Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Empirical comparison of search algorithms for discrete event simulation
Computers and Industrial Engineering
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation Optimization is Evolving
INFORMS Journal on Computing
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
How problem-solving really works
International Journal of Data Analysis Techniques and Strategies
International Journal of Data Analysis Techniques and Strategies
A goal model-driven supply chain design
International Journal of Data Analysis Techniques and Strategies
International Journal of Data Analysis Techniques and Strategies
International Journal of Data Analysis Techniques and Strategies
Decision making in an uncertain world: information-gap modeling inwater resources management
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
Public sector decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult-if not impossible-to quantify and capture when supporting decision models need to be constructed. There are invariably unmodelled design issues, not apparent at the time of model creation, which can greatly impact the acceptability of the solutions proposed by the model. Consequently, it is generally preferable to create several quantifiably good alternatives that provide multiple, disparate perspectives and very different approaches to the particular problem. These alternatives should possess near-optimal objective measures with respect to the known modelled objectives, but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization SO modelling can be combined with niching operators to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this modelling-to-generate-alternatives MGA method is specifically demonstrated on a municipal solid waste management facility expansion case.