| Summary: | This dissertation studies models for locating facilities in time varying demand
environments. We describe the characteristics of the time varying demand that motivate
the analysis of our location models in terms of total demand and the change
in value and location of the demand of each customer. The first part of the dissertation
is devoted to the dynamic location model, which determines the optimal
time and location for establishing capacitated facilities when demand and cost parameters
are time varying. This model minimizes the total cost over a discrete and
finite time horizon for establishing, operating, and closing facilities, including the
transportation costs for shipping demand from facilities to customers. The model
is solved using Lagrangian relaxation and Benders? decomposition. Computational
results from different time varying total demand structures demonstrate, empirically,
the performance of these solution methods.
The second part of the dissertation studies two location models where relocation
of facilities is not allowed and the objective is to determine the optimal location
of capacitated facilities that will have a good performance when demand and cost
parameters are time varying. The first model minimizes the total cost for opening
and operating facilities and the associated transportation costs when demand and
cost parameters are time varying. The model is solved using Benders? decomposition. We show that in the presence of high relocation costs of facilities (opening and closing
costs), this model can be solved as a special case by the dynamic location model. The
second model minimizes the maximum regret or opportunity loss between a robust
configuration of facilities and the optimal configuration for each time period. We
implement local search and simulated annealing metaheuristics to efficiently obtain
near optimal solutions for this model.
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