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Condition-based maintenance optimization via stochastic programming with endogenous uncertainty

COMPUTERS & CHEMICAL ENGINEERING 2022年 156卷 107550-107550页

作者： Leo, Egidio Engell, Sebastian Tech Univ Dortmund Dept Biochem & Chem Engn Proc Dynam & Operat Grp EmilFigge Str 70 D-44221 Dortmund Germany

In this work we address the challenge of integrating production planning and maintenance optimiza-tion for a process plant. We consider uncertain predictions of the equipment degradation by adopting a stochastic programming formulation with decision-dependent uncertainty. The probability of the uncer-tain parameters, in this work the remaining useful time of the plant, depends on the operating conditions of the plant which is modeled by embedding a prognosis model, the Cox model, into the optimization problem. A separation of the variables is suggested to decompose the MINLP formulation via two differ-ent primal decomposition algorithms. We provide computational results and compare the performance of the proposed decompositions with the global solver BARON enhanced with a custom branching priority strategy. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Condition-based maintenance Prognosis stochastic programming Endogenous uncertainty Cox model MINLP

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A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2022年 107卷 102542-102542页

作者： Zhou, Rui Bhuiyan, Tanveer Hossain Medal, Hugh R. Sherwin, Michael D. Yang, Dong Donghua Univ Sch Business & Management Dept Informat Management Shanghai Peoples R China Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15260 USA Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA

Poor supplier performance can result in delays that disrupt manufacturing operations. By proactively managing supplier performance, the likelihood and severity of supplier risk can be minimized. In this paper, we study the problem of selecting optimal supplier development programs (SDPs) to improve suppliers' performance with a limited budget to proactively reduce supplier risks for a manufacturer. A key feature of our research is that it incorporates the uncertainty in supplier performance in response to SDPs selection decisions. This uncertainty is endogenous (decision-dependent), as the probability of supplier performance depends on the selection of SDPs, which introduces modeling and algorithmic challenges. We formulate this problem as a two-stage stochastic program with decision-dependent uncertainty. We implement a sample-based greedy algorithm and an accelerated Benders' decomposition method to solve the developed model. We evaluate our methodology using the numerical cases of four low-volume, highvalue manufacturing firms. The results provide insights into the effects of the budget amount and of the number of SDPs on the firm's expected profit. Numerical experiments demonstrate that an increase in budget results in profit growth, e.g., 5.09% profit growth for one firm. At a lower budget level, increasing the number of available SDPs results in more profit growth. The results also demonstrate the significance of considering uncertainty in supplier performance and considering multiple supplier risks for the firm. In addition, computational experiments demonstrate that our algorithms, especially our greedy approximation algorithm, can solve large-sized problems in a reasonable time. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Supplier risk mitigation stochastic programming Supplier development program Benders' decomposition Greedy algorithm

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Data-driven Piecewise Affine Decision Rules for stochastic programming with Covariate Information

arXiv

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arXiv 2023年

作者： Zhang, Yiyang Liu, Junyi Zhao, Xiaobo Department of Industrial Engineering Tsinghua University Beijing100084 China

Focusing on stochastic programming (SP) with covariate information, this paper proposes an empirical risk minimization (ERM) method embedded within a nonconvex piecewise affine decision rule (PADR), which aims to learn the direct mapping from features to optimal decisions. We establish the nonasymptotic consistency result of our PADR-based ERM model for unconstrained problems, which illustrates the role of piece number in balancing the trade-off between the approximation and estimation errors. To solve the nonconvex and nondifferentiable ERM problem, we develop an enhanced stochastic majorization-minimization (ESMM) algorithm and establish the convergence to (composite strong) directional stationarity along with convergence rate analysis. We show that the proposed PADR-based ERM method applies to a broad class of nonconvex SP problems with theoretical consistency guarantees and computational traceability. Our numerical study demonstrates the effectiveness of PADR-based ERM methods with comparisons to state-of-the-art approaches under various settings, with significantly lower costs, less computation time, and robustness to feature dimensions and nonlinearity of the underlying dependency. © 2023, CC BY.

关键词： stochastic programming

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Mobile Network Slicing under Demand Uncertainty: A stochastic programming Approach

arXiv

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arXiv 2023年

作者： Gholami, Anousheh Torkzaban, Nariman Baras, John S. Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD United States

Network slicing enables the deployment of multiple dedicated virtual sub-networks, i.e. slices on a shared physical infrastructure. Unlike traditional one-size-fits-all resource provisioning schemes, each network slice (NS) in 5G is tailored to the specific service requirements of a group of customers. An end-to-end (E2E) mobile NS orchestration requires the simultaneous provisioning of computing, storage, and networking resources across the core network (CN) and the radio access network (RAN). Constant temporospatial changes in mobile user demand profiles further complicate the E2E NSs resource provisioning beyond the limits of the existing best-effort schemes that are only effective under accurate demand forecasts for all slices. This paper proposes a practical two-time-scale resource provisioning framework for E2E network slicing under demand uncertainty. At each macro-scale instance, we assume that only the spatial probability distribution of the NS demands is available. We formulate the NSs resource allocation problem as a stochastic mixed integer program (SMIP) with the objective of minimizing the total resource cost at the CN and the RAN. At each microscale instance, utilizing the exact slice demand profiles, a linear program is solved to jointly minimize the unsupported traffic and the resource cost at the RAN. We verify the effectiveness of our resource allocation scheme through numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： stochastic programming

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Controlling Microgrids Without External Data: A Benchmark of stochastic programming Methods

arXiv

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arXiv 2023年

作者： Puech, Alban Rigaut, Tristan Le Franc, Adrien Templier, William Alais, Jean-Christophe Tournoud, Maud Bossard, Victor Yousef, Alejandro Stolyarova, Elena Schneider Digital - Ai Hub Schneider Electric Laas Cnrs France

Microgrids are local energy systems that integrate energy production, demand, and storage units. They are generally connected to the regional grid to import electricity when local production and storage do not meet the demand. In this context, Energy Management Systems (EMS) are used to ensure the balance between supply and demand, while minimizing the electricity bill, or an environmental criterion. The main implementation challenges for an EMS come from the uncertainties in consumption, local renewable energy production, and in electricity price and carbon intensity. Model Predictive Control (MPC) is widely used to implement EMS but is particularly sensitive to the forecast quality, and often requires a subscription to expensive third-party forecast services. We introduce four Multistage stochastic Control Algorithms relying only on historical data obtained from on-site measurements. We formulate them under the shared framework of Multistage stochastic programming and benchmark them against two baselines in 61 different microgrid setups using the EMSx dataset [1]. Our most effective algorithm produces notable cost reductions compared to an MPC algorithm that utilizes the same uncertainty model to generate predictions, and it demonstrates similar performance levels to an ideal MPC that relies on perfect forecasts. Copyright © 2023, The Authors. All rights reserved.

关键词： stochastic programming

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A stochastic programming approach to the physician staffing and scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 142卷 106281-106281页

作者： Marchesi, Janaina F. Hamacher, Silvio Fleck, Julia L. Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Ind Engn St Marques de Sao Vicente 225 BR-22451900 Rio De Janeiro RJ Brazil

A challenging problem for Emergency Department (ED) managers is determining the best allocation of the medical staff that is required to promptly attend patients in the face of increasing demand for emergency care, and the ensuing long patient waiting times. We propose a solution framework that supports physician staffing and scheduling in the ED, considering uncertainties related to patient arrivals. For this, we introduce a two-stage stochastic programming model with fixed recourse that solves in an integrated manner the staffing and scheduling problems and aligns physician scheduling with patient arrivals while minimizing the total number of patients waiting and accounting for all scheduling requirements and contractual agreements. We create possible realization scenarios to consider demand uncertainty using Sample Average Approximation (SAA). In addition, we use discrete-event simulation to estimate the benefits derived from the schedule generated by our model. We validate our methodology with two case studies using real data from hospital EDs. The proposed method enhances alignment between service capacity and demand, significantly improving all queue and wait time indicators. In our first case study, the frequency of queue and average time door-to-doctor were reduced by 73% and 92%, respectively, compared to the current manually-defined ED schedule, and in the second case study, the frequency of queue decreased about 22%, and the average time door-to-doctor decreased 48%. Finally, sensitivity analysis showed that our model-generated optimal schedule is robust to variations in both demand and service rates, indicating that, under small perturbations of current operating conditions, hospital managers would not need to rerun the model.

关键词： Healthcare systems Emergency department Physician Scheduling Staffing stochastic programming

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SOLUTION IMPROVEMENT IN THE TASK OF MULTI-EXTREMAL stochastic programming 21

SOLUTION IMPROVEMENT IN THE TASK OF MULTI-EXTREMAL STOCHASTI...

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21st International Multidisciplinary Scientific Geoconference: Ecology and Renewable Energy Sources. Nano, Bio and Green Technologies for a Sustainable Future, SGEM 2021

作者： Kolbin, Viatcheslav V. Petrov, St. Maxim M. Saint – Petersburg State University Russia

ISBN: (纸本)9786197603637

Decision-making theory has been developing significantly for the last 50 years. It finds its various applications in the very different fields of human activities: engineering, social and life science, and sure economics. The task of substantiating decisions under conditions of uncertainty of all types, except for a priori is reduced to narrowing the initial set of alternatives based on the information available to the decision maker. The quality of recommendations for decision-making under conditions of stochastic uncertainty is enhanced by taking into account such characteristics of the decision maker's personality as the attitude to one's own gains and losses, and the propensity to take risks. Justification of decisions under conditions of a priori uncertainty is possible by constructing adaptive control algorithms. While solving multi-stage tasks, it is necessary to minimize corresponding criteria of the objective function at every stage, which due to disturbing initial conditions of the problem, may take place. As one of the possible approaches to this task, let us take for consideration multi-extremal task of stochastic programming. © 2021 International Multidisciplinary Scientific Geoconference. All rights reserved.

关键词： stochastic programming

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Maximum-posterior evaluation for partially observable multistage stochastic programming

Maximum-posterior evaluation for partially observable multis...

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IISE Annual Conference and Expo 2021

作者： Siddig, Murwan Song, Yongjia Khademi, Amin Clemson University ClemsonSC United States

ISBN: (纸本)9781713838470

This paper introduces a computationally practical approach for solving a class of partially observable multistage stochastic programming problems. In this class of problems, the underlying stochastic process is assumed to follow a hidden Markov chain. Recent advances in the literature introduce a derivative of the stochastic dual dynamic programming (SDDP) method, known as the partially observable SDDP algorithm. This approach, however, introduces a belief-state vector to the stochastic programming formulation, which increases the computation significantly. Instead, we solve the problem assuming that this Markov chain is fully observable. However, when evaluating the resulting policy, we use a Bayesian update of the belief vector and use the state's decisions that have the highest posterior. Our numerical experiments on a hydrothermal planning problem show the practicality of this approach. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： stochastic programming

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Sample Approximations of Bilevel stochastic programming Problems with Probabilistic and Quantile Criteria 20th

Sample Approximations of Bilevel Stochastic Programming Prob...

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20th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2021

作者： Ivanov, Sergey V. Ignatov, Aleksei N. Volokolamskoe Shosse 4 Moscow125993 Russia

ISBN: (纸本)9783030778750

In this paper, bilevel stochastic programming problems with probabilistic and quantile criteria are considered. The lower level problem is assumed to be linear for fixed leader’s (upper level) variables and fixed realizations of the random parameters. The objective function and the constraints of the lower level problem depend on the leader’s strategy and random parameters. The objective function of the upper level problem is defined as the value of the probabilistic or quantile functional of the random losses on the upper level. We suggest conditions guaranteeing that the objective function of the upper level is a normal integrand. It is shown that these conditions are satisfied for a class of problems with positive coefficients of the lower level problem. This allows us to suggest sufficient conditions of the existence of a solution to the considered problem. We construct sample approximations of these problems. These approximations reduce to mixed integer nonlinear programming problems. We describe sufficient conditions of the convergence of the sample approximations to the original problems. © 2021, Springer Nature Switzerland AG.

关键词： stochastic programming

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A two-stage stochastic programming framework for risk-based day-ahead operation of a virtual power plant

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第3期30卷 1-13页

作者： Emarati, Mohammadreza Keynia, Farshid Rashidinejad, Masoud Grad Univ Adv Technol Dept Elect & Comp Engn Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran Shahid Bahonar Univ Dept Elect Engn Kerman Iran

With the increasing use of distributed energy resources (DERs), new technical and economic issues have been raised in power systems. Integration of DERs and energy storage systems (ESSs) in the form of virtual power plant (VPP) resolves an important part of these issues. This paper proposes a risk-based two-stage stochastic optimization framework to address the energy management problem for a VPP. The objective of the proposed framework is to optimize the operation of a VPP in day-ahead (DA) and real-time (RT) markets. In order to include the risk parameter in the proposed decision-making problem, conditional value at risk (CVaR) index is applied in the objective function. The considered uncertain parameters in the model are price in DA market, as well as wind and solar generation for the next day. Markov chain Monte Carlo (MCMC) method is applied to model these uncertain parameters through generation of different scenarios. Also, the effects of using ESS on daily operation of considered VPP is investigated. The performance of the proposed method is illustrated through a case study using real data. The obtained results guarantee the appropriate operation of a VPP considering different values for level of the risk.

关键词： conditional value at risk (CVaR) energy management model energy storage systems (ESSs) stochastic programming virtual power plant (VPP)

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