| Summary: | 博士 === 國立臺灣大學 === 電機工程學系 === 85 === This dissertation investigates the job-shop scheduling and
railway scheduling problems. The elements of a job shop
scheduling problem consists of a set of machines and a
collection of jobs to be scheduled. Operation precedence
constraints give the order in which the operations that
comprise each job must be processed. The job shop scheduling
problem thus can be defined as the allocation of machines over
time to perform a collection of jobs to minimize/maximize a
performance measure while satisfying the operation precedence
constraints, machine capacity constraints, processing time
requirements, and ready time requirements. On the other hand,
in railway systems, the timetable containing the arrival/
departure times and the track assignments of all trains at each
station is the most essential schedule for day-to-day
operations. Similar to conventional job-shop scheduling
problem, the railway scheduling problem is the
decision of the arrival/departure times and the assigning
of tracks to all trains while minimizing a particular
objective function and satisfying some specified constraints.
The main differences between job-shop scheduling problems and
railway scheduling problems are the constraints that must be
satisfied. In this dissertation, we
propose an approach for general scheduling problems based
on iterative repair. This approach starts with a
heuristically generated schedule which may be infeasible, then
applies local search techniques to generate a good conflict-
free schedule. Since cycles may exist among a sequence
of repair operations during the repair process, we also
propose a cycle detection and resolution scheme in this
dissertation. This approach is not only suitable for static
schedule generation but also for dynamic rescheduling.
Furthermore, this approach is very flexible, it can be adapted
to different scheduling applications.
Experimental results show that the proposed system can not only
generate a good feasible static schedule but also react to the
dynamic and stochastic environment in an efficient manner.
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