Uncertainties are widespread in the optimization of process systems, such as uncertainties technologies, prices, and customer demands. In this paper, we review basic concepts recent advances a risk-neutral mathematical framework called “stochastic programming” its applications solving systems engineering problems under uncertainty. This intends to provide both tutorial for beginners without pri...

A robot task planner must be able to tolerate uncertainty in the durations of commanded actions and uncertainty in the time of occurrence of exogenous events. Sophisticated temporal reasoning techniques have been proposed to deal with such issues, although few existing planners support them. In this paper, we demonstrate the capabilities of a much simpler technique, hindsight optimization, in w...

Optimization under uncertainty has been an active area of research for many years. However, its application in Process Systems Engineering has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endoge...

We define the robustness of a sequential plan as the probability that it will execute successfully despite uncertainty in the execution environment. We consider a rich notion of uncertainty over continuous domains that includes stochastic action effects, and changes to state variables due to unpredictable exogenous events. Given a characterization of this uncertainty in terms of probability dis...

Supply chain is an organized combination of inbound logistics, production plants and multiechelon distribution network. Decentralized distribution networks are common and prone to exogenous and endogenous uncertainties. Information uncertainty from the downstream customer and material uncertainty from the upstream supplier makes the supply chain behavior more complex. Previous attempts made to ...

In this work, we consider robust predictive control of switched uncertain nonlinear systems required to satisfy a prescribed switching sequence with uncertainty in the switching times subject to state and input constraints. To illustrate our approach, we consider first the problem of satisfying a prescribed schedule subject to uncertainty only in the switching times. Predictive controllers that...

Information Arrival and Real Investment We analyze the investment decision of the rm under uncertainty. The objective of the paper is to create a generic framework to investigate the respective roles of irreversible or sunk costs of investment and the nature of the resolution of uncertainty in determining the behavior of the hurdle rate. We show analytically that the mode of information arrival...

Macroeconomic uncertainty regularly fluctuates in the data. Theory suggests complementarity between capital and labor inputs production can generate time-varying endogenous because concavity function influences how output responds to productivity shocks different states of economy. This paper examines whether is a quantitatively significant source by estimating nonlinear real business cycle mod...

This paper studies optimal monetary policy when a central bank faces uncertainty about the production technology. Our model contains nominal wage and price rigidity and is sufficiently rich to capture the empirical response of aggregate variables to a monetary shock. In this economy, introducing uncertainty of the form developed in Hansen and Sargent (2003) attenuates the central bank’s optimal...

The sociological and demographic literatures have widely demonstrated that fertility decisions are shaped by individuals’ previous life experiences socioeconomic status – i.e., the “shadow of past.” However, rising uncertainty in contemporary societies necessitates an analytical framework acknowledges influence future decision-making process. Based on Narrative Framework, we argue perso...