Online Risk Monitoring Using Offline Simulation

Jiang, Guangxin; Li, Hong; Nelson, Barry L.

doi:10.1287/ijoc.2019.0892

Cited by 17 publications

(6 citation statements)

References 39 publications

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“…Using offline results directly is a promising way to respond to environmental changes under tight computation budgets. Lowrey et al (2018), Jiang (2019), andJiang et al (2020) explain this idea. A simple illustration of this principle in inventory management is the use of radiofrequency identification technology to collect real-time data and inventory policies predefined at the retail store (Bottani et al, 2017).…”

Section: Real-time Supply Chain Optimizationmentioning

confidence: 99%

Digital twin-driven smart supply chain

et al. 2022

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Today’s supply chain is becoming complex and fragile. Hence, supply chain managers need to create and unlock the value of the smart supply chain. A smart supply chain requires connectivity, visibility, and agility, and it needs be integrated and intelligent. The digital twin (DT) concept satisfies these requirements. Therefore, we propose creating a DT-driven supply chain (DTSC) as an innovative and integrated solution for the smart supply chain. We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept. We discuss three research opportunities in building a DTSC, including supply chain modeling, real-time supply chain optimization, and data usage in supply chain collaboration. Finally, we highlight a motivating case from JD.COM, China’s largest retailer by revenue, in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.

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Section: Real-time Supply Chain Optimizationmentioning

confidence: 99%

Digital twin-driven smart supply chain

et al. 2022

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“…A number of papers have emerged in which simulation sample paths are stored and used to build metamodels for making dynamic predictions. In the context of a queueing network, Ouyang and Nelson (2017) proposed a two-stage logistic regression modelling approach in which the state and time aspects of the sample path are treated separately, while Jiang et al (2020) use a logistic regression model to dynamically predict the risk of financial portfolios. Wu and Barton (2016), meanwhile, show that Fourier Laidler, Morgan, Nelson, and Pavlidis analysis can successfully detect changes in the dynamic trajectories of system state variables to discriminate between congested and uncongested systems.…”

Section: Related Work In Simulation Analyticsmentioning

confidence: 99%

Metric Learning for Simulation Analytics

Laidler

Morgan

Nelson

et al. 2020

2020 Winter Simulation Conference (WSC)

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The sample path generated by a stochastic simulation often exhibits significant variability within each replication, revealing periods of good and poor performance alike. As such, traditional summaries of aggregate performance measures overlook the more fine-grained insights into the operational system behavior. In this paper, we take a simulation analytics view of output analysis, turning to machine learning methods to uncover key insights from the dynamic sample path. We present a k nearest neighbors model on system state information to facilitate real-time predictions of a stochastic performance measure. This model is built on the premise of a system-specific measure of similarity between observations of the state, which we inform via metric learning. An evaluation of our approach is provided on a stochastic activity network and a wafer fabrication facility, both of which give us confidence in the ability of metric learning to provide interpretation and improved predictive performance.

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“…Financial agents constantly deal with the trade‐off between walking through a riskier path to obtain high returns or getting less risk, being satisfied with the assurance of lower returns. The uncertainty and ambiguity in data (Lotfi and Zenios, 2018), stock price prediction (Araujo et al., 2018, 2019; Dutta et al., 2018), the adequacy of the model constraints, and the criteria considered to reflect market regulations and investor's preferences (de Lima Silva et al., 2018; Ferreira et al., 2018; de Almeida‐Filho et al., 2020; Silva et al., 2021; Silva and de Almeida‐Filho, 2021), and risk monitoring (Sant'Anna et al., 2019; Jiang et al., 2020) represent some of the challenges in financial portfolio problems.…”

Section: Introductionmentioning

confidence: 99%

An enhanced GRASP approach for the index tracking problem

Silva

Filho

2022

Int Trans Operational Res

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Index tracking models build portfolios of limited size that replicate the performance of a market index. As the size of the index grows, it becomes impractical to find an optimal solution. As far as we know, this work proposes the first greedy randomized adaptive search procedure (GRASP) approach for index tracking. GRASP has proven to be efficient in combinatorial optimization problems and offers a solution construction procedure different from the standard index tracking optimization approaches, bringing a new perspective to the field. Results showed that the proposed GRASP approach found solutions with almost the same quality as those found by CPLEX solver in a smaller time, and the proposed local search component was competitive depending on the problem parametrization. The results found have practical implications concerning the achievement of good solutions by GRASP approaches in a smaller time when compared with hybrid genetic algorithms, and new perspectives for building GRASP heuristics for portfolio optimization problems.

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Online Risk Monitoring Using Offline Simulation

Cited by 17 publications

References 39 publications

Digital twin-driven smart supply chain

Digital twin-driven smart supply chain

Metric Learning for Simulation Analytics

An enhanced GRASP approach for the index tracking problem

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