Applications of Evolutionary Algorithms to Management Problems
Evolutionary Algorithms (EA) are metaheuristics based on a rough abstraction of the mechanisms of natural evolution. While the first variants of EA were already invented in the 1960s, it is in the last 15–20 years that these powerful methods of heuristic optimization have attracted broader attention also outside the scientific community.
This chapter reviews EA from the perspective of management applications where “management” indicates that predominantly economic targets are pursued. In general terms, the preferred areas of application for EA, and other metaheuristics as well, are optimization problems that cannot be solved analytically or with efficient algorithms, such as linear programming, in reasonable time or without making strong simplifying assumptions on the problem. Many of these problems are of a combinatorial nature, such as job shop scheduling, timetabling, nurse rostering, and vehicle routing, to name just a few. In practical settings, often the issue of “robustness” of a solution is equally important as “optimality”, because the optimization context is characterized by uncertainty and changing conditions.
The chapter presents an overview of exemplary EA-applications in different problem classes as well as branches of industry. This is complemented with a full application example from workforce management, thus demonstrating the power and versatility of metaheuristic approaches based on EA. The chapter concludes with an estimation of the current state of EA in management applications in a hype cycle notation.
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