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acceptance of new workings which may extend the working day and interfere with leisure activities.
Huisman and Wagelmans 2006 have proposed a dynamic integrated bus and crew scheduling system that will reschedule the crew and bus simultaneously whenever an
UE occurs. However, in our opinion, and in realistic situations, it is not practical to reschedule the whole crew schedule whenever a crew becomes unavailable. This is
because the complexity associated with rescheduling can be understood from the constraints i.e. driving hour rules of the crew itself. When trying to conduct any
rescheduling activities, schedulers need to consider cost and time factors, such as the number of available members, driving hours left for each one, and their location of
every crew. With such added constraints it becomes very difficult for the system to find an optimum schedule.
There are a few assumptions in the research by Huisman and Wagelmans 2006 that are not feasible in a real world. First, the passengers have a higher priority than crews.
Thus, there is a possibility of violation of the crew rules whenever a bus late occurs before the break time. The crew has to shorten the break or not take a break just to make
sure the bus operate on time. Although this is appropriate to guarantee that the bus services run smoothly, the EC driving rules should not be broken. Furthermore, this is
not acceptable to crews. Second, a trip can only start late due to a delay of the vehicle and thus not due to the crew. This assumption is not real due to the fact that crews are
one of the causes of UE. Third, availability of unlimited crew members, however, this is hardly realistic.
1.2.2 Limitations of Manual Way of Crew Rescheduling
When many UE happen at the same time, especially different types of events, making decisions will be difficult and slow for supervisors. Consider, for example, a time that a
bus is involved in an accident, two crews are late, four buses are stuck in a traffic jam, and a crew is unavailable because of the emergency. In this situation, if a supervisor
wants to reschedule crews it is quite difficult and decisions are made without the help of an automated system.
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In the real world situation, when UE takes place, supervisors have to make quick decisions within a short time. The pressure might cause them to make mistakes in crew
rescheduling. The decisions could possibly break the EC driving hour rules. For example, because of an accident a crew is not available to continue hisher duty. A
supervisor may request other crew to replace the unavailable crew without realising that the crew has to drive more than maximum hours allowed in a day.
The decisions that supervisors make when UE occurs may not be optimum. Optimum in this context means minimising the use of crews or spare crews. Without the help of an
automated system, it is hard to make decisions that achieve an optimum solution because the time is limited. For an example, a crew member is not available for two
hours of hisher duty. Instead of using a spare crew as a replacement, an optimum way is by using an available crew who has finished hisher duty but not yet signed off to
replace the unavailable crew provided they do not exceed the maximum driving hours. We argue that the limitations mentioned above hard and slow to make decisions, prone
to error and not optimum make it difficult for supervisors to manage UE. The limitations above were elicited from interviews with three bus companies in London
which will be presented in detail in Chapter Two.
1.2.3 What May Help
The overview of the current approaches so far has shown that while these approaches may be adequate for deterministic environments, they do not provide solutions that
could help supervisors in reschedules crews in real world situations. Now the question becomes: what fast and accurate appropriate approach can be used to automate the crew
rescheduling process that can help supervisors in dealing with UE that disrupt crew schedules?
Mathematical approaches are able to search optimum or near optimum schedules Wren, 2004, but they also have some limitations, for example, they are usually slow to
produce results in real-time because they are computationally intensive when it comes to complex situations Kwan et al., 1999. Conventional programs allocate resources to
demands following pre-programmed algorithms in a sequential manner and therefore,
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when dealing with a large number of resources and demands, they require a long time to find optimum allocation Rzevski, 2002. Whenever resources or demands change,
these programs start the allocation process from the beginning and if changes are frequent, they oscillate and cannot reach the optimum solution.
The main characteristic for a tool that we are looking for is the ability to find quick solutions in real-time whenever UE take place and in an uncertain environment. The
capabilities of a MAS, especially when dealing with changes in real-time and in uncertain environment, matched our requirements Weiss, 1999; Shen et al., 2001;
Wooldridge, 2002. To the best of our knowledge, the application of a MAS to bus crew scheduling problem is a novel idea. Thus, in this research we propose using a MAS as a
tool to automate the crew rescheduling process. Details of a MAS are discussed in Chapter Two.
1.3 Research Aim
