The problem of optimal team formation is domestic to many areas of work
organization including education, sport, and business. It is beyond manual
implementation to build near optimal teams as soon as the pool of available
personnel grows into several tens. The selection process itself is usually well
defined for each team we construct the criteria relating to the required properties
of the team members. Because these properties can be arbitrarily combined in the
personnel, the objective function becomes self-conflicting. This aggravates the
team formation and calls for specialized software support.
We use quantities values to describe the employees’ capabilities like technical
expertise and also to describe the budget limits needed to format a team
The problem is to estimate the level of risk involved in a software engineering
project. For the sake of simplicity, we will arrive at our conclusion based on two
inputs: project funding and technical experience for the team members.
Suppose our inputs are project_funding and team_experience_level. We can get
the fuzzy values for these crisp values by using the membership functions of the
appropriate sets.
The sets defined for project_funding are very low (0,0,10,30), low (10,30,40,60),
medium (40,60,70,90), high (70,90,100,100).
The sets defined for team_experience_level are beginner (0,15,30), intermediate
(15,30,45), expert (30,60,60).
The set defined for the risk is low (0,25,50), normal (25,50,75), high (50,100,100).The Rules
Now that we have the fuzzy values and we can use the fuzzy rules to arrive at the
final fuzzy value. The rules are as follows:
1. If project_funding is high or team_experience_level is expert then risk is
low.
2. If project_funding is medium and team_experience_level is intermediate
or team_experience_level is beginner then risk is normal.
3. If project_funding is very low then risk is high.
4. If project_funding is low and team_experience_level is beginner then risk
is high.
Input:
1- First line represents number of input variables = 2
2- Second line gives a Variable Name and its crisp input to fuzzify it later (e.g.
project funding 50, experience level 40).
Output:
1- Fuzzifying the inputs
2- Inference of rules
3- Defuzzification output
Test Case
Input:
Variables: 2
Project Fund: 50
Experience Level: 40
Output:
Predicted Value (Risk) = 37.5
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