Capturing incomplete information in resource allocation problems through numerical patterns

Arun Marar, Warren Buckler Powell

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


We look at the problem of optimizing complex operations with incomplete information where the missing information is revealed indirectly and imperfectly through historical decisions. Incomplete information is characterized by missing data elements governing operational behavior and unknown cost parameters. We assume some of this information may be indirectly captured in historical databases through flows characterizing resource movements. We can use these flows or other quantities derived from these flows as "numerical patterns" in our optimization model to reflect some of the incomplete information. We develop our methodology for representing information in resource allocation models using the concept of pattern regression. We use a popular goodness-of-fit measure known as the Cramer-Von Mises metric as the foundation of our approach. We then use a hybrid approach of solving a cost model with a term known as the "pattern metric" that minimizes the deviations of model decisions from observed quantities in a historical database. We present a novel iterative method to solve this problem. Results with real-world data from a large freight railroad are presented.

Original languageEnglish (US)
Pages (from-to)50-58
Number of pages9
JournalEuropean Journal of Operational Research
Issue number1
StatePublished - Aug 16 2009

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


  • Combinatorial optimization
  • Decision support systems
  • Knowledge-based systems
  • Large scale optimization
  • Logistics


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