University of Florida

Joseph C. Hartman

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With my work in the area of industrial engineering, I have had the opportunity to explore many research interests. My research focuses on problems in economic decision analysis. I am interested in developing models that aid in decision making for industry and the government. Specifically, I am interested in the following areas:

  • Engineering Economic Decision Analysis
    • Parallel Replacement Analysis: Determining equipment replacement schedules for groups of assets that are economically interdependent and operate in parallel.
    • Replacement Analysis of Evolving Technologies: Developing models to examine the serial and parallel replacement problem under continuous and breakthrough technological change.
    • Capital Budgeting: Developing models which allocate dollars to projects such that companies meet their investment goals.
  • Transportation Logistics
    • Fleet Replacement: An application of parallel replacement analysis, this work determines replacement schedules for fleets of assets.
    • Network Design: A broad research area generally concerned with fixed charge network flow problems and their applications.
    • Fleet Sizing and Mix: Fleet sizing and mix examines the tradeoff between operating expenses (smaller fleet with high utilization) versus capital expenses (larger fleet with lower utilization) and thus tradeoffs between operating and tactical decisions.
  • Manufacturing Logistics
    • Investment Justification: Investments in manufacturing systems are extremely expensive. Estimating relevant cash flows can be difficult as costs are hard to quantify and benefits may be intangible. Methods are investigated to aid in decision making in this difficult environment.
    • Investment/Utilization Tradeoffs: Similar to fleet sizing, the number of machines and their utilization rates are interdependent.
    • Capacity Planning: The problems specified above often fall into a larger context of capacity planning, at the machine, line, plant and enterprise level.

To tackle these problems, current methods being employed include: Dynamic Programming, as this methodology is excellent for making decisions at discrete points in time and allows for stochastic information. Approximation methods are extensively investigated; Integer Programming, with the use of cutting plane algorithsm; and Networks and Graphs as many decision problems can be represented as networks with nodes representing states and arcs representing decisions.

If you would like more information, feel free to drop me an e-mail.




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