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Showing 1 to 17 of 17 for “"sample average approximation"”.
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Data-driven robust solution schemes for sequential decision making
… machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the machine learning literature—often suffer from poor out-of-sample performance when data is limited. To address this issue, the dissertation proposes a data-efficient …
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Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding
… years, Robbins and Monros original stochastic approximation algorithm and its numerous modern incarnations have seen only mixed success in solving SO problems. The primary reason for this is stochastic approximations explicit reliance on a sequence of algorithmic parameters to guarantee …
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Applications of Optimization and Machine Learning in Assortment Optimization and Revenue Management
… the random utility maximization model using the sample average approximation. To improve the practical viability of this approach, the chapter introduces a stronger mixed-integer programming formulation and an accelerated Benders decomposition method that substantially reduces computation times …
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A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty
The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and …
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Stochastic optimization with biased oracles and hidden convexity
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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A spatial stochastic programming model for timber and core area management under risk of stand-replacing fire
… harvesting decisions for each stand based on a sample set of random fire. We integrate multiple model runs to evaluate the persistence of period-one solutions under the influence of stochastic fires. Follow-up simulations were used to support multiple comparisons of different candidate forest …
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MODELS AND OPTIMIZATION FOR ELECTIVE SURGERY SCHEDULING UNDER UNCERTAINTY CONSIDERING PATIENT HEALTH CONDITION
The managerial aspects to run a healthcare system are becoming increasingly important for patient safety. More than one patients are competing each other to be treated using limited medical resources in a healthcare system. The limited medical resources include surgeons, physicians, …
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Data-driven algorithms for operational problems
… through sampling from offline data. We apply the Sample Average Approximation (SAA) method, and establish a polynomial size upper bound on the number of samples needed to achieve a near-optimal expected cost. Nevertheless, the underlying SAA problem is shown to be #P hard. Motivated by the SAA …
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On the Optimization of Edge Server Scaling and Placement for Open Radio Access Network (O-RAN) Slicing
… develop two solution approaches: one based on sample-average approximation and another based on inverse cumulative distribution function (ICDF) transformations. Using realistic wireless testbed measurements, we show that under stable rural channel conditions with low retransmission variability, …
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Successive convex approximation: analysis and applications
… blocks by successively minimizing a sequence of approximations of f which are either locally tight upper bounds of f or strictly convex local approximations of f. Different block selection rules are considered such as cyclic (Gauss-Seidel), greedy (Gauss-Southwell), randomized, or even multiple …
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Inventory Models for Intermittent Highly Variable Demand and Policy Parameter Adjustments to Meet Desired Service Level Requirements
… Then, the models are examined using a large sample of simulated demand conditions. Approximation results of inventory performance measures - ready, rate excepted number of backorders and on-hand inventory levels are reported. Results indicate that distribution selection rules have great …
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Adaptive Sampling Line Search for Simulation Optimization
… of the existing SO algorithms by determining the sample size adaptively as a trade-off between the error due to estimation and the optimization error, that is, the algorithm expends simulation effort proportional to the quality of the incumbent solution. We also show that ADALINE converges …
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New approaches to airline recovery problems
… an online solution procedure based on look-ahead approximation and sample average approximation, which enables the model's implementation in short computational times. Experimental results show that leveraging partial and probabilistic estimates of future disruptions can reduce expected recovery …
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Data-driven optimization and analytics for operations management applications
… information available is a set of independent samples drawn from the demand distribution. We analyze the well-known sample average approximation (SAA) approach, and obtain new tight analytical bounds on the accuracy of the SAA solution. Unlike previous work, these bounds match the empirical …
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Distributionally robust solution schemes for two-stage optimization and interdiction problems under uncertainty
… optimization, we propose a scalable approximation scheme via piecewise linear and piecewise quadratic decision rules. Based on the decision rule structure, we develop a data-driven distributionally robust framework with two layers of robustness to address distributional uncertainty. …
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Toward Microtransit: Design and Operations of Reservation-based Systems
… the formulation to reserve capacity for a single-sample approximation of the dynamic decision-making problem, as opposed to multi-scenario sample average approximation. This tractable approach is accompanied by (i) theoretical results showing that single-sample approximation provides strong …
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Novel Approaches for Some Stochastic and Deterministic Scheduling Problems
… The results show the efficacy of our mixture approximation method. It generates fairly accurate results while requiring significantly less CPU times. The proposed method offers a good compromise between the Monte Carlo method, which requires extensive effort, and use of simple normal …