We consider the problem of identifying the source location of a contaminant via analyzing changes in concentration levels observed by a sensor network in a river system. To address this problem, we propose a framework including two main steps: (i) pre-processing data; and (ii) training and testing a classification model. Specifically, we first obtain a data set presenting concentration levels of a contaminant from a simulation model, and extract numerical characteristics from the data set. Then, random forest models are generated and assessed to identify the source location of a contaminant. By using the numerical characteristics from the prior step as their inputs, the models provide outputs representing the possibility, i.e., a value between 0 and 1, of a spill event at each candidate location. The performance of the framework is tested on a part of the Altamaha river system in the state of Georgia, United States of America.

We consider the problem of finding a set of feasible or near-feasible systems among a finite number of simulated systems in the presence of stochastic constraints. When the constraints are subjective, a decision maker may want to test multiple threshold values for the constraints. Or the decision maker may simply want to determine how a set of feasible systems changes as constraints become more strict with the objective of pruning systems or finding the system with the best performance. When only the constraint thresholds change for the same set of underlying systems, it is natural to reuse observations collected from the feasibility check with a different threshold value. We present an indifference-zone procedure that recycles observations and provide an overall probability of correct decision for all threshold values. Our numerical experiments show that the proposed procedure performs well in reducing the required number of observations while providing a statistical guarantee on the probability of correct decision.Summary of Contribution: We consider the problem of determining the feasibility of a finite number of systems in the presence of subjective constraints on performance measures that can be estimated only by stochastic simulation. Specifically, our work focuses on the situation where the decision maker is willing to relax some constraint thresholds if necessary to achieve feasibility. Also, we discuss how our proposed procedures can help select the best system in the presence of multiple objectives. This is the first work that considers subjective constraints in the field of ranking and selection in simulation and it provides a practically useful decision-making tool with more flexibility on feasibility determination.History: Accepted by Bruno Tuffin, Area Editor for Simulation.Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.1227 .

Estimating a batch of parameter vectors of a nonlinear model is considered, where there exists a model interpreting the independent and the dependent variables, and the parameter vectors of the model are assumed to be sampled from a multivariate normal distribution. The mean vector and the covariance matrix of the parameter distribution are supposable and such parameter distribution is referred to as the hypothetical underlying distribution. A new framework is proposed, namely, the distribution-guided heuristic search framework, which uses the information of the hypothetical underlying distribution with the following two main concepts: (i) changing the coordinate of the parameter vectors via linear transformation and (ii) probabilistically filtering a parameter vector sampled by a heuristic algorithm. The framework is not a stand-alone algorithm, but it works with any heuristic algorithms to solve the target problem. The framework was tested in two simulation studies and was applied to a real example of measuring the critical dimensions of a 2-dimensional high-aspect-ratio structure of a wafer in semiconductor manufacturing. The test results show that a heuristic algorithm within the proposed framework outperforms the original heuristic algorithm as well as other existing algorithms.