Stochastic simulation requires input probability distributions to model systems with random
dynamic behavior. Given the input distributions, random behavior is simulated using Monte …

Discrete-event simulation models generate random variates from input distributions and
compute outputs according to the simulation logic. The input distributions are typically fitted …

Inventory management is a fundamental challenge in supply chain management. The
challenge is compounded when the associated products have unpredictable demands. This …

This paper introduces an adaptive Bayesian optimization (BayesOpt) framework with
dynamic conditioning and jitter mechanisms. The new framework enhances the adaptability …

A yard template in a container terminal assigns subblocks for containers with the same
departing vessel to reduce vessel turnaround time with the decreased number of container …

This study addresses the difficulties associated with inventory management of products with
stochastic demand. The objective is to find the optimal combination of order quantity and …

This study presents a comprehensive approach to optimizing inventory management under
stochastic demand by leveraging Monte Carlo Simulation (MCS) with grid search and …

Driven by critical challenges in biomanufacturing, including high complexity and high
uncertainty, we propose global optimization methods on the policy-augmented Bayesian …