Abstract | ||
---|---|---|
Enterprises often implement a measurement system to monitor their march towards their strategic goals. Although this way it
is possible to assess the progress of each goal, there is no structured way to reconsider resource allocation to those goals
and to plan an optimal (or near optimal) allocation scheme. In this study we propose a genetic approach to match each goal
with an autonomous entity (agent) with a specific resource sharing behavior. The overall performance is evaluated through
a set of functions and genetic algorithms are used to eventuate in approximate optimal behavior’s schemes. To outline the
strategic goals of the enterprise we used the balanced scorecard method. Letting agents deploy their sharing behavior over
simulation time, we measure the scorecard’s performance and detect distinguished behaviors, namely recommendations for resource
allocation. |
Year | DOI | Venue |
---|---|---|
2009 | 10.1007/s10287-006-0036-6 | Comput. Manag. Science |
Keywords | Field | DocType |
Strategic planning,Performance measurement,Agent-based computational economics,Genetic algorithms | Mathematical optimization,Performance measurement,Balanced scorecard,Resource allocation,Agent-based computational economics,Shared resource,Strategic planning,Genetic algorithm,Management science,Mathematics,Process management | Journal |
Volume | Issue | ISSN |
6 | 3 | 1619-697X |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Pavlos Delias | 1 | 72 | 12.00 |
Nikolaos Matsatsinis | 2 | 121 | 9.44 |