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ASME International Design Engineering Technical Conferences / 42nd Annual Computers and Information in Engineering Conference (IDETC-CIE) / 48th Design Automation Conference (DAC)

作者： Azad, Saeed Herber, Daniel R. Colorado State Univ Dept Syst Engn Ft Collins CO 80523 USA

ISBN: (纸本)9780791886229

This article explores various uncertain control co-design (UCCD) problem formulations. While previous work offers formulations that are method-dependent and limited to only a handful of uncertainties (often from one discipline), effective application of UCCD to real-world dynamic systems requires a thorough understanding of uncertainties and how their impact can be captured. Since the first step is defining the UCCD problem of interest, this article aims at addressing some of the limitations of the literature by identifying possible sources of uncertainties in a general UCCD context and then formalizing ways in which their impact is captured through problem formulation alone (without having to immediately resort to solution strategies). We first develop a universal UCCD formulation and discuss its fundamental elements. Issues such as the treatment of the objective function, the challenge of the analysis-type equality constraints, and various formulations for inequality constraints are discussed. Then, more specialized problem formulations such as the risk-neutral and risk-averse stochastic, worst-case robust, probabilistic robust, fuzzy expected value, and possibilistic chance-constrained UCCD formulations are presented. Key concepts from these formulations, along with insights from closely-related fields, such as robust and stochastic control theory, are discussed, and future research directions are identified.

关键词： control co-design dynamics uncertainty stochastic programming fuzzy programming robust optimization

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Consensus Distributionally Robust Optimization With Phi-Divergence

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IEEE ACCESS 2021年 9卷 92204-92213页

作者： Ohmori, Shunichi Waseda Univ Dept Business Design & Management Tokyo 1698555 Japan

We study an efficient algorithm to solve the distributionally robust optimization (DRO) problem, which has recently attracted attention as a new paradigm for decision making in uncertain situations. In traditional stochastic programming, a decision is sought that minimizes the expected cost over the probability distribution of the unknown parameters. In contrast, in DRO, robust decision making can be derived from data without assuming a probability distribution;thus, it is expected to provide a powerful method for data-driven decision making. However, it is computationally difficult to solve the DRO problem and even by state-of-art solvers the problem size that can be solved to optimality is still limited. Therefore, we propose an efficient algorithm for solving DRO based on consensus optimization (CO). CO is a distributed algorithm in which a large-scale problem is decomposed into smaller subproblems. Because different local solutions are obtained by solving subproblems, a consensus constraint is imposed to ensure that these solutions are equal, thereby guaranteeing global convergence. We applied the proposed method to linear programming, quadratic programming, and second-order cone programming in numerical experiments and verified its effectiveness.

关键词： Optimization programming Probability distribution Decision making Uncertainty stochastic processes Linear programming Alternating direction method of multipliers consensus optimization decomposition method distributionally robust optimization stochastic programming

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Long-term resource planning in the high-tech industry: Capacity or inventory?

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期293卷 926-940页

作者： Smirnov, Dina van Jaarsveld, Willem Atan, Zumbul de Kok, Ton Eindhoven Univ Technol Sch Ind Engn POB 513 NL-5600 MB Eindhoven Netherlands

Volatile and non-stationary demand, very complex production processes, long production and capacity acquisition leadtimes, and extremely high and rarely reversible capacity acquisition costs make capacity planning in the high-tech industry a particularly challenging managerial task. To cope with demand surges and hedge against its uncertainty, firms can invest in order to expand their capacity for the longterm. For the short-term, however, demand is often fixed due to committed orders, and excess capacity might be available that may be used to build up inventory as a complementary means of hedging against these demand surges and uncertainty. In multi-echelon systems, these decisions need to be made simultaneously and must be aligned over the echelons. We address the strategic problem of determining the optimal inventory buildup and capacity expansion timing and amount across locations of a multi-echelon supply chain, as well as the operational problem of production planning. We model the problem as a two-stage stochastic program (SP), with strategic decisions at the first stage and operational decisions at the second stage. We enumerate the capacity expansion timing and use the deterministic equivalent formulation to optimize the other strategic decisions. Through an extensive numerical study, we provide practical managerial insights into the system behavior. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Capacity expansion High-tech industry Multi-echelon assembly system stochastic programming Deterministic equivalent Scenario-based approach

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A Study on the Optimal Inventory Allocation for Clinical Trial Supply Chains

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APPLIED MATHEMATICAL MODELLING 2021年 98卷 161-184页

作者： Zheng, Meimei Du, Ningxin Zhao, Hui Huang, Edward Wu, Kan Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore George Mason Univ Syst Engn & Operat Res Dept Fairfax VA 22030 USA Chang Gung Univ Dept Ind & Business Management Taoyuan Taiwan

With increasing competition in the pharmaceutical industry, pharmaceutical companies pay more attention to improving the efficiency of clinical trial supply chains to reduce the drug supplying cost, which takes up a considerable part of the total research and development expense. To improve the efficiency of clinical trial supply chains, this study investigates the inventory levels of clinical drugs in each period at a distribution center and clinics considering the reverse replenishment, transshipment, and generalized stock out cost. The inventory allocation problem in clinical trial supply chains is formulated as a rolling horizon-based two-stage stochastic mixed-integer model where the minimal operational cost constitutes the underage cost at the production planning level of clinical trial supply chains. An algorithm extending Benders decomposition is proposed as the solution approach. We also derive several structural results and develop the reformulation method and row generation strategy to improve the efficiency of the optimization process. The effectiveness of our approach is demonstrated in the numerical experiment. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Supply chain management Clinical trials Inventory allocation stochastic programming

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stochastic network optimization for strategic resource pre-positioning and allocation

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2025年 287卷

作者： Eberhardt, Katharina Fuchss, Patricia Kaiser, Florian Klaus Rosenberg, Sonja Schultmann, Frank Karlsruhe Inst Technol KIT Inst Ind Prod IIP Karlsruhe Germany

This paper presents a stochastic network modeling approach to develop insights into strategic facility location planning, capacity management, resource pre-positioning, and allocation. The primary purpose of the proposed model is to present a cost-effective logistics network designed for efficiently handling diverse relief items across a spectrum of crisis scenarios. By incorporating stochastic elements, we aim to capture the inherent unpredictability of demand fluctuations and the impact of crises. Our approach optimizes facility sizes to leverage economies of scale while improving allocation decisions. Additionally, it ensures fairness across demand points by implementing a strategy to mitigate relative shortages. To demonstrate the practical applicability of our model, we conduct a computational case study utilizing instances from the national food stockpiling system in Germany. Moreover, we present a sensitivity analysis highlighting the impact of crisis intensity, increased storage and production capacity, and weighting decisions of transportation costs on facility location and assignment decisions. The results provide economic and managerial insights for public decision-makers, enhancing cost-effective disaster preparedness and network design. The case study shows that the proposed model optimizes inventory by eliminating excess quantities and favoring large warehouses, reducing costs through fewer locations. However, prioritizing rapid delivery results in a more decentralized network with smaller, costlier warehouses. The logistics network adapts to varying demand scenarios, strategically placing warehouses in densely populated regions with higher crisis risks.

关键词： Logistics network design Pre-positioning stochastic programming Disaster management Economies of scale

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Multistage Expansion Planning of Integrated Biogas and Electric Power Delivery System Considering the Regional Availability of Biomass

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2021年第2期12卷 920-930页

作者： Yang, Hanyu Li, Canbing Shahidehpour, Mohammad Zhang, Cong Zhou, Bin Wu, Qiuwei Zhou, Long Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Hunan Key Lab Intelligent Informat Anal Integrate Changsha 410082 Hunan Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China IIT Elect & Comp Engn Dept Chicago IL 60616 USA Tech Univ Denmark Ctr Elect Power & Energy Dept Elect Engn DK-2800 Lyngby Denmark China Southern Power Grid Co Ltd Power Res Inst Guangzhou 528000 Peoples R China

Centralized biogas plant (CBP) provides an attractive solution to the energy supply for district heating, electric loads, and residential cooking in remote areas via a local biogas delivery network. To overcome the challenges of biomass availability for CBPs, an integrated expansion planning model is proposed in this paper. The model makes investment decisions on the sequential planning of integrated electric power distribution and biogas delivery networks and uses the constrained biomass transportation network to optimally determine CBP locations in the integrated network. The proposed approach also considers the biomass availability and the cost of biomass supply in the integrated model. The proposed multistage stochastic planning problem makes investment decisions considering the gradual realization of uncertainties as loads grow and biomass is made available. The polyhedral approximation method is applied to convert the proposed mixed-integer second-order cone programming (MISOCP) problem to a mixed-integer linear programming (MILP) problem for making the problem more tractable for large-scale cases. The effectiveness of the proposed integrated model is validated using an 8-node and a 24-node test system.

关键词： Biomass Transportation Planning Biological system modeling Natural gas Power systems Investment Biomass energy expansion planning of integrated energy system electric power distribution stochastic programming

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Efficient Real-Time EV Charging Scheduling via Ordinal Optimization

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IEEE TRANSACTIONS ON SMART GRID 2021年第5期12卷 4029-4038页

作者： Long, Teng Jia, Qing-Shan Wang, Gongming Yang, Yu Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat BNRist Beijing 100084 Peoples R China Berkeley Educ Alliance Res SinBerBEST Singapore 138602 Singapore

The surge of plug-in electric vehicles (PEV) on the roads poses the issue to handle the substantial charging demand. Particularly, the operation of charging stations requires an efficient and scalable real-time scheduling method to accommodate the dramatic charging requests in an economical (i.e., to best utilize the local renewable generation) and friendly (i.e., to reduce the impact on the electric grid) manner. This paper fulfills the objectives and makes the following main contributions. First, we develop a parameterized aggregated PEV charging model using the energy boundaries to express the charging flexibility. We propose to parameterize the aggregated charging policy by the incomplete Beta function based on the problem structures. The proposed model can scale down the decision variables from O(NH) to O(2) where N is the number of PEVs and H is the number of prediction horizon. Second, we develop an ordinal optimization (OO) based method (denoted as OO-P) to search for good enough charging policies within seconds while still providing probabilistic performance guarantee. Third, we demonstrate the performance of the proposed OO-P via simulations. The numerical results show that the solution of OO-P is only 4% worse than the optima. However, OO-P shows high computation efficiency and scalability. Compared with the existing real-time PEV charging scheduling method, OO-P can reduce 6% of the operation cost for the charging station. OO-P is also shown to outperform the existing heuristic rule.

关键词： Electric vehicle ordinal optimization stochastic programming

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Multistage adaptive stochastic mixed integer optimization under endogenous and exogenous uncertainty

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AICHE JOURNAL 2021年第10期67卷 e17333-e17333页

作者： Nasab, Farough Motamed Li, Zukui Univ Alberta Dept Chem & Mat Engn 9211 116 St Edmonton AB T6G1H9 Canada

To solve multistage adaptive stochastic optimization problems under both endogenous and exogenous uncertainty, a novel solution framework based on robust optimization technique is proposed. The endogenous uncertainty is modeled as scenarios based on an uncertainty set partitioning method. For each scenario, the adaptive binary decision is assumed constant and the continuous variable is approximated by a function linearly dependent on endogenous uncertain parameters. The exogenous uncertainty is modeled using lifting methods. The adaptive decisions are approximated using affine functions of the lifted uncertain parameters. In order to demonstrate the applicability of the proposed framework, a number of numerical examples of different complexity are studied and a case study for infrastructure and production planning of shale gas field development are presented. The results show that the proposed framework can effectively solve multistage adaptive stochastic optimization problems under both types of uncertainty.

关键词： decision rule exogenous and endogenous uncertainty robust optimization stochastic programming

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Element Failure Probability of Soil Slope under Consideration of Random Groundwater Level

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INTERNATIONAL JOURNAL OF GEOMECHANICS 2021年第7期21卷 p1-20页

作者： Li, Ze Chen, Yu Guo, Yakun Zhang, Xiaoyan Du, Shigui Kunming Univ Sci & Technol Fac Civil Engn & Mech Kunming 650500 Yunnan Peoples R China Univ Bradford Fac Engn & Informat Bradford BD7 1DP W Yorkshire England Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Yunnan Peoples R China Key Lab Rock Mech & Geohazards Zhejiang Prov Shaoxing 312000 Zhejiang Peoples R China

The instability of soil slopes is directly related to the shear parameters of the soil material and groundwater, which usually causes some uncertainty. In this study, a novel method, the element failure probability method (EFP), will be proposed to analyze the failure of soil slopes. Based on upper bound theory, finite element discretization, and stochastic programming theory, an upper bound stochastic programming model will be established by simultaneously considering the randomness of the shear parameters and groundwater level to analyze the reliability of slopes. The model will be solved using the Monte Carlo method based on the random shear parameters and groundwater levels. Finally, a formula will be derived for the EFP based on the safety factors and velocity fields of the upper bound method. The probability of a slope failure can be calculated using the safety factor, and the distribution of failure regions in space can be determined using the location information of the element. The proposed method will be validated using a classic example. This study could have theoretical value for further research that attempts to advance the application of plastic limit analysis to analyze slope reliability.

关键词： Soil slope Reliability Element failure probability Upper bound method Finite element discretization stochastic programming

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An accelerated directional derivative method for smooth stochastic convex optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第2期290卷 601-621页

作者： Dvurechensky, Pavel Gorbunov, Eduard Gasnikov, Alexander Weierstrass Inst Appl Anal & Stochast Mohrenstr 39 D-10117 Berlin Germany RAS Inst Informat Transmiss Problems Bolshoy Karetny 19Build 1 Moscow 127051 Russia Natl Res Univ Higher Sch Econ 11 Pokrovsky Blvd Moscow 109028 Russia Moscow Inst Phys & Technol 9 Inst Skiy Per Dolgoprudnyi 141700 Moscow Region Russia

We consider smooth stochastic convex optimization problems in the context of algorithms which are based on directional derivatives of the objective function. This context can be considered as an intermediate one between derivative-free optimization and gradient-based optimization. We assume that at any given point and for any given direction, a stochastic approximation for the directional derivative of the objective function at this point and in this direction is available with some additive noise. The noise is assumed to be of an unknown nature, but bounded in the absolute value. We underline that we consider directional derivatives in any direction, as opposed to coordinate descent methods which use only derivatives in coordinate directions. For this setting, we propose a non-accelerated and an accelerated directional derivative method and provide their complexity bounds. Our non-accelerated algorithm has a complexity bound which is similar to the gradient-based algorithm, that is, without any dimension-dependent factor. Our accelerated algorithm has a complexity bound which coincides with the complexity bound of the accelerated gradient-based algorithm up to a factor of square root of the problem dimension. We extend these results to strongly convex problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： stochastic programming Convex programming Acceleration Derivative-free optimization Zero-order methods

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