Abstract:
Scheduling in production systems concludes the proper coordination of activities in
order to increase productivity and reduce operational costs. In dynamic manufacturing
environments, scheduling solutions based on the classical objectives such as
makespan will not be sufficient. In fact, because of random disruptions that may occur
in the system, additional criteria that have capability to counter such disruptions
should be considered. To maintain system performance effective, rescheduling is
often used to counteract the effects of random disruptions.
In practical production environments, the scheduling process starts with determining
an initial schedule. Then, when a disruption arises, the initial schedule should be
revised in order to keep its feasibility and performance quality. The type of scheduling
that is actually carried out in shops is known as real schedule. As it is clear, real
schedule can be different from the initial schedule. This difference depends on the
level of failure and disruption and also the changes of the setting. There are two
policies to achieve a high level of system performance for the real schedule after
occurring of any disruption. These strategies are entitled reactive scheduling and
proactive scheduling.
The “reactive approach” does not consider the uncertainty when an initial schedule is
determined. However, when a random event occurs, it modifies the initial schedule
and performs the necessary reaction to obtain better result. This reaction can be in the
form of modification and improvement of the initial schedule or the formulation of a
totally-new schedule. On the other hand, the “proactive approach” considers the
stochastic and unexpected events to create the initial schedule. In this approach, in
addition to classical criteria such as makespan and tardiness, performance measures
such as robustness and stability is also considered to establish a schedule.
Optimization of stability is concerned with the deviation of the modified schedule
relative to the initial schedule. Optimization of robustness is concerned with the
different in terms of objective function (performance criteria) between initial and
modified schedules. An integrated proactive–reactive approach can also be considered
to generate better and practical results.
In this thesis, a two-step proactive–reactive method is presented foflexible job shop
scheduling to achieve a more stable and robust solution. In the first step, it is
attempted to generate an initially robust schedule by using robust optimization
approach. The initial robust schedule handles the uncertain processing times. In the
second step, when a random disruption occurs (which is the arrival of an unpredicted
new job), an appropriate reaction is adopted to determine the best modified schedule
