AutoStat: output statistical analysis for AutoMod users
Proceedings of the 29th conference on Winter simulation
The WITNESS toolbox—a tutorial
Proceedings of the 30th conference on Winter simulation
P-Complete Approximation Problems
Journal of the ACM (JACM)
The Arena product family: enterprise modeling solutions
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Evolutionary computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Simulated annealing heuristics for the dynamic facility layout problem
Computers and Operations Research
A comparative study of genetic algorithm components in simulation-based optimisation
Proceedings of the 40th Conference on Winter Simulation
Metaheuristics: Progress in Complex Systems Optimization
Metaheuristics: Progress in Complex Systems Optimization
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Evolutionary Selection in Simulation-Based Optimization
Computer Aided Systems Theory - EUROCAST 2009
Benefits of plugin-based heuristic optimization software systems
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Proceedings of the Winter Simulation Conference
Nonlinear optimization to generate non-overlapping random dot patterns
Proceedings of the Winter Simulation Conference
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In this paper we describe the optimization of a facility layout scenario, which involves coupling simulation with the optimization environment HeuristicLab. For this purpose we show a problem formulation that acts as an interface between these two domains of problem modeling and optimization, and discuss optimization methodologies and their results for a number of artificial test problems as well as more complex real-world problems. HeuristicLab was designed with both practitioners and algorithm developers in mind. Practitioners benefit from a graphical user interface that facilitates so-called interactive algorithm engineering, where algorithms can be adjusted without actually writing code. Algorithm developers are aided in the development process by the plug-in based, easily extensible architecture and integrated parallelization functionality.