Logistics planning under uncertainty for disposition of radioactive wastes

  • Authors:

  • George F. List;Bryan Wood;Mark A. Turnquist;Linda K. Nozick;Dean A. Jones;Craig R. Lawton

  • Affiliations:

  • Rensselaer Polytechnic Institute, Department of Civil Engineering, 110 Eighth Street JEC 4049, Troy, NY 12180-3590, USA;Rensselaer Polytechnic Institute, Department of Civil Engineering, 110 Eighth Street JEC 4049, Troy, NY 12180-3590, USA;Cornell University, School of Civil & Environmental Engineering, Hollister Hall, Ithaca, NY 14853-3501, USA;Cornell University, School of Civil & Environmental Engineering, Hollister Hall, Ithaca, NY 14853-3501, USA;Sandia National Laboratories, New Mexico1, P.O. Box 5800, Albuquerque, NM 87185-1138, USA;Sandia National Laboratories, New Mexico1, P.O. Box 5800, Albuquerque, NM 87185-1138, USA

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2006

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Abstract

The US Department of Energy (DOE) faces an enormous environmental remediation challenge involving highly radioactive wastes at former weapons production facilities. The purpose of this analysis is to focus on equipment acquisition and fleet sizing issues related to transportation of wastes from remediation sites to disposal sites. Planning for the transportation of these wastes must be done with recognition of important uncertainties related to overall quantities of waste to be moved, the rate at which the wastes will be prepared for transport, and the certification of suitable transportation containers for use in the effort. However, deadlines for completion of the effort have already been set by the political process, without much regard for these uncertainties. To address this fleet sizing problem, we have created a robust optimization model that focuses on equipment investment decisions. Through this robust optimization, we illustrate how modeling can be used to explore the effects of uncertainty on the equipment acquisition strategy. The disposition of radioactive wastes from DOE sites is an important illustration of a category of problems where equipment investments must be made under conditions of considerable uncertainty. The methodology illustrated in this paper can be applied to this general class of problems.