scheduling

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scheduling

[′skej·əl·iŋ]
(industrial engineering)
A decision-making function that plays an important role in most manufacturing and service industries and often allows an organization to operate with a minimum of resources. Scheduling is applied in procurement and production, in transportation and distribution, and in information processing and communication. In manufacturing, the scheduling function coordinates the flow of parts and products through the system, and balances the workload on machines and personnel, departments, and the entire plant.

Scheduling

A decision-making function that plays an important role in most manufacturing and service industries. Scheduling is applied in procurement and production, in transportation and distribution, and in information processing and communication. A scheduling function typically uses mathematical optimization techniques or heuristic methods to allocate limited resources to the processing of tasks.

Project scheduling is concerned with a set of activities that are subject to precedence constraints, specifying which jobs have to be completed before a given job is allowed to start its processing. All activities belong to a single (and typically large) project that has to be completed in a minimum time; for example, a large real estate development or the construction of an aircraft carrier.

Production or job shop scheduling is important in manufacturing settings, for example, semiconductor manufacturing. Customer orders have to be executed. Each order entails a number of operations that have to be processed on the resources or the machines available. Each order has a committed shipping date that plays the role of a due date. Production scheduling often also includes lot sizing and batching.

Timetabling occurs often in class room scheduling, scheduling of meetings, and reservation systems. In many organizations, especially in the service industries, meetings must be scheduled in such a way that all necessary participants are present; often other constraints have to be satisfied as well (in the form of space and equipment needed). Such problems occur in schools with classroom and examination scheduling as well as in the renting of hotel rooms and automobiles.

Work-force scheduling (crew scheduling, and so on) is increasingly important, especially in the service industries. For example, large call centers in many types of enterprises (airlines, financial institutions, and others) require the development of complicated personnel scheduling techniques.

In order to determine satisfactory or optimal schedules, it is helpful to formulate the scheduling problem as a mathematical model. Such a model typically describes a number of important characteristics. One characteristic specifies the number of machines or resources as well as their interrelationships with regard to the configuration, for example, machines set up in series, machines set up in parallel. A second characteristic of a mathematical model concerns the processing requirements and constraints. These include setup costs and setup times, and precedence constraints between various activities. A third characteristic has to do with the objective that has to be optimized, which may be a single objective or a composite of different objectives. For example, the objective may be a combination of maximizing throughput (which is often equivalent to minimizing setup times) and maximizing the number of orders that are shipped on time.

The scheduling function is often incorporated in a system that is embedded in the information infrastructure of the organization. This infrastructure may be an enterprise-wide information system that is connected to the main databases of the company. Many other decision support systems may be plugged into such an enterprise-wide information system—for example, forecasting, order promising and due date setting, and material requirements planning (MRP).

The database that the scheduling system relies on usually has some special characteristics. It has static data as well as dynamic data. The static data—for example, processing requirements, product characteristics, and routing specifications—are fixed and do not depend on the schedules developed. The dynamic data are schedule-dependent; they include the start times and completion times of all the operations on all the different machines, and the length of the setup times (since these may also be schedule-dependent).

The economic impact of scheduling is significant. In certain industries the viability of a company may depend on the effectiveness of its scheduling systems, for example, airlines and semiconductor manufacturing. Good scheduling often allows an organization to conduct its operations with a minimum of resources. See Material resource planning, Production planning

scheduling

(algorithm)
The arrangement of a number of related operations in time.

There are several kinds of scheduling related to computers:

instruction scheduling - sequencing the instructions executed by the CPU

multitasking ("process scheduling") - sharing a CPU between several processes

application software to help organise your daily meetings etc.

task scheduling - algorithms to solve the general problem of satisfying time and resource constraints between a number of tasks.

Compare planning.
References in periodicals archive ?
In this case the use of scheduling theory to plan behavior results in a 50% increase in the number of communities warned.
What is the probability that an operator who does not know the scheduling theory solution will find it by chance?
Although we strongly advocate the use of scheduling theory as a tool for analyzing behavior in complex systems, readers should be aware that many problems remain.
In what follows we describe approaches to numerical solutions of scheduling problems that have been developed by those working in scheduling theory.
It is therefore important to have a clear picture of the limits on scheduling theory solutions because if our general approach is correct, such limits place equivalent limits on research into strategic behavior and the behavior of operators.
Apart from theoretical issues about the formal use of scheduling theory, such considerations point to the extreme difficulty of some tasks that designers increasingly impose on operators as supervisory control becomes more widespread and the role of operators as strategic decision makers becomes more important.
This may be because of computational intractability or simply because the problem has never been considered by scholars of scheduling theory. For example, Moray et al.
There are several hundred known optimal algorithms or near-optimal heuristics; however, in the case of expirable tasks, extending the use of scheduling theory to behavioral analysis frequently leads to problems for which no solution is yet known.
Although scheduling theory will often suggest decision aids to enhance strategic behavior, it is also possible for researchers of strategic behavior to make significant contributions to scheduling theory by designing heuristics that address the resources of the human problem solver rather than those of the computer (as is the usual case), and by designing displays and interfaces that support the recognition of solutions without the need for arithmetical calculations.
Moreover, scheduling theory provides a framework for the analysis of behavior in a wide variety of work situations as well as in laboratory tasks.
In this paper we present a conceptual framework and task taxonomy that permit human factors researchers to make use of the extensive and sophisticated methods that exist in scheduling theory for the study of strategic behavior.