New formulation and relaxation to solve a concave-cost network flow problem

In this paper we study a minimum cost, multicommodity network flow problem in which the total cost is piecewise linear, concave of the total flow along the arcs. Specifically, the problem can be defined as follows. Given a directed network, a set of pairs of communicating nodes and a set of available capacity ranges and their corresponding variable and fixed cost components for each arc, the problem is to select for each arc a range and identify a path for each commodity between its source and destination nodes so as to minimize the total costs. We also extend the problem to the case of piecewise nonlinear, concave cost function. New mathematical programming formulations of the problems are presented. Efficient solution procedures based on Lagrangean relaxations of the problems are developed. Extensive computational results across a variety of networks are reported. These results indicate that the solution procedures are effective for a wide range of traffic loads and different cost structures. They also show that this work represents an improvement over previous work made by other authors. This improvement is the result of the introduction of the new formulations of the problems and their relaxations.     

Concave cost minimization on networks

The paper deals with the problem of finding a minimum cost multicommodity flow on an uncapacitated network with concave link costs. Problems of this type are the optimal design of a network in the presence of scale economies and the telpack problem.Two different definitions of local optimality are given and compared both from the point of view of the computational complexity and from the point of view of the goodness of the solution they may provide.A vertex following algorithm to find a local optimum is proposed. The computational complexity of each iteration of the algorithm is O(n³), where n is the number of nodes of the network, and is independent of the differentiability of the objective function.Experimental results obtained from a set of test problems of size ranging from 11 nodes and 23 arcs to 48 nodes and 174 arcs, with number of commodities up to 5, are given.     

Generate upper boundary vectors meeting the demand and budget for a p-commodity network with unreliable nodes

The system capacity for a single-commodity flow network is the maximum flow from the source to the sink. This paper discusses the system capacity problem for a p-commodity limited-flow network with unreliable nodes. In such a network, arcs and nodes all have several possible capacities and may fail. Different types of commodity, which are transmitted through the same network simultaneously, competes the capacities of arcs and nodes. In particular, the consumed capacity by different types of commodity varies from arcs and nodes. We first define the system capacity as a vector and then a performance index, the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint, is proposed. Such a performance index can be easily computed in terms of upper boundary vectors meeting the demand and budget. A simple algorithm based on minimal cuts is thus presented to generate all upper boundary vectors. The manager can apply this performance index to measure the system capacity level for a supply-demand system.     

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.     

The flow circulation sharing problem

The flow circulation sharing problem is defined as a network flow circulation problem with a maximin objective function. The arcs in the network are partitioned into regular arcs and tradeoff arcs where each tradeoff arc has a non-decreasing tradeoff function associated with it. All arcs have lower and upper bounds on their flow while the value of the smallest tradeoff function is maximized. The model is useful in equitable resource allocation problems over time which is illustrated in a coal strike example and a submarine assignment example. Some properties including optimality conditions are developed. Each cut in the network defines a knapsack sharing problem which leads to an optimality condition similar to the max flow/min cut theorem. An efficient algorithm for both the continuous and integer versions of the flow circulation sharing problem is developed and computational experience given. In addition, efficient algorithms are developed for problems where some of the arcs have infinite flow upper bounds.     

Optimization of the flow through networks with gains

The optimal flow problem in networks with gains is presented through the simplex method. Out of simple theorical conditions, a method is built which needs only a relatively small number memory and quite a short calculation time by computer. Large examples are given; e.g., one test-example of 1000 nodes and 3000 arcs, and one real problem leading to a linear program of 3000 constraints and 8000 arcs.     

Multicommodity flow models for spanning trees with hop constraints

In this paper we compare the linear programming relaxations of undirected and directed multicommodity flow formulations for the terminal layout problem with hop constraints. Hop constraints limit the number of hops (links) between the computer center and any terminal in the network. These constraints model delay constraints since a smaller number of hops decreases the maximum delay transmission time in the network. They also model reliability constraints because with a smaller number of hops there is a lower route loss probability. Hop constraints are easily modelled with the variables involved in multicommodity flow formulations. We give some empirical evidence showing that the linear programming relaxation of such formulations give sharp lower bounds for this hop constrained network design problem. On the other hand, these formulations lead to very large linear programming models. Therefore, for bounding purposes we also derive several lagrangean based procedures from a directed multicommodity flow formulation and present some computational results taken from a set of instances with up to 40 nodes.     

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.     

A partitioning algorithm for the multicommodity network flow problem

A partitioning algorithm for solving the general minimum cost multicommodity flow problem for directed graphs is presented in the framework of a network flow method and the dual simplex method. A working basis which is considerably smaller than the number of capacitated arcs in the given network is employed and a set of simple secondary constraints is periodically examined. Some computational aspects and preliminary experimental results are discussed.     

A Lagrangian relax-and-cut approach for the sequential ordering problem with precedence relationships

The sequential ordering problem with precedence relationships was introduced in Escudero [7]. It has a broad range of applications, mainly in production planning for manufacturing systems. The problem consists of finding a minimum weight Hamiltonian path on a directed graph with weights on the arcs, subject to precedence relationships among nodes. Nodes represent jobs (to be processed on a single machine), arcs represent sequencing of the jobs, and the weights are sums of processing and setup times. We introduce a formulation for the constrained minimum weight Hamiltonian path problem. We also define Lagrangian relaxation for obtaining strong lower bounds on the makespan, and valid cuts for further tightening of the lower bounds. Computational experience is given for real-life cases already reported in the literature.     

A dual ascent approach for steiner tree problems on a directed graph

The Steiner tree problem on a directed graph (STDG) is to find a directed subtree that connects a root node to every node in a designated node setV. We give a dual ascent procedure for obtaining lower bounds to the optimal solution value. The ascent information is also used in a heuristic procedure for obtaining feasible solutions to the STDG. Computational results indicate that the two procedures are very effective in solving a class of STDG's containing up to 60 nodes and 240 directed/120 undirected arcs. The directed spanning tree and uncapacitated plant location problems are special cases of the STDG. Using these relationships, we show that our ascent procedure can be viewed as a generalization ofboth the Chu-Liu-Edmonds directed spanning tree algorithm and the Bilde-Krarup-Erlenkotter ascent algorithm for the plant location problem. The former comparison yields a dual ascent interpretation of the steps of the directed spanning tree algorithm.     

A Model-Based Approach and Analysis for Multi-Period Networks

The aim of this contribution is to address a general class of network problems, very common in process systems engineering, where spoilage on arcs and storage in nodes are inevitable as time changes. Having a set of capacities, so-called horizon capacity which limits the total flow passing arcs over all periods, the min-cost flow problem in the discrete-time model with time-varying network parameters is investigated. While assuming a possibility of storage or and spoilage, we propose some approaches employing polyhedrals to obtain optimal solutions for a pre-specified planning horizon. Our methods describe some reformulations based on polyhedrals that lead to LP problems comprising a set of sparse subproblems with exceptional structures. Considering the sparsity and repeating structure of the polyhedrals, algorithmic approaches based on decomposition techniques of block-angular and block-staircase cases are proposed to handle the global problem aiming to reduce the computational resources required.     

Algorithms for the quickest path problem and the reliable quickest path problem

The quickest path problem consists of finding a path in a directed network to transmit a given amount of items from an origin node to a destination node with minimal transmission time, when the transmission time depends on both the traversal times of the arcs, or lead time, and the rates of flow along arcs, or capacity. In telecommunications networks, arcs often also have an associated operational probability of the transmission being fault free. The reliability of a path is defined as the product of the operational probabilities of its arcs. The reliability as well as the transmission time are of interest. In this paper, algorithms are proposed to solve the quickest path problem as well as the problem of identifying the quickest path whose reliability is not lower than a given threshold. The algorithms rely on both the properties of a network which turns the computation of a quickest path into the computation of a shortest path and the fact that the reliability of a path can be evaluated through the reliability of the ordered sequence of its arcs. Other constraints on resources consumed, on the number of arcs of the path, etc. can also be managed with the same algorithms.     

LP extreme points and cuts for the fixed-charge network design problem

Many design decisions in transporation, communication, and manufacturing planning can be modeled as problems of routing multiple commodities between various origin and destination nodes of a directed network. Each arc of the network is uncapacitated and carries a fixed charge as well as a cost per unit of flow. We refer to the general problem of selecting a subset of arcs and routing the required multi-commodity flows along the chosen arcs at a minimum total cost as the fixed charge network design problem. This paper focuses on strenghthening the linear programming relaxation of a path-flow formulation for this problem. The considerable success achieved by researchers in solving many related design problems with algorithms that use strong linear programming-based lower bounds motivates this study. We first develop a convenient characterization of fractional extreme points for the network design linear programming relaxation. An auxiliary graph introduced for this characterization also serves to generate two families of cuts that exclude some fractional solutions without eliminating any feasible integer solutions. We discuss a separation procedure for one class of inequalities and demonstrate that many of our results generalize known properties of the plant location problem. Supported in part by grant number ECS-831-6224 of the National Science Foundation.     

Partition inequalities for capacitated survivable network design based on directed p-cycles

We study the design of capacitated survivable networks using directed p-cycles. A p-cycle is a cycle with at least three arcs, used for rerouting disrupted flow during edge failures. Survivability of the network is accomplished by reserving sufficient slack on directed p-cycles so that if an edge fails, its flow can be rerouted along the p-cycles.

We describe a model for designing capacitated survivable networks based on directed p-cycles. We motivate this model by comparing it with other means of ensuring survivability, and present a mixed-integer programming formulation for it. We derive valid inequalities for the model based on the minimum capacity requirement between partitions of the nodes and give facet conditions for them. We discuss the separation for these inequalities and present results of computational experiments for testing their effectiveness as cutting planes when incorporated in a branch-and-cut algorithm. Our experiments show that the proposed inequalities reduce the computational effort significantly.     

The quickest path problem with batch constraints

The quickest path problem has been proposed to cope with flow problems through networks whose arcs are characterized by both travel times and flowrate constraints. Basically, it consists in finding a path in a network to transmit a given amount of items from a source node to a sink in as little time as possible, when the transmission time depends on both the traversal times of the arcs and the rates of flow along arcs. This paper is focused on the solution procedure when the items transmission must be partitioned into batches with size limits. For this problem we determine how many batches must be made and what the sizes should be.     

Enumerating disjunctions and conjunctions of paths and cuts in reliability theory

Let G=(V,E) be a (directed) graph with vertex set V and edge (arc) set E. Given a set P of source-sink pairs of vertices of G, an important problem that arises in the computation of network reliability is the enumeration of minimal subsets of edges (arcs) that connect/disconnect all/at least one of the given source-sink pairs of P. For undirected graphs, we show that the enumeration problems for conjunctions of paths and disjunctions of cuts can be solved in incremental polynomial time. Furthermore, under the assumption that P consists of all pairs within a given vertex set, we also give incremental polynomial time algorithm for enumerating all minimal path disjunctions and cut conjunctions. For directed graphs, the enumeration problem for cut disjunction is known to be NP-complete. We extend this result to path conjunctions and path disjunctions, leaving open the complexity of the enumeration of cut conjunctions. Finally, we give a polynomial delay algorithm for enumerating all minimal sets of arcs connecting two given nodes s₁ and s₂ to, respectively, a given vertex t₁, and each vertex of a given subset of vertices T₂.     

Efficient contraflow algorithms for quickest evacuation planning

The optimization models and algorithms with their implementations on flow over time problems have been an emerging field of research because of largely increasing human-created and natural disasters worldwide. For an optimal use of transportation network to shift affected people and normalize the disastrous situation as quickly and Efficiently as possible, contraflow configuration is one of the highly applicable operations research (OR) models. It increases the outbound road capacities by reversing the direction of arcs towards the safe destinations that not only minimize the congestion and increase the flow but also decrease the evacuation time significantly. In this paper, we sketch the state of quickest flow solutions and solve the quickest contraflow problem with constant transit times on arcs proving that the problem can be solved in strongly polynomial time O(nm²(log n)²), where n and m are number of nodes and number of arcs, respectively in the network. This contraflow solution has the same computational time bound as that of the best min-cost flow solution. Moreover, we also introduce the contraflow approach with load dependent transit times on arcs and present an Efficient algorithm to solve the quickest contraflow problem approximately. Supporting the claim, our computational experiments on Kathmandu road network and on randomly generated instances perform very well matching the theoretical results. For sufficiently large number of evacuees, about double flow can be shifted with the same evacuation time and about half time is sufficient to push the given flow value with contraflow reconfiguration.     

Linear Approximations in a Dynamic Programming Approach for the Uncapacitated Single-Source Minimum Concave Cost Network Flow Problem in Acyclic Networks

We consider minimum concave cost flow problems in acyclic, uncapacitated networks with a single source. For these problems a dynamic programming scheme is developed. It is shown that the concave cost functions on the arcs can be approximated by linear functions. Thus the considered problem can be solved by a series of linear programs. This approximation method, whose convergence is shown, works particularly well, if the nodes of the network have small degrees. Computational results on several classes of networks are reported.