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A Tactical Decision Algorithm for the Optimal Dispatching of Oil Spill Cleanup Equipment

Author

Listed:
  • Harilaos N. Psaraftis

    (Department of Ocean Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Babis O. Ziogas

    (IMI, Inc., Sew York, New York 10017)

Abstract

We develop an optimization procedure for assisting decision-makers in the allocation of resources for cleaning up a specific oil spill. The objective function is to minimize a weighted combination of spill-specific response and damage costs. Inputs to this problem include information about the outflow of oil, availability and performance of spill cleanup equipment, as well as costs of equipment transported and on-scene operation. A general (albeit separable) damage function is assumed. The algorithm is deterministic and is based on a dynamic program within which a series of 0-1 knapsack problems are solved repeatedly. Although this algorithm is approximate, its worst-case performance is quantified and we argue that under realistic inputs the procedure can be expected to produce solutions very close to optimality. Under prescribed conditions we prove that the algorithm produces optimal solutions. A realistic example based on the Argo Merchant oil spill is presented to provide insight into the structure of this problem. Finally, we discuss possible uses of this model within the existing and alternative operational and policy environments.

Suggested Citation

  • Harilaos N. Psaraftis & Babis O. Ziogas, 1985. "A Tactical Decision Algorithm for the Optimal Dispatching of Oil Spill Cleanup Equipment," Management Science, INFORMS, vol. 31(12), pages 1475-1491, December.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:12:p:1475-1491
    DOI: 10.1287/mnsc.31.12.1475
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    Citations

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    Cited by:

    1. Cameron MacKenzie & Hiba Baroud & Kash Barker, 2016. "Static and dynamic resource allocation models for recovery of interdependent systems: application to the Deepwater Horizon oil spill," Annals of Operations Research, Springer, vol. 236(1), pages 103-129, January.
    2. Sengul Orgut, Irem & Ivy, Julie S. & Uzsoy, Reha & Hale, Charlie, 2018. "Robust optimization approaches for the equitable and effective distribution of donated food," European Journal of Operational Research, Elsevier, vol. 269(2), pages 516-531.
    3. Iakovou, Eleftherios & Ip, Chi M. & Douligeris, Christos & Korde, Ashutosh, 1997. "Optimal location and capacity of emergency cleanup equipment for oil spill response," European Journal of Operational Research, Elsevier, vol. 96(1), pages 72-80, January.
    4. Caunhye, Aakil M. & Nie, Xiaofeng & Pokharel, Shaligram, 2012. "Optimization models in emergency logistics: A literature review," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 4-13.
    5. Wenshuai Wu & Yi Peng, 2016. "Extension of grey relational analysis for facilitating group consensus to oil spill emergency management," Annals of Operations Research, Springer, vol. 238(1), pages 615-635, March.
    6. Wenshuai Wu & Yi Peng, 2016. "Extension of grey relational analysis for facilitating group consensus to oil spill emergency management," Annals of Operations Research, Springer, vol. 238(1), pages 615-635, March.
    7. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.
    8. Cameron A. MacKenzie & Hiba Baroud & Kash Barker, 2016. "Static and dynamic resource allocation models for recovery of interdependent systems: application to the Deepwater Horizon oil spill," Annals of Operations Research, Springer, vol. 236(1), pages 103-129, January.
    9. Srinivasa, Anand V. & Wilhelm, Wilbert E., 1997. "A procedure for optimizing tactical response in oil spill clean up operations," European Journal of Operational Research, Elsevier, vol. 102(3), pages 554-574, November.

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