On the probabilistic min spanning tree Problem

We study a probabilistic optimization model for min spanning tree, where any vertex vi of the input-graph G(V,E) has some presence probability pi in the final instance G′ ⊂ G that will effectively be optimized. Suppose that when this “real” instance G′ becomes known, a spanning tree T, called anticipatory or a priori spanning tree, has already been computed in G and one can run a quick algorithm (quicker than one that recomputes from scratch), called modification strategy, that modifies the anticipatory tree T in order to fit G ′. The goal is to compute an anticipatory spanning tree of G such that, its modification for any G ′ ⊆ G is optimal for G ′. This is what we call probabilistic min spanning tree problem. In this paper we study complexity and approximation of probabilistic min spanning tree in complete graphs under two distinct modification strategies leading to different complexity results for the problem. For the first of the strategies developed, we also study two natural subproblems of probabilistic min spanning tree, namely, the probabilistic metric min spanning tree and the probabilistic min spanning tree 1,2 that deal with metric complete graphs and complete graphs with edge-weights either 1, or 2, respectively

The probabilistic k-center problem

The k-Center problem on a graph is to nd a set K of k vertices minimizing the radius dened as the maximum distance between any vertex and K. We propose a probabilistic combinatorial optimization model for this problem, with uncertainty on vertices. This model is inspired by a wildre management problem. The graph represents the adjacency of zones of a landscape, where each vertex represents a zone. We consider a nite set of re scenarios with related probabilities. Given a k-center, its radius may change in some scenarios since some evacuation paths become impracticable. The objective is to nd a robust k-center that minimizes the expected value of the radius over all scenarios. We study this new problem with scenarios limited to a single burning vertex. First results deal with explicit solutions on paths and cycles, and hardness on planar graphs

Stochastic Trip Planning in High Dimensional Public Transit Network

This paper proposes a generalised framework for density estimation in large networks with measurable spatiotemporal variance in edge weights. We solve the stochastic shortest path problem for a large network by estimating the density of the edge weights in the network and analytically finding the distribution of a path. In this study, we employ Gaussian Processes to model the edge weights. This approach not only reduces the analytical complexity associated with computing the stochastic shortest path but also yields satisfactory performance. We also provide an online version of the model that yields a 30 times speedup in the algorithm's runtime while retaining equivalent performance. As an application of the model, we design a real-time trip planning system to find the stochastic shortest path between locations in the public transit network of Delhi. Our observations show that different paths have different likelihoods of being the shortest path at any given time in a public transit network. We demonstrate that choosing the stochastic shortest path over a deterministic shortest path leads to savings in travel time of up to 40\%. Thus, our model takes a significant step towards creating a reliable trip planner and increase the confidence of the general public in developing countries to take up public transit as a primary mode of transportation

Probabilistic optimization in graph-problems

We study a probabilistic optimization model for graph-problems under vertex-uncertainty. We assume that any vertex vi of the input-graph G(V,E) has only a probability pi to be present in the final graph to be optimized (i.e., the final instance for the problem tackled will be only a sub-graph of the initial graph). Under this model, the original "deterministic" problem gives rise to a new (deterministic) problem on the same input-graph G, having the same set of feasible solutions as the former one, but its objective function can be very different from the original one, the set of its optimal solutions too. Moreover, this objective function is a sum of 2|V| terms; hence, its computation is not immediately polynomial. We give sufficient conditions for large classes of graph-problems under which objective functions of the probabilistic counterparts are polynomially computable and optimal solutions are well-characterized. Finally, we apply these general results to natural and well-known combinatorial problems that belong to the classes considered

Robust Shortest Paths under Uncertainty Using Conditional Value-at-Risk

Finding a shortest path in a network is a classical problem in discrete optimization. The systems underlying the network models are subjects to a variety of sources of uncertainty. The purpose of this thesis is to model the shortest path problem with probabilistic arc failures with the aim of finding short but reliable paths through the network. This thesis proposes to use Conditional Value-at-Risk (CVaR) to find a path that is robust under probabilistic arc failures. CVaR, a quantitative risk measure, is roughly the mean excess loss associated with a decision. This thesis also develops three models of losses due to arc failures. Mixed integer linear programming models for the stochastic shortest path problem with probabilistic arc failures are formulated and implemented. The optimization models limit the CVaR of the loss due to arc failures as measured by the models of loss developed in the thesis.Industrial Engineering & Managemen

Task and contingency planning under uncertainty

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1995.Includes bibliographical references (leaves 204-213).by Volkan C. Kubali.Sc.D