Decision Support System presentation and how to select a model and evaluate it from the wide range of models which might solve your problem.

1. DSS PRESENTATION
MODEL SELECTION AND
SEQUENCING IN DECISION
SUPPORT SYSTEMS
GROUP MEMBERS:
116101-PUJA PUNIA
116102-RAHUL YADAV
116103-RAHUL KUMAR MEENA
116104-RAHUL SINGH RAJPUT
116105-RAJAT GOEL

2. ABSTRACT
 A crucial problem confronting the users of decision
support systems(DSS) is the identification of an
appropriate model or a sequence of models that may
be used to solve a particular problem.
 If a single model in the model base cannot satisfy
user requirements, the solution procedure seeks to
obtain a string of models such that some
performance measure is minimized.

3. INTRODUCTION
 A complementary trend is the popularity of decision support
systems (DSS), where the intended functionality of these systems
is to assist decision makers' solve semi-structured and un-
structured problems that require extensive usage of
computational models.
 The high costs of developing and maintaining models further
reinforce the importance of model management in organizations.
 A need to maintain consistency, integrity, non-redundancy, and
flexibility and the need to enhance the availability of models on
an organization-wide basis, has led to the evolution of software
known as model management systems (MMS).

4. INTRODUCTION CONTD.
 The MMS component of a DSS provides decision support by
abstracting procedural and technical aspects of model
implementation and making these invisible to the non-technical
user .
 Two fundamental concerns faced by a user in interacting with a
DSS are: what is the appropriate model to use for a particular
problem, and if such a model does not exist as a single entity,
how can the models defined in the model base be combined to
solve the problem?
 These concerns are particularly relevant when complex decision
processes require the utilization of several models in sequence.
The model linking procedure must not violate the fundamental
objective underlying MMS that of relieving the user from
procedural and technical details.

5. CONTD.
 This paper develops a procedure for model
sequencing that permits the construction of ad
hoc model chains. Knowledge is extracted from a
specialist and stored in the model base, allow- ing
the user's interaction with the DSS to be limited to
conceptualizing the problem in terms of output
requirements.

6. MODEL SELECTION AND
INTEGRATION
 Model selection deals with locating an appropriate model in the
model base that maps into the problem posed by the decision
maker.
 If this search process identifies a subset of the model space
that is potentially applicable, i.e. more than one model is able
to satisfy the user's output requirement, then the MMS chooses
to select and execute a single model from this subset.
 The model integration problem concerns itself with finding a
target set of models to be combined and executed, if, in fact,
no single model is able to provide the requisite output.

7. CONTD.
 Blanning proposed a model representation scheme where model inputs
and outputs are treated as tuples in a relational table, with each tuple
containing information about a specific variable, the models to which it
is input, and the models that estimate it.
 Geoffrion describes a structured modelling approach to model
representation where models are represented in canonical form.
Structured modelling is an extension of graph-based approaches and the
framework uses a "hierarchically organized, partitioned and attributed
acyclic graph to represent a model or a model class“.
 The construction of composite models allows input ports to represent
the input requirements of the model and output ports to represent the
outputs produced. These input/output relationships are used to
construct higher-level composite models.

8. CONTD.
 The use of information in our approach is parsimonious,
as are our assumptions regarding the knowledge
possessed by the user of the MMS (the only information
required by the approach are model inputs and
outputs).
 The model sequencing procedure provides the user
with an optimal sequence of models, where the
criterion for optimality can be varied if so desired.

9. PROCEDURE
 The manner in which the problem is modelled is influenced by the
availability of input data. The objective of MMS is to assist the user
select an appropriate model (or a model string) from the model
base. This selection may be based on either
(i) desired output variables
(ii) both desired output variables and input variables that can be supplied.
 The model sequencing procedure described here operates on an
existing model base. Two assumptions are made regarding the
model base: (i) it contains a large number of models represented
using perhaps different formalisms, and (ii) these models have been
created by knowledgeable users or specialists and have passed some
type of quality assurance test to ensure their validity.

10. 1. MODEL BASE CATALOGS
 Three primary catalogs are maintained in the
system, one each for models (MODCAT), out- put
variables (OUTCAT) and input variables (INCAT).
 MODCAT contains the name of the model, a brief
description of its scope, and assumptions underlying
its use, such as linearity and independence.
 The catalog of outputs is annotated with a
description of the procedure by which the output is
computed.

11. COMPONENTS OF THE
MODEL SELECTOR
 The model selector software consists of two primary modules
called DIALOG and SEARCH. The former provides a user-
friendly interface for a non-technical user and uses the
catalogs to ascertain if a single stored model can satisfy a
specific user need.
 SEARCH consists of a 0/1 integer programming formulation that
seeks to obtain an optimal path to the required outputs.
 The DIALOG component. DIALOG is menu- driven and prompts
the user to supply required output variables and associated
objectives.

12. 3. ITERATION BETWEEN
SEARCH AND DIALOG
 DIALOG analyses the final solution obtained from
SEARCH and uses the catalogs to display the list of
model names, input variable names, and the associated
cost of model execution.
 The primary objective now is to seek another model
string that does not require this input variable. In this
fashion, DIALOG and SEARCH are executed iteratively to
obtain an acceptable model string.
 Iteration between SEARCH and DIALOG may also occur if
either the user finds the objective(s) of the model(s)
selected by the system to be inappropriate, or, the cost
of executing the model string is unacceptable.

13. CONTD.
 For example, if the user does not specify an objective a
priori, and the system selects an estimation model to
compute the value of a certain variable, the user has
the flexibility of requesting SEARCH to find another
model with an optimization objective.
 The approach described combines management science
techniques and basic user interface design principles to
remove procedural and technical obstacles in the
utilization of models by end-users.

14. A PROTOTYPE SYSTEM
 Prototype system has been applied to a model
base.
 The menu driven DIALOG component is
programmed in GWBASIC and the integer
program is solved using LINDO.

15. ASCERTAINING COSTS
 The cost parameters, and the policies used for
obtaining them, can be classified into two distinct
Classes.
 Method employed for estimating the dollar value.
1. In some instances the estimates used are specific
to the computer system (and the associated
charge-back rules)where the model base is
installed.
2. For the prototype developed, all models are stored
and executed on the IBM 3090/600E at Louisiana
State University (LSU), Baton Rouge. Thus, various
cost parameters were estimated based on the
charging algorithm employed by LSU's System
Network Computer Center.

16. CONTD.
(I)Model execution
 CPU usage costs are computed as the product of
actual CPU time (in seconds) and a factor F
(S/second).
 The value of F varies depending on the time of
the day.
 For prototype system, it was assumed that all
models will be executed during "demand time"
(i.e. between 8 am and 5 pm).
 After a model had been debugged and validated,
average CPU usage time was calculated by
executing each model with historic (or
representative) data.

17. (a)Primary inputs available in
the database
 cost was obtained by employing the
university's charging algorithm for
performing I/O operations.
 Cost parameters were calculated by
multiplying the average number of I/Os
required by the model and a charge factor
I ($ per 100 I/Os).
 Standard tape mounting charges used by
the university were incorporated for
obtaining the cost of inputs that required
reading data from magnetic tapes.

18. (b)Primary inputs supplied by
decision maker
 Some primary inputs (e.g. expected
growth rate) must be supplied by the
decision maker, based on his/her
experience or intuition, the cost of these
inputs were arbitrarily set to a low value.

19. C)Primary inputs available from
'outside‘ sources
Primary inputs available from 'outside‘ sources fall into two
major categories.
1.Inputs are obtainable from commercial on-line databases.
The cost associated with this type of input is based on the
connect time to the on-line database.
 These inputs can be further classified into two classes:
(a) input data that can be stored for future use, and
(b) up-to-date, current data that must be down-loaded from
the database each time the input is required.

20. CONCLUSION
 Model manipulation continues to be an important
issue for both academics and practitioners.
 Model management systems attempt to enhance
utilization by effectively shielding non-expert users
from internal representations of models in
computers.
 Our research has addressed one respect of model
management that concerns itself with model
selection and sequencing.
 In this paper we have described mechanisms for
achieving model selection and sequencing that are
independent of model implementation and storage.