Problem Description

Mangers deal with a number of decisions about processes, capacity, location, inventory, etc. The process of making a decision is complicated because of the uncertainties of the future. This implies that the future outcomes of alternatives that we consider today are in doubt.

The aim of this project is to build a decision support system that allows the user to make decisions under uncertainty. We describe a systematic approach that can be used by managers in the process of making decisions. To learn more about decision making under uncertainty, we refer the students to Krajewski and Ritzman (2002).

Solution approach

Step 1:

List the feasible alternatives.

Step 2:

List the events that have an impact on the outcome of each alternative but are not under managers’ control.

Step 3:

Estimate the payoff for each alternative in each event.

Step 4:

Estimate the likelihood of each event using past data, executive opinion, or other forecasting methods.

Step 5:

Select a decision rule to evaluate the alternatives, such as choosing the alternative with the lowest expected cost or choosing the alternative with the maximum expected profits.

The following are decision rules to help the managers select an alternative.

Maximin: Choose the alternative that is the “best of the worst.” This rule is for the pessimist who anticipates the “worst case” return for each alternative.

Maximax: Choose the alternative that is the “best of the best.” This rule is for the optimist who anticipates the “best case” return for each alternative.

Laplace:  Choose the alternative with the best-weighted payoff. To find the weighted payoff, give equal importance to each event. For example, if there are n events, the probability assigned to each event is 1/n. This rule is for the realist.

Minimax: Choose the alternative that gives the best “worst regret.” Calculate a table of regrets in which the rows represent the alternatives and the columns represent the events. A regret is the difference between a given payoff and the best payoff in the same column. For an event, it shows how much is lost by picking an alternative to the one that is best for this event. The regret can be lost profit or increased cost, depending on the situation.

User Interface

1. Build a welcome form.
2. Build a data analysis form. The following are suggestions to help you design this form.
3. Insert two text boxes where the user types in the total number of events (n) and the total number of alternatives (m). Upon submission of this information a table appears. This table has dimensions m by n. The user types in this table the payoff of each alternative in each event. Label the rows by the name of alternatives. Label the columns by the name of events.
4. Insert a command button titled “Enter the Likelihood of Events.” When the user clicks on this button, a table with dimensions 1 by n appears. The user types in this table the likelihood of each event, if this information is available.
5. Insert a frame named “Selection Rules.” The frame includes five option buttons and a command button. The option buttons enable the user to select one of the following decision rules: maximin, maximax, Laplace, minimax regret and expected payoff. When the user clicks on the command button, the decision rule chosen applies, and the user is prompted about the best alternative found. Note that the expected payoff decision rule applies in the case that the user has provided information about the likelihood of the events.
6. Build a form that allows the user to access the reports created.

Design a logo for this project. Insert this logo in the forms created above. Pick a background color and a font color for the forms created. Include the following in the forms created: record navigation command buttons, record operations command buttons, and form operations command buttons as needed.