Understanding how uncertainty effects the dynamics and behavior of
an organization is a critical aspect of system design. Models and
methods that take uncertainty into account can lead to significant
reductions in cost. This book investigates the use of stochastic
optimization models for a generalized assignment problem (GAP) with
uncertain resource capacity and a shift planning and scheduling
problem (SPSP) with unknown demand. For the GAP, the first stage
decisions correspond to an assignment of jobs to agents. Penalties
are incurred when the assignments do not permit all demand to be
satisfied. For the SPSP, the number of full-time and part-time
employees, as well as the number of full- time shifts by type, must
be specified before the demand is known. In the second stage,
feasibility is addressed by allocating overtime and calling in
temporary workers to handle spikes in the mail volume. This book
contains the development and analysis of stochastic integer models
for the GAP and the SPSP and the estimation of the demand
distributions from historical data. To solve the associated
stochastic integer problems, the column generation algorithms are
developed.
General
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