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Intro to Movie Magic Budgeting and Scheduling

Skip to content. To transit agencies, scheduling puts reliable service on the street where it will be most utilized. In addition, scheduling provides data and information to support other sec- tions such as Marketing, Planning, Operations, Administration, and many downstream systems like AVL, APCs, voice annunciators, trip planners, and real time information systems.

The extent of these impacts is sometimes not fully understood within the agency. Scheduling is the brain of the transit organism in its day- to-day functioning. The schedule may also include ad- ditional information such as route descriptions, deadhead times, interline information, run numbers, and block numbers. In this manual, layover and recov- ery are calculated together and the total time between trips is referred to as layover.

Chapter 1.

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Introduction What makes a good schedule? Reliability, service frequency that matches demand or agency policies, operating speeds as high as possible consistent with safety, and minimization of op- erating and capital costs are all important and at times contradictory goals. There are both art and science required in achieving these goals, beyond the requirement that the scheduler be capable of and comfortable with dealing with increasing reams of data.

This manual explores both the science as well as the art of scheduling. But he or she also faces more challenges. Deadlines are often much shorter as a result of what can be, at times, a politically driven service implementation process. There are now ever more downstream customers for scheduling material. At one time, a schedule department was also a print shop, but much of the fi nished schedule information is now needed in elec- tronic form. This means that scheduling purely by hand is no longer practical, even for small transit properties, where keeping on-board electronic gadgets such as next stop annunciators up to date can take longer than the regular scheduling process.

The practice of scheduling is becoming a case of learning another computer program and manipulating the program to get results within the guidelines of the parameters programmed into it.

A quick introduction to scheduling

Introduction within the software but not as competent or even as practical as they could be if developed with a thorough knowledge of the craft of scheduling. The goal of this manual is to provide the reader with all of the skills necessary to be a profes- sional scheduler, lacking only the years of practice needed to develop and apply the seasoning. We pledge to try and make the reading interesting and informative along the way.

This manual is designed to focus on bus scheduling. Rail schedulers will still fi nd sections with information specifi c to their particular mode. However rail scheduling particularly timetabling has many unique aspects that are beyond the scope of this project.

CPU Scheduling: Dispatcher

One last note before we begin: we certainly do not want this manual to be any more mystifying than it needs to be. The convergence line is there, too. These are results of one hundred iterations by the 'Bayes' method. Figure We consider the case where the objective is the number of "empty" hours when the teachers wait for the next scheduled lectures. One calls these hours as "teacher gaps, " or just "gaps" for short.


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We search for such schedules that reduce the sum of teacher gaps considering the schedules of fifth to twelfth class of the public high school. Other factors are school-specific and should be included adapting the software to specific schools.

The algorithm follows a general pattern of permutation algorithms related to the Bayesian Heuristic Approach [ Mockus, ] record the current schedule by reading the data file , record the current number of teacher gaps , set the initial iteration number , set the current iteration number , if , stop and print the current schedule, set the initial teacher number , set the current teacher number , if go to the step 4, generate uniformly distributed random number , go to the step 7, if , select a gap of the teacher , go to step 7, if there are no gaps, select an open lesson of the -th teacher, go to step 7, if there are no open lessons, make a permutation by exchanging the "gap class" with the open one , test the feasibility conditions listed in section 3.

It mimics, in a sense, the usual ways to improve existing schedules. However, replacing a "gap" class by the open one, defined in the step thirteen, a new gap for another teacher is opened, as usual. To satisfy these conditions the probability to reach any schedule should be positive.

In these schools the study schedules are fixed for each grade. Application of this algorithm is more difficult, if classes are divided depending on subjects of choice. For example, some students may choose religion, some others ethics, e.

Introduction to Scheduling Concepts in CP Optimizer

Here the number of "classes" is greater because the schedules of divided classes of the same grade are different. In addition, classes are divided for some subjects and united for others, as usual. In so-called "Profiled Schools" the students of eleventh and twelfth year select, for example, fourteen subjects from sixty available. That means different individual schedules for most of the students. There are no stable student classes anymore.

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They are replaced by changing "interest groups". The changes happen each hour. Personal choices of eleventh and twelfth grade students are defined as sets of subjects. Sequences of these subjects and the corresponding class-rooms are defined by the general school schedule. In this case a new approach is needed for optimal scheduling.

An example is the commercial system. The helps to produce feasible schedules by performing corrections, for example, by closing some gaps, and informing about violated constraints and inconveniences.


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Therefore, comparison with other systems is very difficult. In this sense, the and other similar systems can be regarded mainly as "Support Systems.

In the genuine optimization system a human operator specifies just general objectives and constraints. The schedules, both general and personal, are produced automatically by an optimization system. These schedules may be corrected by an operator considering some additional factors not included in the general objectives and constraints. The human operator can influence the outcome of optimization by choosing an initial schedules, too.