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Transcript of Implementing continuous improvement using genetic algorithms Petter Øgland, Department of...
Implementing continuous improvement using genetic
algorithms
Petter Øgland, Department of Informatics, University of Oslo
QMOD/ICQSS Conference, Verona, Aug 28th 2009
Structure of presentation
1. Introduction
2. Literature review of CQI methods
3. The new CQI method
4. Example of new method in practical use
5. Discussion
6. Conclusion
Classical QMOD: Deming & Lewin
Deming (1986): Plan, Do, Check, ActJuran (1986): Plan, Control, Improve
Lewin (1950): Unfreeze, change, freeze
Unpredictable organizations where project-by-project approaches fail
Genetic Algorithms: Cultivate the flock rather than the individuals
Research questions
• RQ1: Is it possible to use the GA approach for effective QMS design?
• RQ2: If it is possible, why is it not used?
Structure of presentation
1. Introduction
2. Literature review of CQI methods
3. The new CQI method
4. Example of new method in practical use
5. Discussion
6. Conclusion
GA for understanding OD
• Genetic Algorithms (GA) has been suggested for QM as a part of a more general Complex Adaptive Systems (CAS) approach (Dooley et al., 1995; Dooley, 2000)
• GA on a metaphorical level (Goldstein, 1993; Nelson & Winter, 1982)
• Simulation models based on GA (Bruderer & Singh, 1996)
• GA as integrated part of decision support systems (Greer & Ruhe, 2003)
GA for implementing TQM
Embracing control (OR)
Embracing chaos (CAS)
Organizational development (OD)
Lewin (1950) Goldstein (1993)
Dooley (2000)
Quality management (TQM)
Juran (1964)
Deming (1986)
Imai (1986)???
Structure of presentation
1. Introduction
2. Literature review of CQI methods
3. The new CQI method
4. Example of new method in practical use
5. Discussion
6. Conclusion
Genetic Algorithm (Wikipedia, 2009)
• Choose initial population• Evaluate the fitness of each individual in the
population • Repeat until termination: (time limit or sufficient
fitness achieved) – Select best-ranking individuals to reproduce– Breed new generation through crossover and/or
mutation (genetic operations) and give birth to offsping
– Evaluate the individual fitnesses of the offspring – Replace worst ranked part of population with
offspring
Structure of presentation
1. Introduction
2. Literature review of CQI methods
3. The new CQI method
4. Example of new method in use
5. Discussion
6. Conclusion
Example: The KLIBAS system
• 1991-95– Formal development project– High prestige, management commitment– Project “completed”, but nothing worked
• 1996-99– Informal maintenance cycle– Low prestige, little management commitment– Problems, complaints requests fixed as reported– A practical and useful system develop through many
small iterations
Process maturity in KLIBAS due to managing knowledge/power
Development project
Maintenance process
On paper Systematic (managed by people)
Chaotic
In reality Chaotic Systematic
(managed by computer)
QMS as CAS with automated Pareto analysis at the nexus
Pareto analysis
PRECIP: Manual precipitation stations
AWS: Automatic weather stations
METAR: Airport weather stations
UASS: upper air sounding stations
HIRLAM: quality control by use of forecast data
Monitoring of system outputs and users (customer satisfaction)
SYNOP
System monitoring
e-maile-mail
e-maile-mail
GA implementation of daily maintenance & development
Evaluate population:Real-time and nightly automatic data collection for total system by use of e-mail.
Select solutions for next population:Run a Pareto analysis for setting the agenda for the day. This defines the population of processes to be improved.
Perform crossover and mutation:Read, write, discuss; design and implement etc.; the daily practical work of process improvement.
Enter office on the morning of day i.
Exit office in the afternoon of day i.
i: = i + 1
Productivity indicator
0
20
40
60
80
100
120
140
1992
1993
1994
1995
1996
1997
1998
1999
Productivity AVG = 91UCL = 112 LCL = 70
Structure of presentation
1. Introduction
2. Literature review of CQI methods
3. The new CQI method
4. Example of new method in practical use
5. Discussion
6. Conclusion
Is GA the same as kaizen?
Kaizen GA
Similarities Technical kaizen sounds like GA (Imai, 1986)
Social GA sounds like kaizen (Goldberg, 2000)
Differences Social implementation (skills and attitudes)
Technical implementation (following an algorithm)
GA is a SPECIAL type of kaizen
• It is strictly mathematical (an algorithm), not dependent on intuitive or cultural skills
• It is ”stupid” in the sense that each ant in a colony has a lesser brain than an elephant
• It is ”unfocused” as it aims for many improvements at the same time
• It is ”inefficient” as it progresses by trial and error
But it works!
Why others do not use this approach
1. People are unwilling to be run by computer
2. The GA approach generates complexity
3. It is “common knowledge” that the unfreeze-change-freeze approach is the “one best way”
4. TQM personnel lack technical skills for understanding GA
5. GA makes TQM invisible and thus a poor choice when wanting work acknowledgement
Structure of presentation
1. Motivation
2. Overview of current CQI methods
3. The new CQI method
4. Example of method in use
5. Discussion
6. Conclusion
Conclusion
• There are sociological reasons why people might reject the GA approach to TQM, although it WORKS and it is SIMPLE to implement
• The GA approach seems well-suited for designing QMS bottom-up in complex organizations or as a TQM method for people who enjoy living in chaos
Thank you