Page 114 - DCAP601_SIMULATION_AND_MODELING

Simulation and Modelling

Notes Queue Discipline - the process by which a new Arrival advances to truly begin receiving service.
The most ordinary technique is FIFO (first in, first out). But there are other methods: LIFO (last
in, first out); pre-assigned precedence (advance to service separately determined before arrival);
priority by types (categories established before arrival for a variety of reasons, e.g., length of
service time <shorter required service time advances earlier> and size of economic price for
waiting <greater cost advances earlier>); and preemptive (new Arrival displaces another person/
object in the Service Facility and the former returns to the Queue before enduring to receive the
service). We also state here that some consumers may remove themselves from an lively waiting
line and so be “lost” to the system - if they are discontented (upset with waiting) or if they
discover that the good/service is out-of-stock/unavailable.
While it may or may not be really calculable, there is at least an financial cost attributable to
each person or good-in-process while remaining in the Queue. This is a realistic deliberation for
a manager who is concerned with diminishing cost in the productive system.
Lastly we come to the Service Facility as treated in Queuing Theory. Availability, whether the
facility is free or already in- service, is of major concern. Service Time is another serious issue,
identifying that this can differ - as a single server or numerous may offer the same service to
individual customers/goods-in-service or as the individual requires of exacting consumers/goods-
in-service vary as they come to the server. This could be the most unbalanced variable in non-
production line processes. Capacity of the Service Facility, whether of one or more stations (number
of consumers/goods-in-process that can be serviced concurrently), is another measurement of the
analysis. By extension we observe here that the total system - Queue(s) and Service Facility(ies)
shared - may have a certain restricted capacity, which would in turn limit the number of new
Arrivals to precisely matching consumers/goods-in-service exiting the Service Facility.
The practical intention of Queuing Theory is to offer examination tools for systems consisting of
Queues leading to Service Facilities, in order that the systems may be prepared more competent.
Queuing Theory deals mathematically with both the regularities and indiscretions of such
systems - ultimately identifying incidents of congestion (resulting from indiscretions) and
offering avenues for enhancing efficiency, in addition to producing particular numerical data
for further application. The dimensions of congestion offered are as below:
1. The mean and distribution of time used up in a Queue;
2. The mean and distribution of consumers/goods-in-service in both the Queue itself and
the whole system;
3. The mean and distribution of the Service Facility’s “busy periods” (utilization/
unavailability). Each of the three distributions above can be articulated in terms of
mathematical possibilities.
Queuing Theory with its fine-tuned analysis offers a base for a to some extent simplified and
simpler to use set of tools called Model Building and Simulation. The practical production/
operations manager identifies that there is a transaction between queuing costs and service costs
and that jointly they make-up a total cost shared by the producer and the consumer. With an eye
toward reducing overall costs he/she will require an optimum mix of queuing and service costs
to obtain this goal.
Simulation from adequate Models is the method that bridges the gap involving the theoretical
plane of Queuing Theory and the practical tool of a Decision Support System. Simulation is the
experimental laboratory for testing modifications in a productive system through the use of
mathematical models - practically integrating the elements and outlines of a productive system
while displaying key variables subject to experimental manipulation. Using a calculator or a
computer, a investigator can insert suitable values for these key variables into the model, run
the simulation and obtain achievable values for the genuine system without superseding in the
system itself. With a computer the model can be run rapidly and frequently with various
alterations.

108 LOVELY PROFESSIONAL UNIVERSITY