Date of Graduation
5-2018
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Engineering (PhD)
Degree Level
Graduate
Department
Industrial Engineering
Advisor/Mentor
Milburn, Ashlea B.
Committee Member
Zhang, Shengfan
Second Committee Member
Rainwater, Chase E.
Third Committee Member
Mason, Scott J.
Fourth Committee Member
Ramirez-Marquez, Jose
Keywords
Facility Location; Heuristics; Simulated Annealing
Abstract
This dissertation presented three logistics problems. The first problem is a parallel machine scheduling problems that considers multiple unique characteristics including release dates, due dates, limited machine availability and job splitting. The objective of is to minimize the total amount of time required to complete work. A mixed integer programming model is presented and a heuristic is developed for solving the problem. The second problem extends the first parallel scheduling problem to include two additional practical considerations. The first is a setup time that occurs when warehouse staff change from one type of task to another. The second is a fixed time window for employee breaks. A simulated annealing (SA) heuristic is developed for its solution. The last problem studied in this dissertation is a new facility location problem variant with application in disaster relief with both verified data and unverified user-generated data are available for consideration during decision making. A total of three decision strategies that can be used by an emergency manager faced with a POD location decision for which both verified and unverified data are available are proposed: Consider Only Verified, Consider All and Consider Minimax Regret. The strategies differ according to how the uncertain user-generated data is incorporated in the planning process. A computational study to compare the performance of the three decision strategies across a range of plausible disaster scenarios is presented.
Citation
Li, B. (2018). Quantitative Methods For Select Problems In Facility Location And Facility Logistics. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2771
Included in
Industrial Engineering Commons, Industrial Technology Commons, Operational Research Commons