Linear Programming pge 154

Every LP problem is composed of:

• decision variables (who's values are unknown and are searched for),
• an objective function (a linear mathematical function that relates the decision variables to the goal, measures goal attainment, and is to be optimised),
• objective function coefficients (unit profit or cost coefficients indicating the contribution to the objective of one unit of a decision variable),
• constraints (expressed in the form of linear inequalities or equalities),
• capacities (upper, lower limits of variables), and
• input/output coefficients.

Satisfaction of multiple goals page 150

• One decision-making process must try to meet more that one goal.
• Simultaneous goals are often conflicting – achieving one goal makes it harder to achieve the other(s).
• Try to determine a single measure of effectiveness that judges the achievement of all goals.
• Sometimes very difficult to do.

A decision situation in which alternatives are evaluated with several sometimes conflicting goals.

Methods:

• Utility theory.
• Goal programming.
• Linear programming with goals as constraints.
• Point allocation systems.

Mathematical programming page 153

(Not to be confused with computer programming).

Use of defined and verified mathematical processes (algorithms techniques etc.) to determine best outcomes.

• Linear programming
• MinMax techniques
• Game theory

Is a family of tools designed to solve managerial problems in which the decision maker must alloate scarce resources among competing activities to optimise a measurable goal.

• Tools for solving managerial problems
• Useful when decision-maker must allocate resources amongst competing activities.
• Allocation of resources to achieve optimised specific goals.

Modeling with spreadsheets page 145

see Modelling and Analysis.ppt

Spreadsheets are a computerised mathematical and data management tool.

• An end-user modeling tool.
• Flexible and ‘easy’ to use.
• Supports complex mathematical methods.
• Can be used to implement linear programming techniques.
• Supports complex statistical and regression analysis methods.
• Provides services like what-if analysis, data-base management, automation macros.
• Can be used for static and dynamic modeling.
• risk analysis can be incorporated
  

Models can be developed and implemented in a variey of programming languages and systems.

Spreadsheets include extensive forecatsing, statistical and other modeling and databse management capabilities, functions and routines. As spreadsheet packages eveolved, add-ins were develpoed for structuring and solving specific model classes.

DSS related add-ins

Most popular end user modeling tool, incorporates many power financial statistical, mathematical and other functions.

Spreadsheets can perform model solution tasks such as:

1. Linear programming
Informally, linear programming determines the way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model and given some list of requirements represented as linear equations.
from wiki: http://en.wikipedia.org/wiki/Linear_programming

2. Regression analysis
regression analysis refers to techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
from wiki: http://en.wikipedia.org/wiki/Regression_analysis

Other features:

• what-if analysis

http://office.microsoft.com/en-au/excel/HA102431641033.aspx
http://office.microsoft.com/en-us/excel/CH010004551033.aspx
http://www.informit.com/podcasts/episode.aspx?e=B4C8DE7D-3AC1-45CD-8F4A-1F31714AA61A
http://www.sskkii.gu.se/publications/Documents/html/dynwhatif/

• goal-seeking: Indicating a taget cell,its desired value and changing a cell.
  
• datamanagement
• programmability (macros)

Most OLAP systems ahev the look and feel of advance spreadsheet software.

Most spreadsheet packages provide seemless integration beciase they read and write common file structures and easily interface with databases.