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Predicting Shortages in a Supply Chain Using Simulation A supply chain shortage is a serious problem that can lead to assembly plant shutdowns. However, predicting such shortages using simulation poses a challenge, because the information necessary to initialize such a supply chain simulation is often only partially observable in many real-world applications. In “Simulation-Based Prediction,” Lim and Glynn investigate this problem. They formulate such a prediction problem as the problem of computing the conditional expectation of the quantity of interest, given the observed state of the system. Simulation can be easily applied to computing such a conditional expectation when the simulation state is fully observed in the real system. Lim and Glynn propose a new simulation methodology appropriate to the many settings in which the observed current state does not fully determine the simulation’s initial state. With the use of such methods, simulation has the potential to more accurately predict upcoming supply chain bottlenecks and to enhance predictions in the many other problem settings where simulation is commonly used.

We consider minimizing $f(x) = \mathbb{E}[f(x,\omega)]$ when $f(x,\omega)$ is possibly nonsmooth and either strongly convex or convex in $x$. (I) Strongly convex. When $f(x,\omega)$ is $\mu-$strongly convex in $x$, we propose a variable sample-size accelerated proximal scheme (VS-APM) and apply it on $f_{\eta}(x)$, the ($\eta$-)Moreau smoothed variant of $\mathbb{E}[f(x,\omega)]$; we term such a scheme as (m-VS-APM). We consider three settings. (a) Bounded domains. In this setting, VS-APM displays linear convergence in inexact gradient steps, each of which requires utilizing an inner (SSG) scheme. Specifically, mVS-APM achieves an optimal oracle complexity in SSG steps; (b) Unbounded domains. In this regime, under a weaker assumption of suitable state-dependent bounds on subgradients, an unaccelerated variant mVS-PM is linearly convergent; (c) Smooth ill-conditioned $f$. When $f$ is $L$-smooth and $\kappa = L/\mu \ggg 1$, we employ mVS-APM where increasingly accurate gradients $\nabla_x f_{\eta}(x)$ are obtained by VS-APM. Notably, mVS-APM displays linear convergence and near-optimal complexity in inner proximal evaluations (upto a log factor) compared to VS-APM. But, unlike a direct application of VS-APM, this scheme is characterized by larger steplengths and better empirical behavior; (II) Convex. When $f(x,\omega)$ is merely convex but smoothable, by suitable choices of the smoothing, steplength, and batch-size sequences, smoothed VS-APM (or sVS-APM) produces sequences for which expected sub-optimality diminishes at the rate of $\mathcal{O}(1/k)$ with an optimal oracle complexity of $\mathcal{O}(1/\epsilon^2)$. Finally, sVS-APM and VS-APM produce sequences that converge almost surely to a solution of the original problem., Comment: Stochastic Systems (2022)

Climate change is radically altering coral reef ecosystems, mainly through increasingly frequent and severe bleaching events. Yet, some reefs have exhibited higher thermal tolerance after bleaching severely the first time. To understand changes in thermal tolerance in the eastern tropical Pacific (ETP), we compiled four decades of temperature, coral cover, coral bleaching, and mortality data, including three mass bleaching events during the 1982 to 1983, 1997 to 1998 and 2015 to 2016 El Niño heatwaves. Higher heat resistance in later bleaching events was detected in the dominant framework-building genus, Pocillopora, while other coral taxa exhibited similar susceptibility across events. Genetic analyses of Pocillopora spp . colonies and their algal symbionts (2014 to 2016) revealed that one of two Pocillopora lineages present in the region ( Pocillopora “ type 1”) increased its association with thermotolerant algal symbionts ( Durusdinium glynnii ) during the 2015 to 2016 heat stress event. This lineage experienced lower bleaching and mortality compared with Pocillopora “type 3”, which did not acquire D. glynnii . Under projected thermal stress, ETP reefs may be able to preserve high coral cover through the 2060s or later, mainly composed of Pocillopora colonies that associate with D. glynnii . However, although the low-diversity, high-cover reefs of the ETP could illustrate a potential functional state for some future reefs, this state may only be temporary unless global greenhouse gas emissions and resultant global warming are curtailed.

We consider a family of Markov chains whose transition dynamics are affected by model parameters. Understanding the parametric dependence of (complex) performance measures of such Markov chains is often of significant interest. The derivatives and their continuity of the performance measures w.r.t. the parameters play important roles, for example, in numerical optimization of the performance measures, and quantification of the uncertainties in the performance measures when there are uncertainties in the parameters from the statistical estimation procedures. In this paper, we establish conditions that guarantee the smoothness of various types of intractable performance measures—such as the stationary and random horizon discounted performance measures—of general state space Markov chains and provide probabilistic representations for the derivatives. Funding: C.-H. Rhee is supported by the National Science Foundation [Grant CMMI-2146530].

In many operations management settings, the arrival process to the system exhibits clear nonstationarities. These nonstationarities may arise as a consequence of time-of-day effects, day-of-week effects, seasonalities, or stochastic fluctuations in the arrival rate. Tools that are developed for performance prediction and decision making when systems are stationary may not be valid in the nonstationary environment. This paper provides a generic, closed-form, and simple approximation tool for when the nonstationarities are caused by rapid fluctuations in the arrival processes.