Robust Design of RF-MEMS Cantilever Switches Using Contact Physics Modeling

This paper presents the robust design optimization of an RF-MEMS direct contact cantilever switch for minimum actuation voltage and opening time, and maximum power handling capability. The design variables are the length and thickness of the entire cantilever, the widths of the sections of the cantilever, and the dimple size. The actuation voltage is obtained using a 3-D structural-electrostatic finite-element method (FEM) model, and the opening time is obtained using the same FEM model and the experimental model of adhesion at the contact surfaces developed in our previous work. The model accounts for an unpredictable variance in the contact resistance resulting from the micromachining process for the estimation of the power handling. This is achieved by taking the ratio of the root mean square power of the RF current (signal") passing through the switch to the contact temperature ("noise") resulting from the possible range of the contact resistance. The resulting robust optimization problem is solved using a Strength Pareto Evolutionary Algorithm, to obtain design alternatives exhibiting different tradeoffs among the three objectives. The results show that there exists substantial room for improved designs of RF-MEMS direct-contact switches. It also provides a better understanding of the key factors contributing to the performances of RF-MEMS switches. Most importantly, it provides guidance for further improvements of RF-MEMS switches that exploit complex multiphysics phenomena.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87274/4/Saitou7.pd

Meta heuristic for Minimizing Makespan in a Flow-line Manufacturing Cell with Sequence Dependent Family Setup Times

This paper presents a new mathematical model for the problem of scheduling part families and jobs within each part family in a flow line manufacturing cell where the setup times for each family are sequence dependent and it is desired to minimize the maximum completion time of the last job on the last machine (makespan) while processing parts (jobs) in each family together. Gaining an optimal solution for this type of complex problem in large sizes in reasonable computational time using traditional approaches or optimization tools is extremely difficult. A meta-heuristic method based on Simulated Annealing (SA) is proposed to solve the presented model. Based on the computational analyses, the proposed algorithm was found efficient and effective at finding good quality solutions

Adjustable Robust Parameter Design with Unknown Distributions

Abstract This article presents a novel combination of robust optimization developed in mathematical programming, and robust parameter design developed in statistical quality control. Robust parameter design uses metamodels estimated from experiments with both controllable and environmental inputs (factors). These experiments may be performed with either real or simulated systems; we focus on simulation experiments. For the environmental inputs, classic robust parameter design assumes known means and covariances, and sometimes even a known distribution. We, however, develop a robust optimization approach that uses only experimental data, so it does not need these classic assumptions. Moreover, we develop `adjustable' robust parameter design which adjusts the values of some or all of the controllable factors after observing the values of some or all of the environmental inputs. We also propose a new decision rule that is suitable for adjustable integer decision variables. We illustrate our novel method through several numerical examples, which demonstrate its effectiveness.

Improvement of Work Process Performance with Task Assignments and Mental Workload Balancing

The outcome of a work process depends heavily on which tasks assigned to which employees. However, sometimes-optimized assignments based on employees’ qualifications may result in an uneven and ineffective workload distribution among them. Likewise, an even workload distribution without considering the employee\u27s qualifications may cause unproductive employee-task matching that results in low performance of employees. This trade-off is even more noticeable for work processes during critical time junctions, such as in military command centers and emergency rooms that require being fast and effective without making errors. This study proposes that optimizing task-employee assignments according to their capabilities while also keeping them under a workload threshold, results in better performance for work processes, especially during critical time junctions. The goal is to select the employee-task assignments in order to minimize the average duration of a work process while keeping the employees under a workload threshold to prevent errors caused by overload. Due to uncertainties inherent in the problem related with the inter-arrival time of work orders, task durations and employees\u27 instantaneous workload, a utilized simulation-optimization approach solves this problem. More specifically, a discrete event human performance simulation model evaluates the objective function of the problem coupled with a genetic algorithm based meta-heuristic optimization approach to search the solution space. This approach proved to be useful in determining the right task-agent assignments by taking into consideration the employees\u27 qualifications and mental workload in order to minimize the average duration of a work process. Use of a sample work process shows the effectiveness of the developed simulation-optimization approach. Numerical tests indicate that the proposed approach finds better solutions than common practices and other simulation-optimization methods. Accordingly, by using this method, organizations can increase performance, manage excess-level workloads, and generate higher satisfactory environments for employees, without modifying the structure of the process itself