Schedule variability

U.S. General Accounting Office. Railroad safety engineer work shift length and schedule variability. Report No GAO/RCED-92-133, 1992. 

Time management The auditor will need to be sensitive to the schedule of the school and conduct the audit accordingly. The auditor will need to accomplish a review of documentation, school inspection, and interviews while taking into consideration faculty and staff work schedules, class schedules, and hours of school operation. This will require the use of time management skills to address all of the school scheduling variables and issues that need to be addressed within the audit. 

A problem that is all too familiar to suppliers in the automotive industry is that of schedule variability. A vehicle manufacturer issues delivery schedules to specify how many parts of each type are required each day for the following month. And each day a call-off quantity is issued, which specifies how many the vehicle manufacturer actually wants. The two sets of figures are not necessarily the same, although they usually add up to the same cumulative numbers for the month as a whole. In other words, the total scheduled quantities and the total call-off quantities are the same. So what is the problem ... 

In Method 3, controller parameter scheduling, one or more controller settings are adjusted automatically based on the measured value of a scheduling variable. 

Adjustment of the controller gain, gain scheduling, is the most common method. The scheduling variable is usually the controlled variable or set point, but it could be the manipulated variable or some other measured variable. Usually, only the controller gain is adjusted, because many industrial processes exhibit variable steady-state gains but relatively constant dynamics (for example, pH neutralization). 

Approach (a) is illustrated in the next section. Approaches (b) and (c) can be implemented in several different ways. For example, different values of the control settings can be stored for each region (that is, a table look-up approach). Then the controller settings are switched whenever the scheduling variable enters a new region. Alternatively, a dynamic model can be developed for each region and a different controller designed for each model (Bequette, 1998). 

The most widely-used type of controller parameter scheduling is gain scheduling. A simple version has a piecewise constant controller gain that varies with a single scheduling variable, the error signal e ... 

For fixed-bed reactors containing rapidly deactivating catalysts, the scheduled changes ia operating variables to accommodate activity loss can have a marked effect on mn length. This is exemplified by acetylene hydrochiorination to produce vinyl chloride ia tubular fixed-bed reactors. Steel reactors,... 

If the forward velocity of the ship is the state variable u, a best estimate of which is given by the Kalman filter, the gain scheduling controller can be expressed as... 

Once you have determined where to start, you can begin to develop a schedule. However, it s prudent to wait until the pilot test is complete before firmly establishing your timetable, since the results may well affect your estimates. In setting the installation schedule, be sure to take into account variables such as ... 

Rosa, R. R., Colligan, M. J., Lewis, P. (1986). Extended Workdays Effects of 8-hour and 12-hour Rotating Shift Schedules on Test Performance, Subjective Alertness, Sleep Patterns and Psychosocial Variables. Proceedings of the Human Factors Society 30th Annual Meeting, Vol. 2,882-889. 

Everything in Swarm is an object with three main characteristics Name, Data and Rules. An object s Name consists of an ID that is used to send messages to the object, a type and a module name. An object s Data consists of whatever local data (i.e. internal state variables) the user wants an agent to possess. The Rules are functions to handle any messages that are sent to the object. The basic unit of Swarm is a swarm a collection of objects with a schedule of event over those objects. Swarm also supplies the user with an interface and analysis tools. 

During polymerization, when Initiator Is Introduced continuously following a predetermined feed schedule, or when heat removal Is completely controllable so that temperature can be programmed with a predetermined temperature policy, we may regard functions [mo(t ], or T(t), as reaction parameters. A common special case of T(t) Is the Isothernral mode, T = constant. In the present analysis, however, we treat only uncontrolled, batch polymerizations In which [mo(t)] and T(t) are reaction variables, subject to variation In accordance with the conservation laws (balances). Thus, only their Initial (feed) values, Imo] andTo, are true parameters. 

Multiscale modeling of process operations. The description of process variables at different scales of abstraction implies that one could create models at several scales of time in such a way that these models communicate with each other and thus are inherently consistent with each other. The development of multiscale models is extremely important and constitutes the pivotal issue that must be resolved before the long-sought integration of operational tasks (e.g., planning, scheduling, control) can be placed on a firm foundation. 

The last step in the preceding argument, the use of our knowledge about flowshop scheduling, turns what had been a mainly syntactic criterion over the tree structure of the example, into a criterion based on state variables of (jc, y). The state variable values, the completion times of the various flowshop machines, are accessible before the subtrees beneath jc and y have been generated. Indeed, they determine the relationships between the respective elements of the subtrees (jcm, yu). If we can formalize the process of showing that the pair (jc, y) identified with our syntactic criterion, satisfies the eonditions for equivalence or dominance, wc will in the process have generated a new equivalence rule. 

An adaptive control system can automatically modify its behaviour according to the changes in the system dynamics and disturbances. They are applied especially to systems with non-linear and unsteady characteristics. There are a number of actual adaptive control systems. Programmed or scheduled adaptive control uses an auxiliary measured variable to identify different process phases for which the control parameters can be either programmed or scheduled. The "best" values of these parameters for each process state must be known a priori. Sometimes adaptive controllers are used to optimise two or more process outputs, by measuring the outputs and fitting the data with empirical functions. 

The main challenge in short-term scheduling emanates from time domain representation, which eventually influences the number of binary variables and accuracy of the model. Contrary to continuous-time formulations, discrete-time formulations tend to be inaccurate and result in an explosive binary dimension. This justifies recent efforts in developing continuous-time models that are amenable to industrial size problems. 

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