we show, even small instances of this combined design/routing problem are extremely intractable. We describe computational experience with a cutting plane algorithm for this problem.This research was partially supported by a Presidential Young Investigator Award and the Center for Telecommunications Research, Columbia University.Corresponding author.     

Designing WDM Optical Networks Using Branch-and-Price

In this paper, we present an exact solution procedure for the design of two-layer wavelength division multiplexing (WDM) optical networks with wavelength changers and bifurcated flows. This design problem closely resembles the traditional multicommodity flow problem, except that in the case of WDM optical networks, we are concerned with the routing of multiple commodities in two network layers. Consequently, the corresponding optimization models have to deal with two types of multicommodity variables defined for each of the network layers. The proposed procedure represents one of the first branch-and-price algorithms for a general WDM optical network setting with no assumptions on the number of logical links that can be established between nodes in the network. We apply our procedure in a computational study with four different network configurations. Our results show that for the three tested network configurations our branch-and-price algorithm provides solutions that are on average less than 5 % from optimality. We also provide a comparison of our branch-and-price algorithm with two simple variants of the upper bounding heuristic procedure HLDA that is commonly used for WDM optical network design.     

Multicommodity network flows: The impact of formulation on decomposition

This paper investigates the impact of problem formulation on Dantzig—Wolfe decomposition for the multicommodity network flow problem. These problems are formulated in three ways: origin-destination specific, destination specific, and product specific. The path-based origin-destination specific formulation is equivalent to the tree-based destination specific formulation by a simple transformation. Supersupply and superdemand nodes are appended to the tree-based product specific formulation to create an equivalent path-based product specific formulation. We show that solving the path-based problem formulations by decomposition results in substantially fewer master problem iterations and lower CPU times than by using decomposition on the equivalent tree-based formulations. Computational results on a series of multicommodity network flow problems are presented.This paper is dedicated to Phil Wolfe on the occasion of his 65th birthday.     

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.     

Flow control in gas networks: Exact controllability to a given demand

We consider a network of pipelines where the flow is controlled by a number of compressors. The consumer demand is described by desired boundary traces of the system state. We present conditions that guarantee the existence of compressor controls such that after a certain finite time the state at the consumer nodes is equal to the prescribed data. We consider this problem in the framework of continuously differentiable states. We give an explicit construction of the control functions for the control of compressor stations in gas distribution networks. Copyright © 2010 John Wiley & Sons, Ltd.     

Virtual Path Design in Service-Specific ATM Networks

This paper addresses the problem of virtual path management in ATM networks, which is the problem of jointly selecting efficient virtual trunk routes and sizing them to meet end-to-end grade-of-service requirements. The problem is posed over capacitated networks and is formulated as a two-level multi-commodity network flow problem with linear side-constraints (physical layer capacity) and non-linear side constraints (end-to-end/link blocking). Through a variety of examples we show the method (i) generates solutions that agree with engineering judgement, (ii) can solve VP layout management for realistic size networks (of up to 200 nodes) in reasonable time and (iii) provides upper bounds on how far the solution strays from the mathematically optimal design.     

An introduction to dynamic generative networks: Minimum cost flow

What we are dealing with is a class of networks called dynamic generative network flows in which the flow commodity is dynamically generated at source nodes and dynamically consumed at sink nodes. As a basic assumption, the source nodes produce the flow according to time generative functions and the sink nodes absorb the flow according to time consumption functions. This paper tries to introduce these networks and formulate minimum cost dynamic flow problem for a pre-specified time horizon T. Finally, some simple, efficient approaches are developed to solve the dynamic problem, in the general form when the capacities and costs are time varying and some other special cases, as a minimum cost static flow problem.     

Locating Active Sensors on Traffic Networks

Sensors are used to monitor traffic in networks. For example, in transportation networks, they may be used to measure traffic volumes on given arcs and paths of the network. This paper refers to an active sensor when it reads identifications of vehicles, including their routes in the network, that the vehicles actively provide when they use the network. On the other hand, the conventional inductance loop detectors are passive sensors that mostly count vehicles at points in a network to obtain traffic volumes (e.g., vehicles per hour) on a lane or road of the network.This paper introduces a new set of network location problems that determine where to locate active sensors in order to monitor or manage particular classes of identified traffic streams. In particular, it focuses on the development of two generic locational decision models for active sensors, which seek to answer these questions: (1) “How many and where should such sensors be located to obtain sufficient information on flow volumes on specified paths?”, and (2) “Given that the traffic management planners have already located count detectors on some network arcs, how many and where should active sensors be located to get the maximum information on flow volumes on specified paths?”The problem is formulated and analyzed for three different scenarios depending on whether there are already count detectors on arcs and if so, whether all the arcs or a fraction of them have them. Location of an active sensor results in a set of linear equations in path flow variables, whose solution provide the path flows. The general problem, which is related to the set-covering problem, is shown to be NP-Hard, but special cases are devised, where an arc may carry only two routes, that are shown to be polynomially solvable. New graph theoretic models and theorems are obtained for the latter cases, including the introduction of the generalized edge-covering by nodes problem on the path intersection graph for these special cases. An exact algorithm for the special cases and an approximate one for the general case are presented.     

An ant colony optimization metaheuristic for single-path multicommodity network flow problems

This paper studies the single-path multicommodity network flow problem (SMNF), inwhich the flow of each commodity can only use one path linking its origin anddestination in the network. We study two versions of this problem based on twodifferent objectives. The first version is to minimize network congestion, anissue of concern in traffic grooming over wavelength division multiplexing(WDM), and in which there generally exists a commodity flow between every pairof nodes. The second problem is a constrained version of the general linearmulticommodity flow problem, in which, for each commodity, a single path isallowed to send the required flow, and the objective is to determine a flowpattern that obeys the arc capacities and minimizes the total shipping cost.Based on the node-arc and the arc-chain representations, we first present twoformulations. Owing to computational impracticality of exact algorithms forpractical networks, we propose an ant colony optimization-(ACO) basedmetaheuristic to deal with SMNF. Considering different problem properties, wedevise two versions of ACO metaheuristics to solve these two problems,respectively. The proposed algorithms’ efficiencies are experimentallyinvestigated on some generated instances of SMNF. The test results demonstratethat the proposed ACO metaheuristics are computationally efficient and robustapproaches for solving SMNF.     

Cutting plane approach for the maximum flow interdiction problem

The maximum flow interdiction is a class of leader–follower optimization problems that seek to identify the set of edges in a network whose interruption minimizes the maximum flow across the network. Particularly, maximum flow interdiction is important in assessing the vulnerability of networks to disruptions. In this paper, the problem is formulated as a bi-level mixed-integer program and an iterative cutting plane algorithm is proposed as a solution methodology. The cutting planes are implemented in a branch-and-cut approach that is computationally effective. Extensive computational results are presented on 336 different instances with varying parameters and with networks of sizes up to 50 nodes, 1200 edge, and 800 commodities. The computational results show that the proposed cutting plane approach has significant computational advantage over the direct solution of the monolithic formulation of the maximum flow interdiction problem for the majority of the tested instances.     

Analytical solution of a spatially variable coefficient advection–diffusion equation in up to three dimensions

Analytical solutions are provided for the two- and three-dimensional advection–diffusion equation with spatially variable velocity and diffusion coefficients. We assume that the velocity component is proportional to the distance and that the diffusion coefficient is proportional to the square of the corresponding velocity component. There is a simple transformation which reduces the spatially variable equation to a constant coefficient problem for which there are available a large number of known analytical solutions for general initial and boundary conditions. These solutions are also solutions to the spatially variable advection–diffusion equation. The special form of the spatial coefficients has practical relevance and for divergent free flow represent corner or straining flow. Unlike many other analytical solutions, we use the transformation to obtain solutions of the spatially variable coefficient advection–diffusion equation in two and three dimensions. The analytical solutions, which are simple to evaluate, can be used to validate numerical models for solving the advection–diffusion equation with spatially variable coefficients. For numerical schemes which cannot handle flow stagnation points, we provide analytical solution to the spatially variable coefficient advection–diffusion equation for two-dimensional corner flow which contains an impermeable flow boundary. The impermeable flow boundary coincides with a streamline along which the fluid velocity is finite but the concentration vanishes. This example is useful for validating numerical schemes designed to predict transport around a curved boundary.