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Efficient Job Scheduling For A Cellular Manufacturing Environment, Joshua Dennie
Efficient Job Scheduling For A Cellular Manufacturing Environment, Joshua Dennie
Theses
An important aspect of any manufacturing environment is efficient job scheduling. With an increase in manufacturing facilities focused on producing goods with a cellular manufacturing approach, the need arises to schedule jobs optimally into cells at a specific time. A mathematical model has been developed to represent a standard cellular manufacturing job scheduling problem. The model incorporates important parameters of the jobs and the cells along with other system constraints. With each job and each cell having its own distinguishing parameters, the task of scheduling jobs via integer linear programming quickly becomes very difficult and time-consuming. In fact, such a …
A Dynamic Web Server Based Appointment Calendar, Jane Zhong
A Dynamic Web Server Based Appointment Calendar, Jane Zhong
Theses
The most advanced server side technology, Microsoft .NET Framework and Microsoft SQL, will be used to develop a dynamic web server based calendar to handle appointment and meeting scheduling. It s a new calendar in architecture and functions compared to the traditional calendars. The existing commercial calendars, such as Outlook calendar, iCal and MeetingMaker, have a common weakness: unique calendar software should be installed in the user s computer. They are all not web based calendars. Some existing web calendars, like Yahoo calendar, have very limited functions. In this proposal, a web server based calendar is proposed to overcome this …
Faculty Scheduling Using Genetic Algorithms, Kevin Soule
Faculty Scheduling Using Genetic Algorithms, Kevin Soule
Theses
The problem of developing a class schedule for a faculty has been proven to be NP-complete. Therefore when the schedule is large enough, finding just one feasible solution can be impossible for any direct search algorithm within a reasonable time. This project is geared toward investigating the possibility of using genetic-based algorithms to solve faculty scheduling problems of 100 courses or larger quickly. Multiple versions of genetic algorithms and heuristics are tested. Many parameter levels for these algorithms are optimized for fastest convergence.