In engineering design, an optimized solution often turns out to be suboptimal, when implementation errors are encountered. While the theory of robust convex optimization has taken significant strides over the past decade, all approaches fail if the underlying cost function is not explicitly given; it is even worse if the cost function is nonconvex. In this work, we present a robust optimization...

To help disaster response organizations improve the management of regional emergency assets and operations, we propose to interface an agent-based, discrete event simulator with a geographic information system and a rule base (describing standard policies and protocols for various disaster responses). The system architecture will enable modules to dynamically “talk” with each other by exchangin...

In engineering design, an optimized solution often turns out to be suboptimal, when errors are encountered. While the theory of robust convex optimization has taken significant strides over the past decade, all approaches fail if the underlying cost function is not explicitly given; it is even worse if the cost function is nonconvex. In this work, we present a robust optimization method, which ...

This paper briefly reviews some properties of Monte Carlo simulation and emphasizes the link to evolutionary computation. It shows how this connection can help to study evolutionary algorithms within a unified framework. It also gives some practical examples of implementation inspired from MOSES (the mutation-or-selection evolution strategy).

Optimal control of systems with complex nonlinear behaviour such as steady state multiplicity results in a nonlinear optimization problem that needs to be solved online at each sample time. We present an approach based on simulation, function approximation and evolutionary improvement aimed towards simplifying online optimization. Closed loop data from a suboptimal control law, such as MPC base...

A common problem encountered in engineering practice, management science and systems analysis is to find the values of input variables which optimize some function of system performance. When systems are tab complicated to be described analytically, simulation is the appropriate tool for modeling purposes. Methods of optimization through simulation have recently become a steadily growing discip...

We address a central problem in modeling, namely that of learning an algebraic model from data obtained from simulations or experiments. We propose a methodology that uses a small number of simulations or experiments to learn models that are as accurate and as simple as possible. The approach begins by building a low-complexity surrogate model. The model is built using a best subset technique t...

The sea scallop resource of Georges Bank supports one of the largest commercial fisheries in the United States. The objective of this research was to develop a technique to examine different management strategies for the sea scallop resource of Georges Bank and compare these strategies to the optimal. A simulation model followed the sea scallop population dynamics using information from recent ...

This paper critically examines the use of Analytic Target Cascading as a multi-level, hierarchical design optimization model for formulating simulation-based design tasks in architecture. A case study is used to illustrate the main steps involved in posing and solving an ATC problem. With an emphasis on problem formulation, this study is used as the basis of highlighting issues confronted while...