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On the risk-averse selection of resilient multi-tier supply portfolio

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2021年 101卷 102267-102267页

作者： Sawik, Tadeusz AGH Univ Sci & Technol Dept Operat Res PL-30059 Krakow Poland Reykjavik Univ Dept Engn IS-101 Reykjavik Iceland

A multi-portfolio approach and a scenario-based stochastic mixed integer program are developed for risk averse selection of resilient supply and demand portfolios in a geographically dispersed multi-tier supply chain network under disruption risks. The resilience of the supply chain is improved by selection of primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously for each disruption scenario, recovery and transshipment portfolios are determined and decisions on usage the pre-positioned inventory are made to minimize conditional cost-at-risk or maximize conditional service-at-risk. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. In particular, the impact of unit penalty for unfulfilled demand for products on resilience of the risk-averse supply portfolio is investigated. The findings also indicate that the developed multi-portfolio approach forms an embedded network flow structure that leads to computationally efficient stochastic mixed integer program with a very strong LP relaxation. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Multi-tier supply chain Multi-portfolio approach Disruption management stochastic mixed integer programming

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Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine 1

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13th International Conference on Learning and Intelligent Optimization (LION)

作者： Runarsson, Thomas Philip Univ Iceland Sch Engn & Nat Sci Reykjavik Iceland

ISBN: (数字)9783030386290

ISBN: (纸本)9783030386290;9783030386283

The problem of generating surgery schedules is formulated as a mathematical model with probabilistic constraints. The approach presented is a new method for tackling probabilistic constraints using machine learning. The technique is inspired by models that use slacks in capacity planning. Essentially support vector classification is used to learn a linear constraint that will replace the probabilistic constraint. The data used to learn this constraint is labeled using Monte Carlo simulations. This data is iteratively discovered, during the optimization procedure, and augmented to the training set. The linear support vector classifier is then updated during search, until a feasible solution is discovered. The stochastic surgery model presented is inspired by real challenges faced by many hospitals today and tested on real-life data.

关键词： Support vector classification Surgery scheduling stochastic mixed integer programming Monte Carlo simulations

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Coordinated Optimization of EVs and Multiple Buildings with Renewable Energy 16

Coordinated Optimization of EVs and Multiple Buildings with ...

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16th IEEE International Conference on Automation Science and Engineering (CASE)

作者： Liu, Fengxia Xu, Zhanbo Liu, Kun Wu, Jiang Guan, Xiaohong Xi An Jiao Tong Univ MOE KLINNS Lab Xian 710049 Shaanxi Peoples R China Tsinghua Univ Ctr Intelligent & Networked Syst CFINS Dept Automat TNLIST Beijing 100084 Peoples R China

ISBN: (纸本)9781728169040

This paper focuses on the jointly optimization of a micro-grid comprising electric vehicles (EVs) and buildings equipped with photovoltaic (PV) on the roof. The optimization problem of EVs and multiple buildings with renewable energy is formulated as a stochastic mixed integer linear programming (MILP) problem, in which bidirectional integration of EVs, building's demand response (DR) flexibility caused by outside air temperature (OAT) and thermostat setpoint temperature adjustment, and the uncertainty in PV power generation are fully taking into account. Scenario based method is adopted to deal with the randomness of PV generation and stochastic capacity requirements of EVs. The performance of jointly optimization is discussed based on case studies. Numerical results show that the aggregated EVs modeled as virtual storage system could not only significantly reduce the system cost and add building DR flexibility, but also could compensate the intermittency of renewable energy generation through peak shaving and valley filling

关键词： Electric vehicles (EVs) stochastic mixed integer programming system optimization

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Disruption mitigation and recovery in supply chains using portfolio approach

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2019年 84卷 232-248页

作者： Sawik, Tadeusz AGH Univ Sci & Technol Dept Operat Res Al Mickiewicza 30 PL-30059 Krakow Poland

An innovative portfolio approach and stochastic MIP formulations with an embedded network flow problem are developed for selection of primary and recovery suppliers and assembly plants in the presence of supply chain disruption risks. Local and regional multi-level disruptions of suppliers and assembly plants are considered. Unlike most of reported research on supply chain disruption management a disruptive event is assumed to impact both a primary supplier of parts and the buyer's firm primary assembly plant. Then the firm may choose alternate (recovery) suppliers and move production to alternate (recovery) plants along with transshipment of parts from the impacted primary plant to the recovery plants. The resulting allocation of unfulfilled demand for parts among recovery suppliers and unfulfilled demand for products among recovery assembly plants determines recovery supply and demand portfolio, respectively. The selection of supply and demand portfolios is determined simultaneously with production scheduling in assembly plants. An integrated decision-making approach with the perfect information about the potential future disruption scenarios is compared with a hierarchical approach with no such information available ahead of time. In the integrated approach a two-stage stochastic model is applied, in which the first stage decision considers disruption scenarios to happen in the second stage so that the impact of disruption risks is mitigated. The second stage decision optimizes the supply chain recovery process. The scenario analysis indicates that for the hierarchical approach the best-case and worst-case disruption scenarios are, respectively subsets and supersets of the corresponding scenarios for the integrated approach. In addition to risk-neutral decision-making based on expected cost or expected service level optimization, an integrated risk-averse approach is developed using CVaR risk measure. The findings indicate that the developed portfolio approach le

关键词： Supply portfolio Demand portfolio Disruption and recovery Supply chain risk management stochastic mixed integer programming

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Pan-European CVaR-Constrained stochastic Unit Commitment in Day-Ahead and Intraday Electricity Markets

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ENERGIES 2020年第9期13卷 2339页

作者： Nobis, Moritz Schmitt, Carlo Schemm, Ralf Schnettler, Armin Rhein Westfal TH Aachen Inst High Voltage Equipment & Grids Digitalizat & D-52056 Aachen Germany Aachen Univ Appl Sci Energy Technol D-52428 Julich Germany Siemens AG D-80333 Munich Germany Schinkelstr 6 D-52062 Aachen Germany

The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023-considering a full nuclear phase-out in Germany-and 2035-considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources.

关键词： benders decomposition conditional value at risk forecasting errors intraday markets Lagrangian relaxation stochastic mixed integer programming unit commitment

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Approximating Probabilistic Constraints for Surgery Scheduling Using Neural Networks 5th

Approximating Probabilistic Constraints for Surgery Scheduli...

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5th International Conference on Machine Learning, Optimization, and Data Science (LOD)

作者： Runarsson, Thomas Philip Univ Iceland Sch Engn & Nat Sci Reykjavik Iceland

ISBN: (纸本)9783030375997;9783030375980

The problem of generating surgery schedules is formulated as a mathematical model with probabilistic constraints. The approach presented uses modern machine learning to approximate the model's probabilistic constraints. The technique is inspired by models that use slacks in capacity planning. Essentially a neural-network is used to learn linear constraints that will replace the probabilistic constraint. The data used to learn these constraints is verified and labeled using Monte Carlo simulations. The solutions iteratively discovered, during the optimization procedure, produce also new training data. The neural-network continues its training on this data until the solution discovered is verified to be feasible. The stochastic surgery model studied is inspired by real challenges faced by many hospitals today and tested on real-life data.

关键词： Neural networks Surgery scheduling stochastic mixed integer programming Monte Carlo simulations

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Load and Wind Power Scenario Generation Through the Generalized Dynamic Factor Model

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第1期32卷 400-410页

作者： Lee, Duehee Baldick, Ross KPMG LLP Dallas TX 75201 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

Load and wind power scenarios are synthesized through the generalized dynamic factor model (GDFM), which represents the load and wind power as the sum of a periodic component, idiosyncratic noise component, and common component, where the GDFM preserves the correlation structure between load and wind. The common component consists of the dynamic shock, which is white noise, and the matrix polynomial, which represents the temporal and geographical correlation between load and wind power. Since the dimension of dynamic shocks is less than that of actual load and wind power, the GDFM requires fewer dimensions and variables than multivariate time series models. Scenarios are verified through statistical, spectral density, and correlation analysis. The usefulness of scenarios is also verified by calculating the total generation and transmission upgrade costs on the IEEE 300-bus benchmark. Using correlated scenarios results in higher generation and upgrade costs than using uncorrelated or weakly correlated scenarios. Therefore, correlated scenarios should be used in order to more accurately estimate power system planning costs.

关键词： Generalized dynamic factor model load and wind power scenario stochastic mixed integer programming

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Integrated supply, production and distribution scheduling under disruption risks

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2016年 62卷 131-144页

作者： Sawik, Tadeusz AGH Univ Sci & Technol Dept Operat Res & Informat Technol Al Mickiewicza 30 PL-30059 Krakow Poland

This paper presents a bi-objective stochastic mixed integer programming approach for a joint selection of suppliers and scheduling of production and distribution in a multi-echelon supply chain subject to local and regional disruption risks. Two conflicting problem objectives are minimization of cost and maximization of service level. The three shipping methods are considered for distribution of products: batch shipping with a single shipment of different customer orders, batch shipping with multiple shipments of different customer orders and individual shipping of each customer order immediately after its completion. The stochastic combinatorial optimization problem is formulated as a time-indexed mixed integer program with the weighted-sum aggregation of the two objective functions. The supply portfolio is determined by binary selection and fractional allocation variables while time-indexed assignment variables determine the production and distribution schedules. The problem formulation incorporates supply-production, production-distribution and supply-distribution coordinating constraints to efficiently coordinate supply, production and distribution schedules. Numerical examples modelled after an electronics supply chain and computational results are presented and some managerial insights are reported. The findings indicate that for all shipping methods, the service-oriented supply portfolio is more diversified than the cost-oriented portfolio and the more cost-oriented decision-making, the more delayed the expected supply, production and distribution schedules. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Supply chain risk management stochastic mixed integer programming Integrated scheduling Supply disruptions

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On the risk-averse optimization of service level in a supply chain under disruption risks

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第1期54卷 98-113页

作者： Sawik, Tadeusz AGH Univ Sci & Technol Dept Operat Res & Informat Technol PL-30059 Krakow Poland

The worst-case optimization of service level in the presence of supply chain disruption risks is considered for the two different service levels measures: the expected worst-case demand fulfillment rate and the expected worst-case order fulfillment rate. The optimization problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions of supplies. The suppliers are located in different geographic regions and the supplies are subject to random local and regional disruptions. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional service-at-risk as a worst-case service level measure. The risk-averse solutions that optimize the worst-case performance of a supply chain are compared for the two service level measures. In addition, to demonstrate the impact on the cost in the process of optimizing the worst-case service level, a joint optimization of expected cost and conditional service-at-risk using a weighted-sum approach is considered and illustrated with numerical examples. The findings indicate that the worst-case order fulfillment rate shows a higher service performance than the worst-case demand fulfillment rate. Maximization of the expected worst-case fraction of fulfilled customer orders better mitigates the impact of disruption risks. The supply portfolio is more diversified and the expected worst-case fraction of fulfilled orders is greater for most confidence levels. Finally, the results clearly show that worst-case service level is in opposition to cost.

关键词： supply chain risk management stochastic mixed integer programming worst-case service level supplier selection stochastic scheduling

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BI-OBJECTIVE OPTIMIZATION OF GRID-CONNECTED DECENTRALIZED ENERGY SYSTEMS

BI-OBJECTIVE OPTIMIZATION OF GRID-CONNECTED DECENTRALIZED EN...

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作者： Onur Altintas Bilkent University

学位级别：硕士

We present a bi-objective two stage stochastic programming model for optimal sizing of a grid-connected hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. National grid is assumed to be a carbon-intense alternative to renewables and used as a backup source to ensure reliability. Storage device is included to examine its role in reducing the carbon emission and the intermittency of renewable sources. It is assumed that decision maker is sensitive to both cost and carbon emission, therefore two objectives are considered: total cost and carbon emission caused by electricity purchased from the utility grid. A simulation optimization algorithm has been developed for the problem.

关键词： Multi-objective Optimization Simulation Renewable Energy Systems CO2 emission stochastic mixed integer programming

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