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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Liu, Xuan Zhou, Min Ma, Jianjun Wang, Bin Zhan, Shuguang Dong, Hairong Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China China Acad Railway Sci Co Ltd Inst Elect Comp Technol Beijing 100044 Peoples R China Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Anhui Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

High-speed railways (HSRs) are susceptible to disruptions that can cause trains to deviate from their operation plan and, in severe cases, even disrupt the rolling stock plan. The duration of disruptions is often highly uncertain and unpredictable. It makes the timetable rescheduling problem even more challenging. This paper proposes an Event-Activity Network-based rescheduling model to solve the timetable rescheduling problem for HSRs with the reassignment of rolling stock under uncertain disruption. To deal with the uncertainty, the uncertain disruption is regarded to have several possible deterministic scenarios. A two-stage stochastic programming approach is utilized to make a conflict-free rescheduling scheme for all possible scenarios within the minimum end time. A case study based on the practical data of the Beijing-Tianjin Intercity Railway is carried out to demonstrate the effectiveness of the proposed method. The results show that the proposed method can decrease the total delay and cancellations, and improve the robustness of rescheduling schemes.

关键词： High-speed railway Uncertain disruption Rolling stock two-stage stochastic programming

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A regularized smoothing method for fully parameterized convex problems with applications to convex and nonconvex two-stage stochastic programming

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MATHEMATICAL programming 2021年第1-2期189卷 117-149页

作者： Borges, Pedro Sagastizabal, Claudia Solodov, Mikhail Inst Matematica Pura & Aplicada Rio De Janeiro RJ Brazil IMECC UNICAMP Campinas SP Brazil

We present an approach to regularize and approximate solution mappings of parametric convex optimization problems that combines interior penalty (log-barrier) solutions with Tikhonov regularization. Because the regularized mappings are single-valued and smooth under reasonable conditions, they can be used to build a computationally practical smoothing for the associated optimal value function. The value function in question, while resulting from parameterized convex problems, need not be convex. One motivating application of interest is two-stage (possibly nonconvex) stochastic programming. We show that our approach, being computationally implementable, provides locally bounded upper bounds for the subdifferential of the value function of qualified convex problems. As a by-product of our development, we also recover that in the given setting the value function is locally Lipschitz continuous. Numerical experiments are presented for two-stage convex stochastic programming problems, comparing the approach with the bundle method for nonsmooth optimization.

关键词： Smoothing techniques Interior penalty solutions Tikhonov regularization Nonconvex stochastic programming two-stage stochastic programming Lipschitz continuity of value functions

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Scenario-based two-stage stochastic programming for a Hybrid Manufacturing-Remanufacturing System with the uncertainty of returns, quality and demand

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2021年第1期46卷 1-9页

作者： Reddy, K. Nageswara Kumar, Akhilesh Velaga, Nagendra R. Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Indian Inst Technol Civil Engn Dept Mumbai Maharashtra India

This paper addresses the optimization of make-to-order hybrid manufacturing - remanufacturing system to make capacity and inventory decisions jointly along with production decisions. The proposed system considers a common production facility and the same assembly/disassembly line to perform manufacturing and remanufacturing operations simultaneously. The current study takes into account an environment where new and remanufactured (reman) products competing to each other that is, the common demand stream for both products but different selling prices. Furthermore, the relative capacity consumed by remanufacturing over the manufacturing is explained in two ways, namely less capacity intensive case and more capacity intensive case. Differently, from previous studies, we consider a scenario with a discounted selling price for reman products, shortage penalty costs, lost sales, disposal and uncertainty in demand, amount and yield of returns. Hence, to handle those uncertainties, a scenario-based stochastic programming model in a two-stage setting is presented. In the first stage, the raw material inventory and production capacity levels are planned and in the second stage, the production, inventory and disposal decisions are determined by balancing overage and underage costs. The results indicate that net values associated with new and reman products can be decisive in choosing either manufacturing or remanufacturing.

关键词： Capacity investment inventory planning remanufacturing two-stage stochastic programming scenario-based approach

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two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources

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APPLIED ENERGY 2021年 291卷 116830-116830页

作者： Han, Dongho Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn 291 Daehak Ro Daejeon 34141 South Korea

Microgrid (MG) is intended to accommodate distributed renewable energy sources and manage the generated electricity using a dispatch and storage system to satisfy the demand of a local region over time. Main challenges associated with operating such a system stems primarily from the intermittent and uncertain nature of renewable energy sources causing the energy generation to be highly irregular and less predictable. Multi-microgrid (MMG) systems with flexibility to trade energy between individual MGs can alleviate the demand-supply mismatch problem, but its design and operation become more complicated. Previous studies on MMGs addressed only the case where all the MGs are fully connected to share electricity. However, the installation of electric cable connections over the full network can be costly and is often unjustified. To address the question of how to best achieve a tradeoff, this work proposes the use of a two-stage stochastic decision-making approach for designing and operating an MMG considering both the capital cost for installing the electric cables and operating cost. First, real weather data are used to develop stochastic models that describe the uncertain, intermittent nature of energy production through renewable sources over a day. Then, in the first stage, design-level decisions regarding the installation of the electric cable between individual pairs of the MGs are to be determined to achieve the best tradeoff for the modeled system and weather pattern. In the second stage, the optimal energy dispatch planning for each individual MG including the energy trading between the MGs is carried out based on a particular realized scenario of the renewable energy production.

关键词： Multi-microgrid Uncertainty modeling two-stage stochastic programming Monte Carlo simulation Optimal topology Optimal operation

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Multi-objective two-stage stochastic programming for adaptive interdisciplinary pain management with piecewise linear network transition models

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IISE TRANSACTIONS ON HEALTHCARE SYSTEMS ENGINEERING 2021年第3期11卷 240-254页

作者： Iqbal, Gazi Md Daud Rosenberger, Jay Chen, Victoria Gatchel, Robert Noe, Carl Coppin State Univ Coll Business Baltimore MD 21216 USA Univ Texas Arlington Ind & Mfg Syst Engn Arlington TX USA Univ Texas Arlington Psychol Arlington TX USA UT Southwestern Med Ctr Pain Management & Anesthesiol Dallas TX USA

Pain is a major health problem for many people, and pain management is currently innovating because of the opioid crisis in the United States. Existing models optimizing personal adaptive treatment strategies for chronic pain management have only considered one pain outcome. However, most of the pain management centers consider multiple pain outcome measures to identify pain intensity. Consequently, this research uses five pain outcomes. Transition models are represented by piecewise linear networks (PLN). A multi-objective mixed integer linear program (MILP) is developed to optimize treatment strategies for patients based upon on these transition models. A convex quadratic program (QP) is developed to determine weights for multiple levels of multiple pain outcomes that are consistent with surveys submitted by pain management experts. Results show that the MILP that considers multiple pain outcomes yields treatment recommendations with better expected outcomes compared to observed data and to solutions from an optimization model with a single pain outcome objective.

关键词： Piecewise linear network model mixed integer linear program convex quadratic programming two-stage stochastic programming pain management odd's ratio

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A two-stage stochastic programming for AGV scheduling with random tasks and battery swapping in automated container terminals

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2023年第1期174卷

作者： Li, Linman Li, Yuqing Liu, Ran Zhou, Yaoming Pan, Ershun Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai 200240 Peoples R China

Automated guided vehicle (AGV) is one of the main equipment for horizontal transportation in automated container terminals, and the optimization of AGV scheduling has become increasingly important. Existing scheduling systems tend to make decisions based on deterministic conditions, ignoring the dynamic changes and uncertainties of the terminal environment, such as the arrival of random tasks during AGV operations. In addition, the battery swapping process is neglected in most AGV scheduling studies, yet it is crucial to ensure the operation of AGVs. In this paper, we construct a two-stage stochastic programming model for the joint scheduling problem of battery swapping and task operation with random tasks. A double-threshold constraint for battery swapping decision-making is adopted. The results show that the double-threshold strategy is better for AGV utilization than the single-threshold one. Upon the solution method, a simulationbased ant colony optimization algorithm is proposed. Sample average approximation is used to calculate the expected cost, and two local search procedures are introduced to improve the quality of the solutions. In the cases of multiple instances and several random task samples with different arrival rates, our method was compared with three practical policies under a deterministic model. Computational results show that the scheduling scheme considering random tasks in advance is more robust and stable.

关键词： two-stage stochastic programming AGV scheduling Random tasks Battery swapping Ant colony optimization algorithm

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Designing a closed-loop supply chain for reusable packaging materials: A risk-averse two-stage stochastic programming model using CVaR

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 167卷

作者： Das, Debabrata Verma, Priyanka Tanksale, Ajinkya N. Natl Inst Ind Engn NITTE Mumbai 400087 Maharashtra India BHU Dept Mech Engn Indian Inst Technol Varanasi 221005 Uttar Pradesh India

Reusable packaging material (RPM) provide a circular and sustainable way of transporting parts/products safely along the various stages of supply chain. In the present work, we study a multi-echelon closed-loop supply chain network design problem for wooden RPMs. Initially, a mixed-integer programming model under deterministic setting is proposed to determine the optimal configuration of the manufacturing facilities and distribution centers of RPMs. Further, a risk-averse two-stage stochastic programming model is developed to incorporate the uncertainties associated with the demand, initial inventory and level of reusability of the RPMs. The developed model utilizes conditional-value-at-risk (CVaR) as risk measure since it provides more robust solutions compared to a risk-neutral approach in presence of uncertainty. The applicability of the model is explained using a real-life example of the prevalent supply chain of wooden pallets in several automobile manufacturers and their suppliers in India. The results suggest trade-off among various cost components such as fixed cost of opening facilities and capacity installation, manufacturing, acquisition, refurbishing, and environmental cost. Several managerial insights are drawn by carrying out sensitivity analysis of deterministic model and running the stochastic model with different values of CVaR parameters. Finally, we present numerical results to discuss how incorporating a risk measure affects the computational performance and solution of the CVaR model.

关键词： Reusable Packaging Materials Network Design Closed-Loop Supply Chain two-stage stochastic programming Conditional-Value-at-Risk

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Intraday Scheduling of a System With Following Units Based on two-stage stochastic programming

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Zhou, Yunhai Jia, Qian Xin, Yuejie Zhang, Tao China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China China Three Gorges Univ Hubei Prov Key Lab Operat & Control Cascaded Hydro Yichang Peoples R China

In order to further reduce the impact of renewable energy forecast errors on the system scheduling plan, this paper proposes an intraday rolling scheduling strategy for systems with following units based on two-stage stochastic programming. Firstly, the nonparametric kernel density is used to estimate the probability density function of wind and photovoltaic power prediction errors. The first stage is to pre-decide the operating state of the following unit with the goal of minimizing the start-stop cost of the fast unit. Then, based on the determined start-stop information in the second stage, the active power output of each unit is optimized with the goal of the lowest expected cost of the overall system operation. Finally, the objective function is linearized to transform the model into a mixed integer linear programming problem, which can be solved with the help of the solver software GUROBI. Through the analysis of practical examples, it is verified that the built model can reduce the number of starts and stops of the following units, reducing the operating cost of the power system and increasing the rate of renewable energy on-grid, which has more practical application significance.

关键词： renewable energy intraday scheduling two-stage stochastic programming following units nonparametric kernel density estimation start-stop state

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two-stage stochastic programming Based on Particle Swarm Optimization for Aircraft Sequencing and Scheduling

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第4期20卷 1365-1377页

作者： Hong, Youkyung Choi, Byunghun Kim, Youdan Seoul Natl Univ Inst Adv Aerosp Technol Dept Mech & Aerosp Engn Seoul 08826 South Korea Agcy Def Dev Deajeon 34186 South Korea

A two-stage stochastic programming algorithm based on particle swarm optimization is proposed for an optimal and robust design in the aircraft sequencing and scheduling problem. The first and second stage decision problems are defined as aircraft sequencing and scheduling, respectively, and the uncertainties that arise from the aircraft flight time between two successive fixes are considered. Particle swarm optimization is utilized for a randomized search to make the first stage decision under incomplete information about uncertain parameters. In addition, the random key representation is adopted to apply particle swarm optimization to a discrete aircraft sequencing problem. Then, the second stage decision is made using mixed integer linear programming after the realization of the uncertain parameters. The performance of the proposed algorithm is verified by numerical simulations.

关键词： Aircraft sequencing and scheduling uncertainty two-stage stochastic programming particle swarm optimization

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DISTRIBUTIONALLY ROBUST two-stage stochastic programming

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SIAM JOURNAL ON OPTIMIZATION 2022年第3期32卷 1499-1522页

作者： Duque, Daniel Mehrotra, Sanjay Morton, David P. Northwestern Univ Ind Engn & Management Sci Evanston IL 60208 USA

Distributionally robust optimization is a popular modeling paradigm in which the underlying distribution of the random parameters in a stochastic optimization model is unknown. Therefore, hedging against a range of distributions, properly characterized in an ambiguity set, is of interest. We study two-stage stochastic programs with linear recourse in the context of distributional ambiguity, and formulate several distributionally robust models that vary in how the ambiguity set is built. We focus on the Wasserstein distance under a p-norm, and an extension, an optimal quadratic transport distance, as mechanisms to construct the set of probability distributions, allowing the support of the random variables to be a continuous space. We study both unbounded and bounded support sets, and provide guidance regarding which models are meaningful in the sense of yielding robust first-stage decisions. We develop cutting-plane algorithms to solve two classes of problems, and test them on a supply-allocation problem. Our numerical experiments provide further evidence as to what type of problems benefit the most from a distributionally robust solution.

关键词： distributionally robust optimization two-stage stochastic programming Wasser-stein distance optimal transport distance

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