Implementing continuous improvement using genetic algorithms. Petter Øgland, Department of Informatics, University of Oslo QMOD/ICQSS Conference, Verona, Aug 28th 2009. Structure of presentation. Introduction Literature review of CQI methods The new CQI method
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Petter Øgland, Department of Informatics, University of Oslo
QMOD/ICQSS Conference, Verona, Aug 28th 2009
Juran (1986): Plan, Control, Improve
Deming (1986): Plan, Do, Check, Act
Lewin (1950): Unfreeze, change, freeze
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 understanding OD
SYNOP of a more general Complex Adaptive Systems (CAS) approach (Dooley et al., 1995; Dooley, 2000)
AWS: Automatic weather stations
PRECIP: Manual precipitation stations
UASS: upper air sounding stations
METAR: Airport weather stations
HIRLAM: quality control by use of forecast data
Monitoring of system outputs and users (customer satisfaction)
System monitoringQMS as CAS with automated Pareto analysis at the nexus
Enter office on the morning of day of a more general Complex Adaptive Systems (CAS) approach (Dooley et al., 1995; Dooley, 2000)i.
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.
Exit office in the afternoon of day i.
i: = i + 1GA implementation of daily maintenance & development