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Gulf Coast Port Selection Using Multiple-Objective Decision Analysis

Author

Listed:
  • Rivelino R. De Icaza

    (Department of Industrial Engineering, College of Engineering, University of Arkansas, Fayetteville, Arkansas 72701)

  • Gregory S. Parnell

    (Department of Industrial Engineering, College of Engineering, University of Arkansas, Fayetteville, Arkansas 72701)

  • Edward A. Pohl

    (Department of Industrial Engineering, College of Engineering, University of Arkansas, Fayetteville, Arkansas 72701; Center for Innovation in Healthcare Logistics, University of Arkansas, Fayetteville, Arkansas 72701)

Abstract

In today’s competitive global markets, ports play a vital role in global supply chain operations. A port selection model was developed to demonstrate the potential to aid shipping line companies with decisions to select the best ports in the U.S. Gulf Coast. Because port selection is complex, is dynamic, and includes multiple objectives, a multiple-objective decision analysis quantified the value of the various ports using industry data and an industry expert’s knowledge. In addition, a corresponding cost model was developed using available cost data for each port. Monte Carlo simulation analyzed the uncertainties in the value and cost models. For the demonstration model scenario assumptions, the results show that the Houston port would be the best alternative for shipping lines in the Gulf Coast. The value model also identified opportunities for improvement for each port compared with the best West Coast port. The demonstration models show that port data exist to support the development of a multiple-objective decision analysis model and a cost model that can provide useful insights to port selection decision makers. Furthermore, these models can be easily tailored to support port selection in other regions by tailoring the objectives and measures to the decision-maker objectives.

Suggested Citation

  • Rivelino R. De Icaza & Gregory S. Parnell & Edward A. Pohl, 2019. "Gulf Coast Port Selection Using Multiple-Objective Decision Analysis," Decision Analysis, INFORMS, vol. 16(2), pages 87-104, June.
    • Handle: RePEc:inm:ordeca:v:16:y:2019:i:2:p:87-104
    DOI: 10.1287/deca.2018.0381
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    Cited by:

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    2. James E. Smith & James S. Dyer, 2021. "On (Measurable) Multiattribute Value Functions: An Expository Argument," Decision Analysis, INFORMS, vol. 18(4), pages 247-256, December.

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