The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents an integrated approach for enhancing the performance of stochastic optimization processes by incorporating techniques from statistical experimental designs, such as response surface methodology. The two-stage process includes an “exploratory” phase, during which a fraction of the finite time budget is reserved for conducting informative measurements to best approximate the stochastic...
System dynamics, which is an approach built on information feedbacks and delays in the model in order to understand the dynamical behavior of a system, has successfully been implemented for supply chain management problems for many years. However, research within in multi-objective optimization of supply chain problems modelled through system dynamics has been scares. Supply chain decision making...
Reliable simulation estimation builds on accurately specified input models. In the context of simulating tail events, knowledge on the tail of the input model is especially important, yet is often hard to obtain due to a lack of data. In this paper, we consider tail event estimation without any knowledge on the input tail, but rather only making a general assumption that it is convex. We focus on...
Stochastic optimization facilitates decision making in uncertain environments. In typical problems, probability distributions are fit to historical data for the chance variables and then optimization is carried out, as if the estimated probability distributions are the “truth”. However, this perspective is optimistic in nature and can frequently lead to sub-optimal or infeasible results because the...
Motivated by our recent extension of the Two-Stage Sequential Algorithm (eTSSO), we propose an adaptation of the framework in Pasupathy et al. (2015) for the study of convergence of kriging-based procedures. Specifically, we extend the proof scheme in Pasupathy et al. (2015) to the class of kriging-based simulation-optimization algorithms. In particular, the asymptotic convergence and the convergence...
This paper investigates empirically two-range robust optimization (2R-RO) as an alternative to stochastic programming in terms of computational time and solution quality. We consider a number of possible projects with anticipated costs and cash flows, and an investment decision to be made under budget limitations. In 2R-RO, each uncertain parameter is allowed to take values from more than one uncertainty...
Performance analysis via stochastic simulation is often subject to input model uncertainty, meaning that the input model is unknown and needs to be inferred from data. Motivated especially from situations with limited data, we consider a worst-case analysis to handle input uncertainty by representing the partially available input information as constraints and solving a worst-case optimization problem...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.