This paper presents a load balancing method to improve network utilization when static routing algorithms are employed. Static routing algorithms can generally be reduced to a path assignment problem with the aim of minimizing a cost function: i.e., maximizing network revenues. Unfortunately the problem, when multiple constraints such as different QoS traffic classes are present, is computation...

This paper proposes a new Multiobjective Genetic Algorithm (MOGA) approach for Bicriteria Network Design (BND) Problem. The objectives are to maximize flow and minimize cost. The proposed method adopts priority-based encoding method to represent a path in the network. Different from other encoding methods, such as path oriented encoding method, priority-based encoding method can be applied for ...

Given a network modeled as a graph G with each link associated with a cost and k weights, the Constrained Shortest Path (CSP(k)) problem asks for computing a minimum cost path from a source node s to a target node t satisfying pre-specified bounds on path weights. This problem is NP-hard. In this paper we propose a new approximation algorithm called GEN-LARAC for CSP(k) problem based on Lagrang...

For oversized cargo transportation, traditional transportation schemes only consider road length, road width, the transportation cost as weight values in analysis and calculation of route selection. However, for oversized trucks, turning direction at road intersections is also a factor worth considering. By introducing the classical algorithm of Dijkstra into the model of road network, this res...

We consider the multi-robot task allocation (MRTA) problem in an initially unknown environment. The objective of the MRTA problem is to find a schedule or sequence of tasks that should be performed by a set of robots so that the cost or energy expended by the robots is minimized. Existing solutions for the MRTA problem mainly concentrate on finding an efficient task allocation among robots, wit...

Conventional wisdom holds that the efficient way to achieve a climate target is to price carbon emissions at a rate that increases exponentially. We show that an exponentially increasing tax on carbon emissions is not efficient when climate targets are expressed in terms of temperature. In that case, the least-cost policy path deviates from an exponential trajectory in order to take advantage o...

hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh The grammar problem, a generalization of the single-source shortest-path problem introduced by Knuth, is to compute the minimum-cost derivation of a terminal string from each non-terminal of a given context-free grammar, with the cost of a derivation being suitably defined. This problem also subsume...

In the path minimum query problem, we preprocess a tree on n weighted nodes, such that given an arbitrary path, we can locate the node with the smallest weight along this path. We design novel succinct indices for this problem; one of our index structures supports queries in O(α(m,n)) time, and occupiesO(m) bits of space in addition to the space required for the input tree, where m is an intege...

An M-convex function is a nonlinear discrete function defined on integer points introduced by Murota in 1996, and the M-convex submodular flow problem is one of the most general frameworks of efficiently solvable combinatorial optimization problems. It includes the minimum cost flow and the submodular flow problems as its special cases. In this paper, we first devise a successive shortest path ...

This paper considers a variation of the Elementary Constrained Shortest Path Problem in which nodes are separated into subsets and a feasible path through the network may visit at most one node from each subset. We refer to this problem as the Multiple Choice Elementary Constrained Shortest Path Problem (MC-ECSPP). The MC-ECSPP arises as a subproblem in branch-and-price approaches for variation...