This paper describes several models for enzyme mediated transport across a cell membrane. A standard Michaelis-Menten model (based on empirical descriptions) is compared to a deterministic model (based on the idealization of the plasma membrane as a tri-Iamellar structure) and to a stochastic model (having the additional assumption of compartmentation of the tri-Iamellar membrane). Lineweaver-B...

Project scheduling is the part of project management that deals with determining when intime to start (and finish) which activities and with the allocation of scarce resources to theproject activities. In practice, virtually all project managers are confronted with resourcescarceness. In such cases, the Resource-Constrained Project Scheduling Problem (RCPSP)arises. This optimization problem has...

Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process model, typically via Monte Carlo sampling. Accurate evaluations of expectations or higher-order moments for quantities of interest, e.g., generating cost, can require a prohibitively large number of samples. We propo...

A stochastic version of the Brusselator model is proposed and studied via the system size expansion. The mean-field equations are derived and shown to yield to organized Turing patterns within a specific parameters region. When determining the Turing condition for instability, we pay particular attention to the role of cross diffusive terms, often neglected in the heuristic derivation of reacti...

We show that a number of problems in Artificial Intelligence can be seen as Stochastic Constraint Optimization Problems (SCOPs): problems that have both a stochastic and a constraint optimization component. We argue that these problems can be modeled in a new language, SC-ProbLog, that combines a generic Probabilistic Logic Programming (PLP) language, ProbLog, with stochastic constraint optimiz...

We combine conditional state density construction with an extension of the Scenario Approach for stochastic Model Predictive Control to nonlinear systems to yield a novel particle-based formulation of stochastic nonlinear output-feedback Model Predictive Control. Conditional densities given noisy measurement data are propagated via the Particle Filter as an approximate implementation of the Bay...

Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration problem. To make matters worse, it is often hard to measure the accuracy of one’s ML estimates. We present bidirectional Monte Carlo, a technique for obtaining accurate log-ML estimates on data simulated from a model. This...

We study stochastic programming formulations for the origindestination model in airline seat allocation under uncertainty. In particular, we focus on solving the stability issues of the traditional probabilistic model by purposefully underestimating the demands. The stochastic seat allocation models assume at least the possession of the distributional information, which is usually difficult to ...

The theory of functionally generated portfolios (FGPs) is an aspect of the continuous-time, continuouspath Stochastic Portfolio Theory of Robert Fernholz. FGPs have been formulated to yield a master equation a description of their return relative to a passive (buy-and-hold) benchmark portfolio serving as the numéraire. This description has proven to be analytically very useful, as it is both pa...

Constraint Programming (CP) is a very general programming paradigm that proved its efficiency on solving complex industrial problems. Most real-life problems are stochastic in nature, which is usually taken into account through different compromises, such as applying a deterministic algorithm to the average values of the input, or performing multiple runs of simulation. Our goal in this paper i...