Combining Worst Case and Average Case Considerations in an Integrated Emergency Response Network Design Problem

The authors study an emergency response network design problem that integrates relief (supply) and evacuation (demand) sides under disaster location and intensity uncertainties which, in turn, dictate uncertainty in terms of the location and amount of demand. Representing these uncertainties by discrete scenarios, the authors present a stochastic programming framework in which two second stage objectives, the average and worst case costs, are combined. In the authors' model, they minimize, over all of the scenarios, the fixed costs of opening supply centers and shelters, and the weighted sum of average and worst case flow costs. Thus, the model gives the decision maker the flexibility to put relative emphasis on the worst case and average flow cost minimization and explore outcomes in terms of total costs and network configurations. To solve large scale instances with varying relative weights, the authors devise alternative Benders Decomposition approaches. The authors implement these by using an advanced callback feature of the solver while simultaneously incorporating several performance-enhancing steps that help to improve runtimes significantly. The authors conduct a detailed computational study to highlight the efficiency of their proposed solution methodology. Furthermore, they apply their approach in a realistic case study based on Geographical Information Systems data on coastal Texas and present interesting insights about the problem and the resulting network structures for varying weights assigned to objectives.


  • English

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  • Accession Number: 01663191
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 23 2018 4:23PM