  Recent research found that fairness plays a key role in customer
satisfaction. Therefore, many manufacturing and services industries have become
aware of the need to treat customers fairly. Still, there is a huge lack of
models that enable industries to make operational decisions fairly, such as a
fair scheduling of the customers' jobs. Our main aim in this research is to
provide a unified framework to enable schedulers making fair decisions in
repetitive scheduling environments. For doing so, we consider a set of
repetitive scheduling problems involving a set of $n$ clients. In each out of
$q$ consecutive operational periods (e.g. days), each one of the customers
submits a job for processing by an operational system. The scheduler's aim is
to provide a schedule for each of the $q$ periods such that the quality of
service (QoS) received by each of the clients will meet a certain predefined
threshold. The QoS of a client may take several different forms, e.g., the
number of days that the customer receives its job later than a given due-date,
the number of times the customer receive his preferred time slot for service,
or the sum of waiting times for service. We analyze the single machine variant
of the problem for several different definitions of QoS, and classify the
complexity of the corresponding problems using the theories of classical and
parameterized complexity. We also study the price of fairness, i.e., the loss
in the system's efficiency that results from the need to provide fair
solutions.
