scholarly journals Efficient Algorithms on Multicommodity Flow over Time Problems with Partial Lane Reversals

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Urmila Pyakurel ◽  
Shiva Prakash Gupta ◽  
Durga Prasad Khanal ◽  
Tanka Nath Dhamala
Keyword(s):  
Polynomial Time ◽  
Flow Problem ◽  
Maximum Flow ◽  
Multicommodity Flow ◽  
Time Step ◽  
Approximation Solution ◽  
Flow Problems ◽  
Time Problem ◽  
Flow Over Time ◽  
Over Time

The multicommodity flow problem arises when several different commodities are transshipped from specific supply nodes to the corresponding demand nodes through the arcs of an underlying capacity network. The maximum flow over time problem concerns to maximize the sum of commodity flows in a given time horizon. It becomes the earliest arrival flow problem if it maximizes the flow at each time step. The earliest arrival transshipment problem is the one that satisfies specified supplies and demands. These flow over time problems are computationally hard. By reverting the orientation of lanes towards the demand nodes, the outbound lane capacities can be increased. We introduce a partial lane reversal approach in the class of multicommodity flow problems. Moreover, a polynomial-time algorithm for the maximum static flow problem and pseudopolynomial algorithms for the earliest arrival transshipment and maximum dynamic flow problems are presented. Also, an approximation solution to the latter problem is obtained in polynomial-time.

scholarly journals FLOW OVER TIME PROBLEM WITH INFLOW-DEPENDENT TRANSIT TIMES

2018 ◽  
Vol 23 (1) ◽  
pp. 49-56
Author(s):  
Durga Prasad Khanal ◽  
Urmila Pyakurel ◽  
Tanka Nath Dhamala
Keyword(s):  
Network Flow ◽  
Time Horizon ◽  
The Road ◽  
Transit Times ◽  
Time Problem ◽  
Fast Flow ◽  
Static Flow ◽  
Time Expanded Network ◽  
Flow Over Time ◽  
Over Time

 Network flow over time is an important area for the researcher relating to the traffic assignment problem. Transmission times of the vehicles directly depend on the number of vehicles entering the road. Flow over time with fixed transit times can be solved by using classical (static) flow algorithms in a corresponding time expanded network which is not exactly applicable for flow over time with inflow dependent transit times. In this paper we discuss the time expanded graph for inflow-dependent transit times and non-existence of earliest arrival flow on it. Flow over time with inflow-dependent transit times are turned to inflow-preserving flow by pushing the flow from slower arc to the fast flow carrying arc. We gave an example to show that time horizon of quickest flow in bow graph GB was strictly smaller than time horizon of any inflow-preserving flow over time in GB satisfying the same demand. The relaxation in the modified bow graph turns the problem into the linear programming problem.

scholarly journals Maximum Multicommodity Flow with Intermediate Storage

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Durga Prasad Khanal ◽  
Urmila Pyakurel ◽  
Tanka Nath Dhamala
Keyword(s):  
Continuous Time ◽  
Flow Problem ◽  
Natural Transformation ◽  
Capacity Constraints ◽  
Multicommodity Flow ◽  
Flow Problems ◽  
Transit Times ◽  
Multicommodity Flow Problem ◽  
Intermediate Storage

The multicommodity flow problem deals with the transshipment of more than one commodity from respective sources to corresponding sinks without violating the capacity constraints. Due to the capacity constraints, flows out from the sources may not reach their sinks, and so, the storage of excess flows at intermediate nodes plays an important role in the maximization of flow values. In this paper, we introduce the maximum static as well as maximum dynamic multicommodity flow problems with intermediate storage. We present polynomial and pseudopolynomial time algorithms for the former and latter problems, respectively. We also present the solution procedures to these problems in contraflow network having symmetric as well as asymmetric arc transit times. We transform the solutions in continuous-time settings by using natural transformation.

scholarly journals The inverse maximum flow problem under Lk norms

2012 ◽  
Vol 28 (1) ◽  
pp. 59-66
Author(s):  
ADRIAN DEACONU ◽  
◽  
ELEONOR CIUREA ◽  
Keyword(s):  
Polynomial Time ◽  
Flow Problem ◽  
Maximum Flow ◽  
Upper Bounds ◽  
Initial Vector ◽  
Lower And Upper Bounds ◽  
Maximum Flow Problem ◽  
The Given

The problem consists in modifying the lower and upper bounds of a given feasible flow f in a network G so that the given flow becomes a maximum flow in G and the distance between the initial vector of bounds and the modified one measured using Lk norm (k ∈ N∗) is minimum. A fast apriori fesibility test is presented. An algorithm for solving this problem is deduced. Strongly and weakly polynomial time implementations of this algorithm are presented. Some particular cases of the problem are discussed.

scholarly journals Inverse Generalized Maximum Flow Problems

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 899 ◽  
Author(s):  
Javad Tayyebi ◽  
Adrian Deaconu
Keyword(s):  
Flow Problem ◽  
Natural Extension ◽  
Maximum Flow ◽  
Fast Method ◽  
Np Hard ◽  
Flow Problems ◽  
Maximum Flow Problem ◽  
Hard Problems ◽  
Optimum Solution ◽  
Np Hard Problems

A natural extension of maximum flow problems is called the generalized maximum flow problem taking into account the gain and loss factors for arcs. This paper investigates an inverse problem corresponding to this problem. It is to increase arc capacities as less cost as possible in a way that a prescribed flow becomes a maximum flow with respect to the modified capacities. The problem is referred to as the generalized maximum flow problem (IGMF). At first, we present a fast method that determines whether the problem is feasible or not. Then, we develop an algorithm to solve the problem under the max-type distances in O ( m n · log n ) time. Furthermore, we prove that the problem is strongly NP-hard under sum-type distances and propose a heuristic algorithm to find a near-optimum solution to these NP-hard problems. The computational experiments show the accuracy and the efficiency of the algorithm.

scholarly journals The Maximum Flows in Planar Dynamic Networks

2016 ◽  
Vol 11 (2) ◽  
pp. 282 ◽  
Author(s):  
Camelia Schiopu ◽  
Eleonor Ciurea
Keyword(s):  
Flow Model ◽  
Flow Problem ◽  
Dynamic Networks ◽  
Maximum Flow ◽  
Stationary Case ◽  
Dynamic Flow ◽  
Dynamic Flow Model ◽  
Maximum Flows ◽  
Static Flow ◽  
Over Time

An nontrivial extension of the maximal static flow problem is the maximal dynamic flow model, where the transit time to traverse an arc is taken into consideration. If the network parameters as capacities, arc traversal times, and so on, are constant over time, then a dynamic flow problem is said to be stationary. Research on flow in planar static network is motivated by the fact that more efficient algorithms can be developed by exploiting the planar structure of the graph. This article states and solves the maximum flow in directed (1, n) planar dynamic networks in the stationary case.

scholarly journals Finding Most Vital Links over Time in a Flow Network

2012 ◽  
Vol 2 (2) ◽  
pp. 173-186 ◽  
Author(s):  
Shahram MOROWATI-SHALILVAND ◽  
Javad MEHRI-TEKMEH
Keyword(s):  
Programming Problem ◽  
Time Horizon ◽  
Benders Decomposition ◽  
Maximum Flow ◽  
Integer Programming Problem ◽  
Time Dynamic ◽  
Benders Decomposition Algorithm ◽  
Flows Over Time ◽  
Flow Over Time ◽  
Over Time

This paper deals with finding most vital links of a network which carries flows over time (also called ”dynamic flows”). Given a network and a time horizon T, Single Most Vital Link Over Time (SMVLOT) problem looks for a link whose removal results in greatest decrease in the value of maximum flow over time (dynamic maximum flow) up to time horizon T between two terminal nodes. SMVLOT problem is formulated as a mixed binary linear programming problem. This formulation is extended to a general case called k-Most Vital Links Over Time (KMVLOT) problem, in which we look for finding those k links whose removal makes greatest decrease in the value of maximum flow over time. A Benders decomposition algorithm is proposed for solving SMVLOT and KMVLOT problems. For the case of SMVLOT problem, the proposed algorithm is improved to a fully combinatorial algorithm by adopting an iterative method for solving existing integer programming problem. However, our experimental results showed the superiority of proposed methods.

BINDING ALGORITHM FOR POWER OPTIMIZATION BASED ON NETWORK FLOW METHOD

2002 ◽  
Vol 11 (03) ◽  
pp. 259-271 ◽  
Author(s):  
YOONSEO CHOI ◽  
TAEWHAN KIM
Keyword(s):  
Network Flow ◽  
Power Optimization ◽  
Flow Problem ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Data Flow Graph ◽  
Commodity Flow ◽  
Behavioral Synthesis ◽  
Flow Problems ◽  
Step Procedure

We propose an efficient binding algorithm for power optimization in behavioral synthesis. In prior work, it has been shown that several binding problems for low-power can be formulated as multi-commodity flow problems (due to an iterative execution of data flow graph) and be solved optimally. However, since the multi-commodity flow problem is NP-hard, the application is limited to a class of small sized problems. To overcome the limitation, we address the problem of how we can effectively make use of the property of efficient flow computations in a network so that it is extensively applicable to practical designs while producing close-to-optimal results. To this end, we propose a two-step procedure, which (1) determines a feasible binding solution by partially utilizing the computation steps for finding a maximum flow of minimum cost in a network and then (2) refines it iteratively. Experiments with a set of benchmark examples show that the proposed algorithm saves the run time significantly while maintaining close-to-optimal bindings in most practical designs.

Networks Flow Applications

2013 ◽  
pp. 246-256
Author(s):  
Alireza Boloori ◽  
Monirehalsadat Mahmoudi
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Flow Problem ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Shortest Path Problems ◽  
Flow Problems ◽  
Large Scale Integration ◽  
Minimum Cost Flow Problem ◽  
Scale Integration

In this chapter, some applications of network flow problems are addressed based on each type of problem being discussed. For example, in the case of shortest path problems, their concept in facility layout, facility location, robotics, transportation, and very large-scale integration areas are pointed out in the first section. Furthermore, the second section deals with the implementation of the maximum flow problem in image segmentation, transportation, web communities, and wireless networks and telecommunication areas. Moreover, in the third section, the minimum-cost flow problem is discussed in fleeting and routing problems, petroleum, and scheduling areas. Meanwhile, a brief explanation about each application as well as some corresponding literature and research papers are presented in each section. In addition, based on available literature in each of these areas, some research gaps are identified, and future trends as well as chapter’s conclusion are pointed out in the fourth section.

scholarly journals Lexicographically Maximum Flows under an Arc Interdiction

2021 ◽  
Vol 4 (2) ◽  
pp. 8-14
Author(s):  
Phanindra Prasad Bhandari ◽  
Shree Ram Khadka
Keyword(s):  
Network Flow ◽  
Flow Problem ◽  
Maximum Flow ◽  
Directed Network ◽  
Network System ◽  
Residual Flow ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Maximum Flow Problem ◽  
Maximum Flows

Network interdiction problem arises when an unwanted agent attacks the network system to deteriorate its transshipment efficiency. Literature is flourished with models and solution approaches for the problem. This paper considers a single commodity lexicographic maximum flow problem on a directed network with capacitated vertices to study two network flow problems under an arc interdiction. In the first, the objective is to find an arc on input network to be destroyed so that the residual lexicographically maximum flow is lexicographically minimum. The second problem aims to find a flow pattern resulting lexicographically maximum flow on the input network so that the total residual flow, if an arc is destroyed, is maximum. The paper proposes strongly polynomial time solution procedures for these problems.

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