Customer choice behavior, such as 'buy-up' and 'buy-down', is an important
phe-nomenon in a wide range of industries. Yet there are few models or
methodologies available to exploit this phenomenon within yield management
systems. We make some progress on filling this void. Specifically, we
develop a model of yield management in which the buyers' behavior is
modeled explicitly using a multi-nomial logit model of demand. The
control problem is to decide which subset of fare classes to offer at
each ...
Customer choice behavior, such as 'buy-up' and 'buy-down', is an important
phe-nomenon in a wide range of industries. Yet there are few models or
methodologies available to exploit this phenomenon within yield management
systems. We make some progress on filling this void. Specifically, we
develop a model of yield management in which the buyers' behavior is
modeled explicitly using a multi-nomial logit model of demand. The
control problem is to decide which subset of fare classes to offer at
each point in time. The set of open fare classes then affects the purchase
probabilities for each class. We formulate a dynamic program to
determine the optimal control policy and show that it reduces to a dynamic
nested allocation policy. Thus, the optimal choice-based policy can
easily be implemented in reservation systems that use nested allocation
controls. We also develop an estimation procedure for our model based on
the expectation-maximization (EM) method that jointly estimates arrival
rates and choice model parameters when no-purchase outcomes are
unobservable. Numerical results show that this combined optimization
-estimation approach may significantly improve revenue performance
relative to traditional leg-based models that do not account for choice
behavior.
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