Modeling Operations Research 1
Managerial decision modeling
Hoofdstuk uno
What is decision modeling?
Although there are several definitions of decision modeling, we define it here as a scientific approach
to managerial decision making. Alternatively, we can define it as the development of a model of a
real-world problem scenario or environment. Big organisations as IBM, Google, UPC etc frequently
use decision modeling to help solve complex problems. Although mathematical tools have been in
existence for thousands of years , the formal study and application of quantitative decision modeling
techniques to practical decision making is largely a product of the twentieth century.
It isn’t enough, though, just to know the mathematical details of how a particular decision modeling
technique can be set up and solved. It is equally important to be familiar with the limitations,
assumptions, and specific applicability of the model.
Types of decision models
1. Deterministic models
Deterministic models assume that all the relevant input data values are known with
certainty.; that is, they assume that all the information needed for modeling a decision-
making problem environment is available, with fixes and known values. An example of such a
model is the case of Dell Corporation, which makes several different types of PC
products(laptops, desktops), all of which compete for the same resources(labor, hard disks,
chips…). Dell knows the specific amounts of each resource required to make one unit of each
type of PC, based on the PC’s design specifications. Further based on the expected selling
price and cost prices of various resources, Dell knows the expected profit contribution per
unit of each type of PC.
Waarden zijn vast/zeker
2. Probabilistic models
In contrast to deterministic models, probabilistic models(also called stochastic models)
assume that some input data values are not known with certainty. That is, they assume that
the values of some important variables will not be known before decisions are made. It is
therefore important to incorporate this “ignorance” into the model. An example of this type
of model is the decision of whether to start a new business venture. As we have seen with
the high variability in the stock market during the past several years, the success of such
ventures is unsure. However, investors have to make decisions regarding this type of
venture, based on their expectations of future performance. Clearly, such expectations are
not guaranteed to occur. In recent years, we have all seen several examples of firms that
have yielded great rewards to their investors and others that have either failed.
Onzekerheid speelt een rol
Managerial decision modeling
Hoofdstuk uno
What is decision modeling?
Although there are several definitions of decision modeling, we define it here as a scientific approach
to managerial decision making. Alternatively, we can define it as the development of a model of a
real-world problem scenario or environment. Big organisations as IBM, Google, UPC etc frequently
use decision modeling to help solve complex problems. Although mathematical tools have been in
existence for thousands of years , the formal study and application of quantitative decision modeling
techniques to practical decision making is largely a product of the twentieth century.
It isn’t enough, though, just to know the mathematical details of how a particular decision modeling
technique can be set up and solved. It is equally important to be familiar with the limitations,
assumptions, and specific applicability of the model.
Types of decision models
1. Deterministic models
Deterministic models assume that all the relevant input data values are known with
certainty.; that is, they assume that all the information needed for modeling a decision-
making problem environment is available, with fixes and known values. An example of such a
model is the case of Dell Corporation, which makes several different types of PC
products(laptops, desktops), all of which compete for the same resources(labor, hard disks,
chips…). Dell knows the specific amounts of each resource required to make one unit of each
type of PC, based on the PC’s design specifications. Further based on the expected selling
price and cost prices of various resources, Dell knows the expected profit contribution per
unit of each type of PC.
Waarden zijn vast/zeker
2. Probabilistic models
In contrast to deterministic models, probabilistic models(also called stochastic models)
assume that some input data values are not known with certainty. That is, they assume that
the values of some important variables will not be known before decisions are made. It is
therefore important to incorporate this “ignorance” into the model. An example of this type
of model is the decision of whether to start a new business venture. As we have seen with
the high variability in the stock market during the past several years, the success of such
ventures is unsure. However, investors have to make decisions regarding this type of
venture, based on their expectations of future performance. Clearly, such expectations are
not guaranteed to occur. In recent years, we have all seen several examples of firms that
have yielded great rewards to their investors and others that have either failed.
Onzekerheid speelt een rol
