SURROGATE SEARCH: A SIMULATION OPTIMIZATION METHODOLOGY FOR LARGE-SCALE SYSTEMS

For certain settings in which system performance cannot be evaluated by analytical methods, simulation models are widely utilized. This is especially for complex systems. To try to optimize these models, simulation optimization techniques have been developed. These attempt to identify the system designs and parameters that result in (near) optimal system performance. Although more realistic results can be provided by simulation, the computational time for simulator execution, and consequently, simulation optimization may be very long. Hence, the major challenge in determining improved system designs by incorporating simulation and search methodologies is to develop more efficient simulation optimization heuristics or algorithms. This dissertation develops a new approach, Surrogate Search, to determine near optimal system designs for large-scale simulation problems that contain combinatorial decision variables. First, surrogate objective functions are identified by analyzing simulation results to observe system behavior. Multiple linear regression is utilized to examine simulation results and construct surrogate objective functions. The identified surrogate objective functions, which can be quickly executed, are then utilized as simulator replacements in the search methodologies. For multiple problems containing different settings of the same simulation model, only one surrogate objective function needs to be identified. The development of surrogate objective functions benefits the optimization process by reducing the number of simulation iterations. Surrogate Search approaches are developed for two combinatorial problems, operator assignment and task sequencing, using a large-scale sortation system simulation model. The experimental results demonstrate that Surrogate Search can be applied to such large-scale simulation problems and outperform recognized simulation optimization methodology, Scatter Search (SS). This dissertation provides a systematic methodology to perform simulation optimization for complex operations research problems and contributes to the simulation optimization field

Abordagem sistemática para avaliação econômica de cenários para modelos de simulação discreta em manufatura.

O interesse pelo uso combinado da simulação a eventos discretos com o custeio baseado em atividades, planejamento de experimentos e valor presente líquido para auxiliar a tomada de decisões em sistemas de manufatura tem crescido ao longo dos últimos anos. Entretanto, tradicionalmente a combinação de duas ou três delas pode ser encontrada na literatura e não o emprego de todas elas ao mesmo tempo. Sendo assim, o objetivo desta dissertação é propor uma abordagem que integre estas quatro técnicas para avaliar economicamente cenários em simulação a eventos discretos, para sistemas de manufatura. Para tanto, o método de pesquisa “modelagem e simulação” foi escolhido para conduzir esta dissertação. Em seguida, foi realizada uma análise da literatura corrente sobre o uso combinado dessas técnicas. A partir desta análise, uma abordagem que integra as quatro técnicas foi construída. Então, um modelo de simulação foi desenvolvido para imitar o comportamento de uma célula de manufatura real. Em seguida, este modelo foi preparado para contemplar custos através do sistema de custeio baseado em atividades. Utilizando o modelo, cenários foram simulados através da aplicação do planejamento e análise de experimentos. Em seguida, os cenários que mais afetam a produção da célula foram analisados sob o ponto de vista econômico, utilizando o método do valor presente líquido e a simulação de Monte Carlo. Os resultados da aplicação desta abordagem sistemática sugerem a viabilidade de se utilizar estas quatro técnicas de modo integrado. Como implicações práticas, a abordagem pode ser utilizada para direcionar o tomador de decisões em como cada técnica pode contribuir dentro da estrutura e quais as informações podem ser geradas a partir da utilização de cada técnica. Portanto, a contribuição desta pesquisa consiste na combinação entre a simulação a eventos discretos, custeio baseado em atividades, planejamento de experimentos e valor presente líquido em uma abordagem para auxiliar o processo de tomada de decisões, relacionando opções de decisões estratégicas de investimentos de capital com a gestão do desempenho operacional dessas opções simuladas

Decision-maker Trade-offs In Multiple Response Surface Optimization

The focus of this dissertation is on improving decision-maker trade-offs and the development of a new constrained methodology for multiple response surface optimization. There are three key components of the research: development of the necessary conditions and assumptions associated with constrained multiple response surface optimization methodologies; development of a new constrained multiple response surface methodology; and demonstration of the new method. The necessary conditions for and assumptions associated with constrained multiple response surface optimization methods were identified and found to be less restrictive than requirements previously described in the literature. The conditions and assumptions required for a constrained method to find the most preferred non-dominated solution are to generate non-dominated solutions and to generate solutions consistent with decision-maker preferences among the response objectives. Additionally, if a Lagrangian constrained method is used, the preservation of convexity is required in order to be able to generate all non-dominated solutions. The conditions required for constrained methods are significantly fewer than those required for combined methods. Most of the existing constrained methodologies do not incorporate any provision for a decision-maker to explicitly determine the relative importance of the multiple objectives. Research into the larger area of multi-criteria decision-making identified the interactive surrogate worth trade-off algorithm as a potential methodology that would provide that capability in multiple response surface optimization problems. The ISWT algorithm uses an ε-constraint formulation to guarantee a non-dominated solution, and then interacts with the decision-maker after each iteration to determine the preference of the decision-maker in trading-off the value of the primary response for an increase in value of a secondary response. The current research modified the ISWT algorithm to develop a new constrained multiple response surface methodology that explicitly accounts for decision-maker preferences. The new Modified ISWT (MISWT) method maintains the essence of the original method while taking advantage of the specific properties of multiple response surface problems to simplify the application of the method. The MISWT is an accessible computer-based implementation of the ISWT. Five test problems from the multiple response surface optimization literature were used to demonstrate the new methodology. It was shown that this methodology can handle a variety of types and numbers of responses and independent variables. Furthermore, it was demonstrated that the methodology can be successful using a priori information from the decision-maker about bounds or targets or can use the extreme values obtained from the region of operability. In all cases, the methodology explicitly considered decision-maker preferences and provided non-dominated solutions. The contribution of this method is the removal of implicit assumptions and includes the decision-maker in explicit trade-offs among multiple objectives or responses