Generalized Minimum Cost Flow Research Articles

Inverse Generalized Minimum Cost Flow Problem Under the Hamming Distances

Given a generalized minimum cost flow problem, the corresponding inverse problem is to find a minimal adjustment of the cost function so that the given generalized flow becomes optimal to the problem. In this paper, we consider both types of the weighted Hamming distances for measuring the adjustment. In the sum-type case, it is shown that the inverse problem is APX-hard. In the bottleneck-type case, we present a polynomial time algorithm.

Minimum-Cost Flows in Unit-Capacity Networks

We consider combinatorial algorithms for the minimum-cost flow problem on networks with unit capacities, and special cases of the problem. Historically, researchers have developed special-purpose algorithms that exploit unit capacities. In contrast, for the maximum flow problem, the classical blocking flow and push-relabel algorithms for the general case also have the best bounds known for the special case of unit capacities. In this paper we show that the classical blocking flow push-relabel cost-scaling algorithms of Goldberg and Tarjan (Math. Oper. Res. 15, 430---466, 1990) for general minimum-cost flow problems achieve the best known bounds for unit-capacity problems as well. We also develop a cycle-canceling algorithm that extends Goldberg's shortest path algorithm (Goldberg SIAM J. Comput. 24, 494---504, 1995) to minimum-cost, unit-capacity flow problems. Finally, we combine our ideas to obtain an algorithm that solves the minimum-cost bipartite matching problem in O(r1/2mlogC)$O(r^{1/2} m \log C)$ time, where m is the number of edges, C is the largest arc cost (assumed to be greater than 1), and r is the number of vertices on the small side of the vertex bipartition. This result generalizes (and simplifies) a result of Duan et al. (2011) and solves an open problem of Ramshaw and Tarjan (2012).

An exact solution approach based on column generation and a partial-objective constraint to design a cellulosic biofuel supply chain

This study provides an exact solution method to solve a mixed-integer linear programming model that prescribes an optimal design of a cellulosic biofuel supply chain. An embedded structure can be transformed to a generalized minimum cost flow problem, which is used as a sub-problem in a column generation approach, to solve the linear relaxation of the mixed-integer program. This study proposes a dynamic programming algorithm to solve the sub-problem in O(m) time, generating improving path-flows. It proposes an inequality, called the partial objective constraint, which is based on the portion of the objective function associated with binary variables, to underlie a branch-and-cut approach. Computational tests show that the proposed solution approach solves most instances faster than a state-of-the-art commercial solver (CPLEX).

A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

An efficient metaheuristic for integrated scheduling and staffing IT projects based on a generalized minimum cost flow network

AbstractScheduling IT projects and assigning the project work to human resources are an important and common tasks in almost any IT service company. It is particularly complex because human resources usually have multiple skills. Up to now only little work has considered IT‐specific properties of the project structure and human resources. In this article, we present an optimization model that simultaneously schedules the activities of multiple IT projects with serial network structures and assigns the project work to multiskilled internal and external human resources with different efficiencies. The goal is to minimize costs. We introduce a metaheuristic that decomposes the problem into a binary scheduling problem and a continuous staffing problem where the latter is solved efficiently by exploiting its underlying network structure. For comparison, we solve the mixed–binary linear program with a state–of–the–art commercial solver. The impacts of problem parameters on computation time and solution gaps between the metaheuristic and the solver are assessed in an experimental study. Our results show that the metaheuristic provides very favorable results in considerable less time than the solver for midsize problems. For larger problems, it shows a similar performance while the solver fails to return feasible solutions. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 111–127, 2012

A POLYNOMIAL-TIME ALGORITHM FOR THE GENERALIZED INDEPENDENT-FLOW PROBLEM

We consider a compound problem of the generalized minimum-cost flow problem and the independent-flow problem, which we call the generalized independent-flow problem. The generalized minimum- cost flow problem is to find a minimum-cost flow in a capacitated network with gains, where each arc flow is multiplied by a gain factor when going through an arc. On the other hand, the independent-flow problem due to Fujishige is to find a minimum-cost flow in a multiple-source multiple-sink capacitated network with submodular constraints on the set of supply vectors on the source vertex set and on the set of demand vectors on the sink vertex set. We present a polynomial-time algorithm for the generalized independent- flow problem, based on Wayne's algorithm for generalized minimum-cost flows and Fujishige's algorithm for independent flows, which can be regarded as an extension of Wallacher and Zimmermann's submodular flow algorithm.

Combinatorial interior point methods for generalized network flow problems

We present combinatorial interior point methods for the generalized minimum cost flow and the generalized circulation problems based on Wallacher and Zimmermann's combinatorial interior point method for the minimum cost network flow problem. The algorithms have features of both a combinatorial algorithm and an interior point method. They work towards optimality by iteratively reducing the value of a potential function while maintaining interior point solutions. At each iteration, flow is augmented along a generalized circulation, which is computed by solving a TVPI (Two Variables Per Inequality) system. The algorithms run in time, where m and n are, respectively, the number of arcs and nodes in the graph, and L is the length of the input data.

A Polynomial Combinatorial Algorithm for Generalized Minimum Cost Flow

We propose the first combinatorial solution to the generalized minimum cost flow problem (flow with losses and gains). Despite a rich history dating back to Kantorovich and Dantzig, until now, the only known way to solve the problem in polynomial-time was via general-purpose linear programming techniques. Polynomial combinatorial algorithms were previously known only for the version of our problem without costs. We design the first such algorithms for the version with costs. Our algorithms also find provably good solutions faster than optimal ones, providing the first strongly polynomial approximation schemes for the problem. Our techniques extend to optimize linear programs with two variables per inequality. Polynomial combinatorial algorithms were previously developed for testing the feasibility of such linear programs. We propose the first such methods for the optimization version.

Flows on hypergraphs

We consider the capacitated minimum cost flow problem on directed hypergraphs. We define spanning hypertrees so generalizing the spanning tree of a standard graph, and show that, like in the standard and in the generalized minimum cost flow problems, a correspondence exists between bases and spanning hypertrees. Then, we show that, like for the network simplex algorithms for the standard and for the generalized minimum cost flow problems, most of the computations performed at each pivot operation have direct hypergraph interpretations.

A generic auction algorithm for the minimum cost network flow problem

In this paper we broadly generalize the assignment auction algorithm to solve linear minimum cost network flow problems. We introduce a generic algorithm, which contains as special cases a number of known algorithms, including the e-relaxation method, and the auction algorithm for assignment and for transportation problems. The generic algorithm can serve as a broadly useful framework for the development and the complexity analysis of specialized auction algorithms that exploit the structure of particular network problems. Using this framework, we develop and analyze two new algorithms, an algorithm for general minimum cost flow problems, called network auction, and an algorithm for thek node-disjoint shortest path problem.

Monge and feasibility sequences in general flow problems

In a feasible transportation problem, there is always an ordering of the arcs such that greedily sending maximal flow on each arc in turn, according to that order, yields a feasible solution. We characterize those transportation graphs for which there exists a single order which is good for all feasible problems with the same graph. The characterizations are shown to be intimately related to Monge sequences and to totally balanced matrices. We describe efficient algorithms which, for a given graph, construct such order whenever it exists. For a transportation problem with corresponding m× n bipartite graph with e arcs, we show how to generate such an order in O(min( e log e, mn)) steps. Using that order, the feasibility question for any given supply and demand vectors can be determined in O( m+ n) time. We also extend the characterization and algorithms to general minimum cost flow problems in which the underlying graph is nonbipartite, and the sources and destinations are not predetermined. We generalize the theory of Monge sequences too to such problems.

最小費用流問題に対する多項式時間の双対単体法

Recently, Ikura and Nemhauser proposed a polynomial-time algorithm for the transportation problem of the Hitchcock type by the dual simplex method. The Ikura-Nemhauser algorithm can also solve the general minimum cost flow problem by reducing the minimum cost flow problem to a Hitchcock problem. In this paper, instead of such a reduction, we show a polynomial-time dual simplex algorithm for finding a minimum cost flow in a two-terminal capacitated network by applying Ikura and Nemhauser's idea directly to general two-terminal networks.

Optimal Energy Exchange in a Decentralized Power Pool

Due to the natural diversity of incremental generating costs among interconnected power sys-tems, electric utility companies have been “pooling” together to improve economics of operation by exchanainq energy. In decentralized power pools, each utility still retains the control over its aenerating facilities. They report to a computerized “broker” the hourly buy and sell bids for block energy based on their local economic dispatch computations. The broker then matches the buyers and sellers to determine the flow of energy exchange.The main drawback of the “greedy” brokerage method is presented in this paper. The mathematical model for the decentralized power pool is presented as a generalized minimum cost flow problem with convex node production costs. A new combinatorial optimization algorithm, Maximum Path Return method, (MPR) is shown suitable as a substitute for the brokerage method. At each iteration, the method finds the maximum return (cost reduction) of a unit flow augmentation. This return is the buying price of the receiving node less the sum of the selling price of the sending node and the transmission cost of the cheapest path connecting the two nodes.This method takes the network topology into consideration. It combines the marginal analysis algorithm for discrete convex production allocation problems and the path algorithm embodied in many LP network flow methods. A special scaling technique for the MPR method is presented. The sealino MPR method solves the problem in 0(n4) polynomial time, where n is the number of utilities in the network.

Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems

This paper presents new algorithms for the maximum flow problem, the Hitchcock transportation problem, and the general minimum-cost flow problem. Upper bounds on the numbers of steps in these algorithms are derived, and are shown to compale favorably with upper bounds on the numbers of steps required by earlier algorithms. First, the paper states the maximum flow problem, gives the Ford-Fulkerson labeling method for its solution, and points out that an improper choice of flow augmenting paths can lead to severe computational difficulties. Then rules of choice that avoid these difficulties are given. We show that, if each flow augmentation is made along an augmenting path having a minimum number of arcs, then a maximum flow in an n-node network will be obtained after no more than ~(n a - n) augmentations; and then we show that if each flow change is chosen to produce a maximum increase in the flow value then, provided the capacities are integral, a maximum flow will be determined within at most 1 + logM/(M--1) if(t, S) augmentations, wheref*(t, s) is the value of the maximum flow and M is the maximum number of arcs across a cut. Next a new algorithm is given for the minimum-cost flow problem, in which all shortest-path computations are performed on networks with all weights nonnegative. In particular, this algorithm solves the n X n assigmnent problem in O(n 3) steps. Following that we explore a scaling technique for solving a minimum-cost flow problem by treating a sequence of derived problems with scaled down capacities. It is shown that, using this technique, the solution of a Iiitchcock transportation problem with m sources and n sinks, m ~ n, and maximum flow B, requires at most (n + 2) log2 (B/n) flow augmentations. Similar results are also given for the general minimum-cost flow problem. An abstract stating the main results of the present paper was presented at the Calgary International Conference on Combinatorial Structures and Their Applications, June 1969. In a paper by l)inic (1970) a result closely related to the main result of Section 1.2 is obtained. Dinic shows that, in a network with n nodes and p arcs, a maximum flow can be computed in 0 (n2p) primitive operations by an algorithm which augments along shortest augmenting paths. KEY WOl¢l)S AND PHP~ASES: network flows, transportation problem, analysis of algorithms CR CATEGOI{.IES: 5.3, 5.4, 8.3