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will make it more effective in achieving successful rescheduling. The work that was done on this research is still not enough fully to cover the literature gap. More research
is needed to cover the literature gap. This chapter concludes the research undertaken in this dissertation and recommends directions for further research. It begins with the
thesis summary, followed by the conclusions that were based on literature, interviews and experiments carried out. Limitations of this research are offered in the next section.
The final section provides recommendations for further research.
7.2 Summary This dissertation is made up of seven chapters. Chapter One served as an introduction
to the research problem, outlining the aim and objectives that guided the scope of this thesis. The research described in this dissertation is concerned with UE that cause
disruption to bus crew schedules and subsequently bus operation. One way to manage UE is by crew rescheduling. Most of the current approaches, which are based on static
schedules, do not provide the capability of crew rescheduling in real time scenarios. In practice, crew rescheduling is managed manually based on supervisors’ capabilities and
experience in managing UE. There are many limitations to manual crew rescheduling, such as it being hard and slow to make decisions when many UE happen at the same
time, the possibility of breaking the EC driving hour rules, and the decisions are not optimum in the use of crew resources. To overcome these limitations, this research
proposes an automated crew rescheduling system. The aim of the system is to help supervisors to make decisions about crew rescheduling while managing UE.
Chapter Two provides a review of the UE problem, the current approaches to the bus
crew scheduling problems, practical experiences of bus companies in dealing with UE problems, the motivation to use MAS, theoretical description of MAS and the current
use of MAS in scheduling. From the analysis of the current approaches, the limitations of the current approaches have been learnt. The limitations in most of the current
approaches to bus crew scheduling are concentrated on achieving an optimum schedule. The definition of the optimum schedule is limited to minimum duties and minimum
cost. However, crew schedules should be flexible enough to accommodate real-time changes in everyday operation. The UE problem is one of the challenges in bus
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operation that needs to be tackled. UE will always happen and nothing can prevent them because the world is imperfect. The only way is to minimize the effect of the UE
problem. When a UE problem takes place it will cause many effects, and one of them is on crew schedules. One way to handle this is with real-time crew rescheduling that
currently is done manually at garages by supervisors. MAS is a promising approach that might be useful to automate the crew rescheduling process. MAS has been known to
provide quick solutions in real-time and in uncertain environments. This paved the way for the main research question of this thesis, which is: Is MAS a suitable approach for
automating crew rescheduling process in real-time so that it will help supervisors in dealing with UE problems to crew schedules?
Chapter Three presents the proposed approach, which is an automated crew
rescheduling system. Prior to that, the chapter presents some of the issues regarding the proposed approach. Two issues are discussed, first, whether to reschedule crews or
reschedule crew schedules; second, whether to propose a complete crew scheduling system or just an additional to the current system. The research found that crew
schedule rescheduling is not suitable for real-time because of the complexity associated with it. The research also concludes that an additional module is suitable for this
research since the focus is on solving problems arising with UE that happen in everyday operation on crew schedules, and not on finding optimum schedules. The chapter
thereafter models the manual way of crew rescheduling and, based on these models, it proposes the Crew Rescheduling System with MAS CRSMAS. Then CRSMAS is
modelled with the concept of MAS. In CRSMAS architecture there are two types of agents which are: duty agent DA and crew agent CA. CA represents a crew, and DA
corresponds to a duty that needs to find a crew because the original crew is late or unavailable. The agent’s interactions are modelled with sequence diagrams which show
the types of messages passing between agents in different scenarios. CRSMAS can be used to help supervisors in making quick decisions about crew rescheduling whenever a
crew is late or unavailable. The decision should not allow the violation of EC driving hour rules, suggest the best, optimum solution, and within a short period of time within
seconds or minutes.
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Chapters Four and Five present the two experiments in order to evaluate CRSMAS
that are single event and multiple events. The single event experiments only test one event at a time but with different types of event lateness, delay, and unavailability,
different types of schedule large, medium, and small, different duty distributions maximum, median, and minimum and different event timings. The purpose is to test
the capability of CRSMAS in all types of events and schedules and also to identify the characteristics of crew schedules that influence the possibility of successful
rescheduling. Multiple events testing looked at several events taking place concurrently and randomly. The purpose is to test the robustness of CRSMAS in handling many
random events at a time. Chapter Four presents the results and analysis of single event experiments, and multiple events experiments and analysis are presented in Chapter
Five. The experiments are conducted using real-world data taken from bus companies in London.
Chapter Six presents the analysis and evaluation of the two experiments in order to
identify the weaknesses in CRSMAS and proposes modification wherever appropriate. The chapter sets two criteria for analysis and evaluation that are matching rate and
rescheduling speed. The analysis shows that the overall matching rate is low because of a few reasons that are the small number of dutiescrews, duties not being equally spread,
relief time not being long enough, the event being too long, and only one round of rescheduling. For every reason given, the chapter suggests a way to deal with it. The
rescheduling speed is quick and acceptable in the real-time scenario. The chapter then identifies and proposes modifications to CRSMAS to make it better so it can fulfil the
aim of its creation. The proposed modifications are the ability to reschedule more than one round, the ability to perform rescheduling of different types of events at the same
time, filtering the events before going through rescheduling process, and changing the crew schedules to support the rescheduling process. The chapter suggests potential
beneficiaries of the system such as operation manager, supervisor, scheduler and researcher in public transport.
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7.3 Conclusions