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Title: Algorithm to solve a chance-constrained network capacity design problem with stochastic demands and finite support

Here, we consider the problem of determining the capacity to assign to each arc in a given network, subject to uncertainty in the supply and/or demand of each node. This design problem underlies many real-world applications, such as the design of power transmission and telecommunications networks. We first consider the case where a set of supply/demand scenarios are provided, and we must determine the minimum-cost set of arc capacities such that a feasible flow exists for each scenario. We briefly review existing theoretical approaches to solving this problem and explore implementation strategies to reduce run times. With this as a foundation, our primary focus is on a chance-constrained version of the problem in which α% of the scenarios must be feasible under the chosen capacity, where α is a user-defined parameter and the specific scenarios to be satisfied are not predetermined. We describe an algorithm which utilizes a separation routine for identifying violated cut-sets which can solve the problem to optimality, and we present computational results. We also present a novel greedy algorithm, our primary contribution, which can be used to solve for a high quality heuristic solution. We present computational analysis to evaluate the performance of our proposed approaches.
 [1] ;  [2] ;  [3] ;  [3]
  1. General Motors, Warren, MI (United States)
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  3. Univ. of Michigan, Ann Arbor, MI (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0894-069X; 647498
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Naval Research Logistics
Additional Journal Information:
Journal Volume: 63; Journal Issue: 3; Journal ID: ISSN 0894-069X
Office of Naval Research - Wiley
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States