Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd Easton / Heuristics are often implemented to find better solutions to computationally
challenging problems. Heuristics use varying techniques to search for quality solutions.
Several optimization heuristics have drawn inspiration from real world practices. Ant
colony optimization mimics ants in search of food. Genetic algorithms emulate traits
being passed from a parent to a child. Simulated annealing imitates annealing metal.
This thesis presents a new variable neighborhood search optimization heuristic,
fútbol Strategies applied to Optimize Combinatorial problems to Create Efficient Results,
which is called the SOCCER heuristic. This heuristic mimics fútbol and the closest player
to the ball performs his neighborhood search and players are assigned different
neighborhoods. The SOCCER heuristic is the first application of variable neighborhood
search heuristic that uses a complex structure to select neighborhoods.
The SOCCER heuristic can be applied to a variety of optimization problems. This
research implemented the SOCCER heuristic for job shop scheduling problems. This
implementation focused on creating a quality schedule for a local limestone company.
A small computational study shows that the SOCCER heuristic can quickly solve
complex job shop scheduling problems with most instances finishing in under an half an
hour. The optimized schedules reduced the average production time by 7.27%. This is
roughly a 2 day decrease in the number of days required to produce a month’s worth of
orders. Thus, the SOCCER heuristic is a new optimization tool that can aid companies
and researchers find better solutions to complex problems.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/18963 |
| Date | January 1900 |
| Creators | Kubik, Krista M |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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