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Non-compliance in transit-based evacuation pick-up point assignments

SOCIO-ECONOMIC PLANNING SCIENCES 2022年 82卷

作者： Wang, Qingyi Wallace, Stein W. Sichuan Univ Business Sch Chengdu 610064 Peoples R China Norwegian Sch Econ Dept Business & Management Sci Helleveien 30 NO-5045 Bergen Norway

In the literature on evacuation planning, a basic assumption is that all evacuees are fully compliant with respect to the officially planned evacuee-to-facility assignments. However, this assumption is challenged in reality. Hence, we propose two-stage stochastic evacuation pick-up point assignment models, which explicitly incorporate uncertain non-compliance behavior of evacuees. In the first stage, evacuees of each demand point are assigned to a specific transit pick-up point. In the second stage, imbalances of evacuees at each pick-up point are revealed based on the first-stage assignments and the realized non-compliance relevant scenarios. With numerical studies and a case study, we investigate the importance of including correlated stochastic noncompliance behavior in the models, illustrate that our models are effective to produce better pick-up point assignment plans and obtain managerial insights on evacuation planning and model applications.

关键词： Evacuation planning Evacuee non-compliance behavior Pick-up point assignments stochastic programming

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A forward modelling approach to optimise the portfolio of projects among oil companies under uncertainty: a reactive two-stage stochastic model

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International Journal of Industrial and Systems Engineering 2022年第4期42卷 427-451页

作者： Fayyazi, Mohsen Yakhchali, Siamak Haji Pishvaee, Mir Saman Jolai, Fariborz Department of Industrial Engineering Kish International Campus University of Tehran Tehran Iran School of Industrial Engineering College of Engineering University of Tehran Tehran Iran School of Industrial Engineering Iran University of Science and Technology Tehran Iran

Because of high volatility in oil price, oil companies should change their strategies along with changing oil prices. Thus, dynamic portfolio management is strongly recommended to increase the rate of oil production and determine resource allocation for projects in each period of the planning horizon. To achieving the objective, a two-stage stochastic mathematical model is developed to optimise the portfolio of oil projects. To make the model more realistic, splitting the projects and their resumption are permitted. To solve the model, a robust optimisation approach is designed, and the results of the robust and two-stage stochastic designs are compared. These comparisons are based on a realisation algorithm developed by this study. To illustrate the capability and power of the stochastic model in handling the uncertainty, a case study on an oil company is presented. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： stochastic programming

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A stochastic program for biomass contract selection under demand uncertainty

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2022年第4期13卷 1011-1029页

作者： Guericke, Daniela Madsen, Henrik Tech Univ Denmark Dept Appl Math & Comp Sci Richard Petersens Pl DK-2800 Lyngby Denmark

Due to the carbon neutral goals in many countries, a shift from traditional fuels to biomass is currently taking place in the energy sector. In this publication, we are looking at the long-term biomass contracting decisions for combined heat and power plants and power producers. A major share of biomass contracts are long-term contracts with runtimes of around 1 year, so the actual biomass demand is still uncertain when the contracts are negotiated. The operators can select different types of contracts ranging from fixed contracts with fixed amounts and deliveries to more flexible contracts and call options that allow for some flexibility in terms of amount and delivery times. We propose a stochastic program to optimize the contract selection including amounts and deliveries taking the biomass storage and uncertain demand into account. We present results of a case study from industry and show how the model utilizes the contracts for flexibility to adapt to different demand scenarios. Furthermore, the model is used for investigating the tradeoff between storage restrictions and fulfilling the demand in all scenarios. We show why it is important to model this problem as a stochastic program and why considering an expected demand is not enough.

关键词： Biomass supply planning Biomass contracts stochastic programming Flexibility Value of stochastic solution

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Modelling Component Hardening Strategy for Power Network Resilience Optimization Under Uncertain Disruptions: A Distributionally Robust Approach

Modelling Component Hardening Strategy for Power Network Res...

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IISE Annual Conference and Expo 2024

ISBN: (纸本)9781713877851

Designing a resilient power network is paramount for a stable power supply. The component hardening strategy has widespread applications in the resilience design of power networks to mitigate the impact of various natural disasters and malicious attacks. However, the uncertainties associated with natural disasters and deliberate attacks pose a significant challenge in the decision-making of the component hardening strategy. In this article, we put forth a distributionally robust optimization (DRO) model incorporating the component hardening strategy to minimize the impact of uncertain disruptions. The decision-dependent ambiguity set of the DRO model and the binary decision variables in the post-disruption stage greatly increase the complexity of solving the DRO model. A cutting plane algorithm is customized to solve the DRO model. We prove that the proposed cutting plane algorithm can exponentially converge for the master problem and linearly converge for the separate subproblem. The model is also extended to a general form to cover the existing post-disruption recovery models. The results from the case study of a 35 kV power network show that the proposed DRO model yields a high-quality component hardening strategy to enhance the resilience of the power network, comparing with the existing stochastic programming models. The convergence speed of the algorithm is also examined, and it is demonstrated to be effective. © IISE Annual Conference and Expo *** rights reserved.

关键词： stochastic programming

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Modeling time-dependent randomness in stochastic dual dynamic programming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期273卷 650-661页

作者： Lohndorf, Nils Shapiro, Alexander Univ Luxembourg Luxembourg Ctr Logist & Supply Chain Management L-1511 Luxembourg Luxembourg Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

We consider the multistage stochastic programming problem where uncertainty enters the right-hand sides of the problem. stochastic Dual Dynamic programming (SDDP) is a popular method to solve such problems under the assumption that the random data process is stagewise independent. There exist two approaches to incorporate dependence into SDDP. One approach is to model the data process as an autoregressive time series and to reformulate the problem in stagewise independent terms by adding state variables to the model (TS-SDDP). The other approach is to use Markov Chain discretization of the random data process (MC-SDDP). While MC-SDDP can handle any Markovian data process, some advantages of statistical analysis of the policy under the true process are lost. In this work, we compare both approaches based on a computational study using the long-term operational planning problem of the Brazilian interconnected power systems. We found that for the considered problem the optimality bounds computed by the MC-SDDP method close faster than its TS-SDDP counterpart, and the MC-SDDP policy dominates the TS-SDDP policy. When implementing the optimized policies on real data, we observe that not only the method but also the quality of the stochastic model has an impact on policy performance and that using an Av@R formulation is effective in making the policy robust against a misspecified stochastic model. (C) 2018 Elsevier B.V. All rights reserved.

关键词： stochastic programming Dynamic programming Markov decision process Coherent risk measures

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Integrating lean, resilient, and sustainable practices in supply chain network: mathematical modelling and the AUGMECON2 approach

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2022年第4期9卷 451-471页

作者： Shafiee, Mohammad Mehrjerdi, Yahia Zare Keshavarz, Marzieh Yazd Univ Dept Ind Engn Yazd Iran

Nowadays, organisations try to find a proper method to increase their competitiveness. Lean, resilient, and sustainable are top-hole managerial practices in supply chain management. This paper tries to study the effects of integrating lean, resilient, and sustainable practices in the supply chain network (SCN). To this end, a new multi-objective mixed-integer programming is developed in such a way that lean manufacturing is implemented in a resilient-sustainable SCN. The objective functions try to maximise job opportunities and minimise costs, environmental effects, and delivery time. Resilience has been taken into account to tackle the lack of raw materials during disruptions by implementing multiple sourcing in the model. Besides, to devote more attention to sustainable practices, backup suppliers have been prioritised based on sustainability criteria. The academics were enquired to rate sustainability criteria for selecting a backup supplier. Finally, a fuzzy TOPSIS method was used to rank the backup suppliers based on sustainability criteria. The improved version of augmented epsilon-constraint (AUGMECON2) was applied to cope with multi-objectivity of the problem. The results indicated a synergistic effect among leanness, resilience, and sustainability in the supply chain. Also, it turned out that sustainable backup supplier selection boosts the synergistic effect among these three concepts.

关键词： Lean supply chain resilience sustainability stochastic programming supplier selection ε -constraint method

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A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty

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ANNALS OF OPERATIONS RESEARCH 2022年第1期319卷 1231-1263页

作者： Chang, Yanbin Song, Yongjia Eksioglu, Burak Clemson Univ Clemson SC 29634 USA Univ Arkansas Fayetteville AR 72701 USA

In the aftermath of a hurricane, humanitarian logistics plays a critical role in delivering relief items to the affected areas in a timely fashion. This paper proposes a novel stochastic look-ahead framework that implements a two-stage stochastic programming model in a rolling horizon approach to address the evolving uncertain logistics system state during the post-hurricane humanitarian logistics operations. The two-stage stochastic programming model that executes in this rolling horizon approach is formulated as a mixed-integer programming problem. The model aims to minimize the total cost incurred in the logistics operations, which consist of transportation cost and social cost. The social cost is measured as a function of deprivation for unsatisfied demand. Our extensive numerical results and sensitivity analysis demonstrate the effectiveness of the proposed approach in reducing the total cost incurred during the post-hurricane relief logistics operations compared to the two-stage stochastic programming model implemented in a static fashion.

关键词： stochastic programming Rolling horizon Disaster relief logistics Social cost

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Real-time stochastic operation strategy of a microgrid using approximate dynamic programming-based spatiotemporal decomposition approach

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IET RENEWABLE POWER GENERATION 2019年第16期13卷 3061-3070页

作者： Zhu, Jianquan Mo, Xiemin Zhu, Tao Guo, Ye Luo, Tianyun Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou Guangdong Peoples R China Guangdong Power Exchange Ctr Guangzhou Guangdong Peoples R China

This study focuses on the real-time operation of a microgrid (MG). A novel approximate dynamic programming based spatiotemporal decomposition approach is developed to incorporate efficient management of distributed energy storage systems into MG real-time operation while considering uncertainties in renewable generation. The original dynamic energy management problem is decomposed into single-period and single-unit sub-problems, and the value functions are used to describe the interaction among the sub-problems. A two-stage procedure is further designed for the real-time decisions of those sub-problems. In the first stage, empirical data is utilised offline to approximate the value functions. Then in the second stage, each sub-problem can make immediate and independent decision in both temporal and spatial dimensions to mitigate adverse effects of intermittent renewable generation in a MG. No central operator intervention is required, and the near optimal decisions can be obtained at a very fast speed. Case studies based on a six-bus MG and an actual island MG are conducted to demonstrate the effectiveness of the proposed algorithm.

关键词： dynamic programming stochastic processes distributed power generation energy management systems stochastic programming energy storage independent decision intermittent renewable generation central operator intervention six-bus MG actual island MG time stochastic operation strategy microgrid approximate dynamic programming-based spatiotemporal decomposition approach real-time operation novel approximate dynamic programming incorporate efficient management distributed energy storage systems original dynamic energy management problem single-period single-unit sub-problems value functions two-stage procedure real-time decisions immediate decision

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Enhanced indexation via chance constraints

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OPERATIONAL RESEARCH 2022年第2期22卷 1553-1573页

作者： Beraldi, Patrizia Bruni, Maria Elena Univ Calabria Dept Mech Energy & Management Engn Via P BucciCubo 41 C Arcavacata Di Rende Italy

The enhanced index tracking (EIT) represents a popular investment strategy designed to create a portfolio of assets that outperforms a benchmark, while bearing a limited additional risk. This paper analyzes the EIT problem by the chance constraints (CC) paradigm and proposes a formulation where the return of the tracking portfolio is imposed to overcome the benchmark with a high probability value. Besides the CC-based formulation, where the eventual shortage is controlled in probabilistic terms, the paper introduces a model based on the Integrated version of the CC. Here the negative deviation of the portfolio performance from the benchmark is measured and the corresponding expected value is limited to be lower than a given threshold. Extensive computational experiments are carried out on different set of benchmark instances. Both the proposed formulations suggest investment strategies that track very closely the benchmark over the out-of-sample horizon and often achieve better performance. When compared with other existing strategies, the empirical analysis reveals that no optimization model clearly dominates the others, even though the formulation based on the traditional form of the CC seems to be very competitive.

关键词： Enhanced indexation Chance constraints stochastic programming

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Confidence regions of stochastic variational inequalities: error bound approach

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OPTIMIZATION 2022年第7期71卷 2157-2184页

作者： Liu, Yongchao Zhang, Jin Dalian Univ Technol Sch Math Sci Dalian Peoples R China Southern Univ Sci & Technol SUSTech Int Ctr Math Dept Math Shenzhen Peoples R China

In this paper, we aim to build confidence regions of the true solution to the stochastic variational inequalities problem (SVIP) when the sample average approximation (SAA) scheme is implemented. A new approach based on error bound conditions admitted by the SVIP is proposed. This so-called error bound approach provides an upper bound of the distance between SAA solutions and the true solution set through the distance between the SAA function and the true counterpart at the SAA solutions. Certain statistical tools such as central limit theorem and Owen's empirical likelihood theorem are then employed to construct the asymptotic confidence regions of the solutions to SVIP. In particular, if the SVIP admits a global error bound condition, the non-asymptotic (uniform) confidence regions of the solutions are also approachable. Different from the conventional normal map approach, our error bound approach does not require any information regarding the derivative of the solution mapping with respect to perturbations of involved functions in SVIP. For constructing component-wise confidence regions, the validity of the error bound approach is guaranteed for those cases where the functions own separable structures.

关键词： stochastic programming stochastic variational inequalities (non-)asymptotic confidence regions empirical likelihood method

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