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14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Satchidanandan, Bharadwaj Dahleh, Munther A. Massachusetts Institute of Technology Laboratory for Information and Decision Systems United States

ISBN: (纸本)9781665421041

Several economic and environmental considerations have made the transition to electric mobility inevitable. The power system can derive substantial benefits from this transition since Electric Vehicles (EVs) that are not in transit can be used as energy storage resources in the grid. Successful integration of EV energy storage into the grid requires the accompaniment of certain reforms in grid and market operations, one of which pertains to economic dispatch. In this paper, we address the problem of economic dispatch for a power system that has EV energy storage integrated into it. A quintessential feature that distinguishes EV storage from other forms of energy storage is that EV storage is unreliable: An EV could connect and disconnect from the grid at random times and so the availability of an EV's battery to the grid is intermittent and random. Consequently, the system operator cannot bank on the energy that it stores in an EV to be available for discharge at a future time. These aspects necessitate a rethinking of the economic dispatch problem when EV storage is integrated into the grid. The economic dispatch problem must account not simply for the availability of storage, but also the uncertainty in the availability. In this paper, we model these aspects and formulate the EV storage-integrated Economic Dispatch (EV-ED) problem. We also present an optimal, albeit computationally intensive approach to solving it. Methods to overcome the computational difficulty are briefly discussed. © 2022 IEEE.

关键词： stochastic programming

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Fostering long-term care planning in practice: extending objectives and advancing stochastic treatment within location-allocation modelling

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期291卷 1041-1061页

作者： Cardoso-Grilo, Teresa Oliveira, Monica Duarte Barbosa-Povoa, Ana Inst Univ Lisboa ISCTE IUL Business Res Unit BRU IUL Ave Forcas Armadas P-1649026 Lisbon Portugal Univ Lisbon Ctr Management Studies Inst Super Tecn Av Rovisco Pais 1 P-1049001 Lisbon Portugal

Many countries are currently concerned with the planning of networks of Long-Term Care (LTC), which requires considering a multiplicity of policy objectives and anticipating the impact of key uncertainties. Nevertheless, location-allocation literature has not been modelling key health policy objectives, and the use of stochastic planning models entails low practical usability due to prohibitive computational times. This study tackles these issues by proposing an approach that supports the reorganization of LTC networks (in terms of services location, capacity planning and patients allocation) while exploring different health policy objectives and considering uncertainty within a reasonable computational time, leading to the development of a stochastic multi-objective mathematical programming model - the LTCNetPlanner. The LTCNetPlanner builds upon health economics and policy concepts to model the maximization of health and wellbeing together with cost- and equity-related objectives within location-allocation literature. Concerning uncertainty, a scenario-based stochastic approach is developed and alternative scenario reduction methods enabling a faster model resolution are explored within the LTCNetPlanner. Specifically, it is proposed a novel Morphol-KMG method able to reduce the number of scenarios while accounting for experts' knowledge. A case study in the Great Lisbon region is explored, showing the usefulness of the proposed scenario reduction method to reduce computational times, and how planning decisions change when health and wellbeing benefits are considered. (C) 2020 Elsevier B.V. All rights reserved.

关键词： OR in health services Long-term care planning Multi-objective stochastic programming Scenario reduction methods

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Time-dependent stochastic vehicle routing problem with random requests: Application to online police patrol management in Brussels

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期292卷 869-885页

作者： Saint-Guillain, Michael Paquay, Celia Limbourg, Sabine Catholic Univ Louvain Louvain Belgium Maastricht Univ Sch Business & Econ Maastricht Netherlands Univ Liege HEC Management Sch Liege Belgium

The Static and stochastic Vehicle Routing Problem with Random Requests (SS-VRP-R) describes realistic operational contexts in which a fleet of vehicles has to serve customer requests appearing dynamically. Based on a probabilistic knowledge about the appearance of requests, the SS-VRP-R seeks a priori sequences of vehicle relocations, optimizing the expected responsiveness to the requests. In this paper, an existing computational framework, based on recourse strategies, is adapted to meet the objectives of the SS-VRP-R. The resulting models are applied to a real case study of the management of police units in Brussels. In this context, the expected average response time is minimized. To cope with the reality of the urban context, a time-dependent variant is also studied (TD-SS-VRP-R) in which the travel time between two locations is a function that depends on the departure time at the first location. Experiments confirm the contribution and the adaptability of the recourse strategies to a real-life, complex operational context. Provided an adequate solution method, simulation-based results show the high quality of the a priori solutions designed, even when compared to those designed by field experts. Finally, the experiments provide evidence that there is no potential gain in considering time-dependency in such an operational context. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Routing stochastic programming On-demand transportation Optimization under uncertainty Recourse strategies

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Bilevel Integer Programs with stochastic Right-Hand Sides

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INFORMS JOURNAL ON COMPUTING 2021年第4期33卷 1644-1660页

作者： Zhang, Junlong Ozaltin, Osman Y. Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA

We develop an exact value function-based approach to solve a class of bilevel integer programs with stochastic right-hand sides. We first study structural properties and design two methods to efficiently construct the value function of a bilevel integer program. Most notably, we generalize the integer complementary slackness theorem to bilevel integer programs. We also show that the value function of a bilevel integer program can be characterized by its values on a set of so-called bilevel minimal vectors. We then solve the value function reformulation of the original bilevel integer program with stochastic righth- and sides using a branch-and-bound algorithm. We demonstrate the performance of our solution methods on a set of randomly generated instances. We also apply the proposed approach to a bilevel facility interdiction problem. Our computational experiments show that the proposed solution methods can efficiently optimize large-scale instances. The performance of our value function-based approach is relatively insensitive to the number of scenarios, but it is sensitive to the number of constraints with stochastic righth- and sides.

关键词： bilevel programming stochastic programming integer programming value function branch and bound

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A Posteriori Probabilistic Bounds of Convex Scenario Programs With Validation Tests

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第9期66卷 4015-4028页

作者： Shang, Chao You, Fengqi Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA

Scenario programs have established themselves as efficient tools toward decision-making under uncertainty. To assess the quality of scenario-based solutions a posteriori, validation tests based on Bernoulli trials have been widely adopted in practice. However, to reach a theoretically reliable judgment of risk, one typically needs to collect massive validation samples. In this article, we propose new a posteriori bounds for convex scenario programs with validation tests, which are dependent on both realizations of support constraints, and performance on out-of-sample validation data. The proposed bounds enjoy wide generality in that many existing theoretical results can be incorporated as particular cases. To facilitate practical use, a systematic approach for parameterizing a posteriori probability bounds is also developed, which is shown to possess a variety of desirable properties allowing for easy implementations and clear interpretations. By synthesizing comprehensive information about support constraints and validation tests, improved risk evaluation can be achieved for randomized solutions in comparison with existing a posteriori bounds. Case studies on controller design of aircraft lateral motion are presented to validate the effectiveness of the proposed a posteriori bounds.

关键词： Uncertainty Probabilistic logic Robustness Decision making Optimization Random variables Bernoulli trials data-driven decision-making scenario approach stochastic programming

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A simulation-based decomposition approach for two-stage staffing optimization in call centers under arrival rate uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期293卷 966-979页

作者： Thuy Anh Ta Chan, Wyean Bastin, Fabian L'Ecuyer, Pierre Phenikaa Univ Fac Comp Sci ORLab Hanoi Vietnam Univ Montreal Pavillon Andre Aisenstadt Montreal PQ H3C 3J7 Canada Univ Montreal CIRRELT Pavillon Andre Aisenstadt Montreal PQ H3C 3J7 Canada Univ Montreal GERAD Pavillon Andre Aisenstadt Montreal PQ H3C 3J7 Canada

We study a solution approach for a staffing problem in multi-skill call centers. The objective is to find a minimal-cost staffing solution while meeting a target level for the quality of service to customers. We consider a common situation in which the arrival rates are unobserved random variables for which preliminary forecasts are available in a first stage when making the initial staffing decision. In a second stage, more accurate forecasts are obtained and the staffing may have to be modified at a cost, to meet the constraints. This leads to a challenging two-stage stochastic optimization problem in which the quantities involved in the (nonlinear) constraints can only be estimated via simulation, so several independent simulations are required for each first-level scenario. We propose a solution approach that combines sample average approximation with a decomposition method. We provide numerical illustrations to show the practical efficiency of our approach. The proposed method could be adapted to several other staffing problems with uncertain demand, e.g., in retail stores, restaurants, healthcare facilities, and other types of service systems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： stochastic programming Simulation stochastic optimization Sample average approximation L-shaped decomposition

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Sequential Optimization under Uncertainty: Models, Algorithms, and Applications

Sequential Optimization under Uncertainty: Models, Algorithm...

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作者： Yu, Xian University of Michigan

学位级别：Ph.D., Doctor of Philosophy

The primary focus of this dissertation is to develop mathematical models and solution approaches for sequential decision-making and optimization under uncertainty, with applications in transportation, logistics, and healthcare-related operations management. In real-world applications, system operators often need to make sequential decisions, that may involve both discrete and continuous variables under data uncertainties. These problems can be modeled by multistage stochastic integer programs (MS-SIP) that are, however, computationally intractable due to the well-known “curse of dimensionality” issue. MS-SIP assume that the distributions of uncertainty parameters are known and one has access to a finite number of samples of the distributions. In contrary to MS-SIP, multistage distributionally robust integer programs (MS-DRIP) make no assumption on distributions of uncertain parameters. Instead, the optimal solutions are sought for the worst-case probability distributions within a family of candidate distributions, namely, the ambiguity set. Compared to multistage sequential decision models, the two-stage counterparts, namely TS-SIP and TS-DRIP, are easier to solve, where planning decisions are made before uncertainty realizes. In this dissertation, we investigate the four models by developing highly efficient and scalable algorithms and recommend the most practical one in the context of designing and operating complex service systems. Specifically, in Chapter 2, we first study MS-DRIP under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages. We derive mixed-integer linear programming or mixed-integer semidefinite programming reformulations for the min-max Bellman equations, and for the latter we show how to obtain upper and lower bounds of the optimal objective value. We employ the stochastic Dual Dynamic integer programming method for solving the resultant MS-SIP. Our numerical resul

关键词： Optimization under uncertainty Multistage Sequential Decision-Making Distributionally robust optimization stochastic programming Dynamic programming Mixed-integer semidefinite/linear programming Sequential optimization

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Accelerating Benders decomposition for short-term hydropower maintenance scheduling

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期289卷 240-253页

作者： Rodriguez, Jesus A. Anjos, Miguel F. Cote, Pascal Desaulniers, Guy HEC Montreal 3000 Chemin Cote St Catherine Montreal PQ H3T 2A7 Canada GERAD 3000 Ch Cote St Catherine Montreal PQ H3T 2A7 Canada Ecole Polytech Montreal Dept Math & Ind Engn CP 6079Succ Ctr Ville Montreal PQ H3C 3A7 Canada Rio Tinto Aluminium 1954 Davis Saguenay PQ G7S 4R5 Canada Univ Edinburgh Sch Math James Clerk Maxwell BldgPeter Guthrie Tait Rd Edinburgh EH9 3FD Midlothian Scotland

Maintenance of power generators is essential for reliable and efficient electricity production. Because generators under maintenance are typically inactive, optimal planning of maintenance activities must consider the impact of maintenance outages on the system operation. However, in hydropower systems finding a minimum cost maintenance schedule is a challenging optimization problem due to the uncertainty of the water inflows and the nonlinearity of the hydroelectricity production. Motivated by an industrial application problem, we formulate the hydropower maintenance scheduling problem as a two-stage stochastic program, and we implement a parallelized Benders decomposition algorithm for its solution. We obtain convex subproblems by approximating the hydroelectricity production using linear inequalities and indicator variables, which account for the nonlinear effect of the number of active generators in the solution. For speeding up the execution of our decomposition algorithm, we tailor and test seven techniques, including three new applications of special ordered sets, presolve and warm start for Benders acceleration. Given the large number of possible configurations of these acceleration techniques, we illustrate the application of statistical methods and computational experiments to identify the best performing configuration, which achieved a fourfold speedup of the decomposition algorithm. Results in an industrial setting confirm the high scalability on the number of scenarios of our parallelized Benders implementation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： (R) Benders decomposition stochastic programming Decomposition methods Parallel computing Hydroelectricity

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Planning hinterland container transportation in congested deep-sea terminals

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2021年第3期33卷 583-622页

作者： Zweers, Bernard G. Bhulai, Sandjai van der Mei, Rob D. Vrije Univ Amsterdam Amsterdam Netherlands Ctr Wiskunde & Informat Sci Pk 123 NL-1090 GB Amsterdam Netherlands

The size of container ships and the number of containers being transshipped at container terminals have steadily increased over the years. Consequently, it is important to make efficient use of the hinterland capacity. A concept that is used to do this is synchromodal transportation, in which at the very last moment the mode of transportation for a container is decided. Unfortunately, some deep-sea terminals are rather congested and it is unknown by the time the transportation plan is made how many containers can be loaded to and unloaded from a barge. Motivated by this, we study an operational planning problem with uncertainty that is faced by an inland terminal in the port of Amsterdam as a two-stage stochastic problem with recourse. We solve this problem using sample average approximation (SAA) and a fast heuristic using constraints based on stochastic programming (SP). The SAA method gives near-optimal solutions for small instances. For larger instances, the SP-based method is shown to be a good alternative because it is much faster than the SAA method and produces solutions that are less than 1% from the SAA solutions.

关键词： Synchromodal transportation Multimodal transportation Container transportation planning Sample average approximation stochastic programming

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Optimal investment for a retirement plan with deferred annuities allowing for inflation and labour income risk

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期295卷 1132-1146页

作者： Owadally, Iqbal Jang, Chul Clare, Andrew Cass Business Sch 106 Bunhill Row London EC1Y 8TZ England Hanyang Univ 55 Hanyangdeahak Ro Ansan 15588 South Korea

We construct an optimal investment portfolio model for an individual investor saving in a retirement plan. The investor earns stochastic labour income with both permanent and temporary shocks, and has access to equity, conventional bond, inflation-indexed bond and cash, as well as two types of deferred annuities: nominal and inflation-protected. The objective function consists of power utility in terms of real retirement income from the annuities as well as bequest from remaining wealth in tradable securities. Asset returns are represented by a vector autoregressive model underpinned by Nelson-Siegel real and nominal yield curves. The optimization problem is solved numerically using multi-stage stochastic programming with a hybrid scenario structure combining a scenario tree with scenario fans. Our numerical results show that deferred annuities are bought early and in increasing amounts during the working lifetime of the investor, with portfolio risk declining with age. Welfare is diminished by 40% if deferred annuities are not available. Inflation-protected deferred annuities are marginally more important in the presence of real labour income risk, but nominal deferred annuities are bought as a cheaper alternative if real yields are low or negative. Portfolio composition and annuity allocation vary depending on financial market expectations, but our central result about the importance of deferred annuities is robust to a variety of financial market conditions. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Finance stochastic programming Inflation-protected annuities Interest rate model Scenario fans

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