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An Adaptive Learning-Based Approach for Nearly Optimal dynamic Charging of Electric Vehicle Fleets

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2018年第7期19卷 2066-2075页

作者： Korkas, Christos D. Baldi, Simone Yuan, Shuai Kosmatopoulos, Elias B. Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece Ctr Res & Technol Hellas Informat & Telemat Inst Thessaloniki 57001 Greece Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

Managing grid-connected charging stations for fleets of electric vehicles leads to an optimal control problem where user preferences must be met with minimum energy costs (e.g., by exploiting lower electricity prices through the clay, renewable energy production, and stored energy of parked vehicles). Instead of state-of-the-art charging scheduling based on open-loop strategies that explicitly depend on initial operating conditions, this paper proposes an approximate dynamic programming feedback-based optimization method with continuous state space and action space, where the feedback action guarantees uniformity with respect to initial operating conditions, while price variations in the electricity and available solar energy are handled automatically in the optimization. The resulting control action is a multi-modal feedback, which is shown to handle a wide range of operating regimes, via a set of controllers whose action that can be activated or deactivated depending on availability of solar energy and pricing model. Extensive simulations via a charging test case demonstrate the effectiveness of the approach.

关键词： Electric vehicles charging optimization approximate dynamic programming

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Merchant Energy Trading in a Network

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OPERATIONS RESEARCH 2018年第5期66卷 1304-1320页

作者： Nadarajah, Selvaprabu Secomandi, Nicola Univ Illinois Coll Business Adm Chicago IL 60607 USA Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA

We formulate the merchant trading of energy in a network of storage and transport assets as a Markov decision process with uncertain energy prices, generalizing known models. Because of the intractability of our model, we develop heuristics and both lower and dual (upper) bounds on the optimal policy value estimated within Monte Carlo simulation. We achieve tractability using linear optimization, extending near optimal approximate dynamic programming techniques for the case of a single storage asset, versions of two of which are commercially available. We propose (i) a generalization of a deterministic reoptimization heuristic, (ii) an iterative version of the least squares Monte Carlo approach, and (iii) a perfect information dual bound. We apply our methods to a set of realistic natural gas instances. The combination of our reoptimization heuristic and dual bound emerges as a practical approach to nearly optimally solve our model. Our iterative least squares Monte Carlo heuristic is also close to optimal. Compared to our other heuristic, it exhibits slightly larger optimality gaps and requires some tuning, but is faster to execute in some cases. Our methods could enhance single energy storage asset software and have potential relevance beyond our specific application.

关键词： merchant commodity and energy operations Markov decision processes approximate dynamic programming dual bounds energy storage real options least squares Monte Cario linear optimization

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The One-Dimensional dynamic Dispatch Waves Problem

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TRANSPORTATION SCIENCE 2018年第2期52卷 402-415页

作者： Klapp, Mathias A. Erera, Alan L. Toriello, Alejandro Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Pontificia Univ Catolica Chile Engn Sch Santiago Chile

We study same-day delivery systems by formulating the dynamic dispatch waves problem (DDWP), which models a depot where delivery requests arrive dynamically throughout a service day. At any dispatch epoch (wave), the information available to the decision maker is (1) a set of known, open requests that remain unfulfilled, and (2) a set of potential requests that may arrive later in the service day. At each wave, the decision maker decides whether or not to dispatch a vehicle, and if so, which subset of open requests to serve, with the objective of minimizing expected vehicle operating costs and penalties for unserved requests. We consider the DDWP with a single delivery vehicle and request destinations on a line, where vehicle operating times and costs depend only on the distance between points. We propose an efficient dynamic programming approach for the deterministic variant, and leverage it to design an optimal a priori policy with predetermined routes for the stochastic case. We then show that fully dynamic policies may perform arbitrarily better than a priori ones, and propose heuristics and dual bounds for this case.

关键词： same-day delivery dynamic dispatch approximate dynamic programming

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The Benefits of State Aggregation with Extreme-Point Weighting for Assemble-to-Order Systems

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OPERATIONS RESEARCH 2018年第4期66卷 1040-1057页

作者： Nadar, Emre Akcay, Alp Akan, Mustafa Scheller-Wolf, Alan Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey Eindhoven Univ Technol Sch Ind Engn NL-5600 MB Eindhoven Netherlands Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA

We provide a new method for solving a very general model of an assemble-to-order system: multiple products, multiple components that may be demanded in different quantities by different products, batch production, random lead times, and lost sales, modeled as a Markov decision process under the discounted cost criterion. A control policy specifies when a batch of components should be produced and whether an arriving demand for each product should be satisfied. As optimal solutions for our model are computationally intractable for even moderately sized systems, we approximate the optimal cost function by reformulating it on an aggregate state space and restricting each aggregate state to be represented by its extreme original states. Our aggregation drastically reduces the value iteration computational burden. We derive an upper bound on the distance between aggregate and optimal solutions. This guarantees that the value iteration algorithm for the original problem initialized with the aggregate solution converges to the optimal solution. We also establish the optimality of a lattice-dependent base-stock and rationing policy in the aggregate problem when certain product and component characteristics are incorporated into the aggregation/disaggregation schemes. This enables us to further alleviate the value iteration computational burden in the aggregate problem by eliminating suboptimal actions. Teveraging all of our results, we can solve the aggregate problem for systems of up to 22 components, with an average distance of 11.09% from the optimal cost in systems of up to 4 components (for which we could solve the original problem to optimality).

关键词： assemble-to-order systems Markov decision processes approximate dynamic programming aggregation

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OPTIMALLY REPLACING MULTIPLE SYSTEMS IN A SHARED ENVIRONMENT

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2018年第2期32卷 179-206页

作者： Abdul-Malak, David T. Kharoufeh, Jeffrey P. Univ Pittsburgh Dept Ind Engn 1025 Benedum Hall3700 O Hara St Pittsburgh PA 15261 USA

We consider the problem of optimally replacing multiple stochastically degrading systems using condition-based maintenance. Each system degrades continuously at a rate that is governed by the current state of the environment, and each fails once its own cumulative degradation threshold is reached. The objective is to minimize the sum of the expected total discounted setup, preventive replacement, reactive replacement, and downtime costs over an infinite horizon. For each environment state, we prove that the cost function is monotone nondecreasing in the cumulative degradation level. Additionally, under mild conditions, these monotonicity results are extended to the entire state space. In the case of a single system, we establish that monotone policies are optimal. The monotonicity results help facilitate a tractable, approximate model with state-and action-space transformations and a basis-function approximation of the action-value function. Our computational study demonstrates that high-quality, near-optimal policies are attainable and significantly outperform heuristic policies.

关键词： maintenance optimization approximate dynamic programming

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Model predictive control under forecast uncertainty for optimal operation of buildings with integrated solar systems

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SOLAR ENERGY 2018年第Sep.期171卷 953-970页

作者： Liu, Xiaoqi Paritosh, Parth Awalgaonkar, Nimish M. Bilionis, Ilias Karava, Panagiota Purdue Univ Lyles Sch Civil Engn 550 Stadium Mall Dr W Lafayette IN 47907 USA Purdue Univ Ctr High Performance Bldg Ray W Herrick Labs 177 S Russell St W Lafayette IN 47907 USA Purdue Univ Sch Mech Engn 585 Purdue Mall W Lafayette IN 47907 USA

In this paper, we explore intelligent operation strategies, based on stochastic model predictive control (SMPC), for optimal utilization of solar energy in buildings with integrated solar systems. Our approach takes into account the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this complex optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process regression, and achieves good solution quality. In the final step, we use an emulator that couples physical system models in TRNSYS with the SMPC controller developed using Python and MATLAB to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate under consideration, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a baseline well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.

关键词： Autoregressive model approximate dynamic programming Building-integrated solar energy systems Forecast uncertainty

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A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning

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IEEE TRANSACTIONS ON SMART GRID 2018年第4期9卷 3291-3303页

作者： Keerthisinghe, Chanaka Verbic, Gregor Chapman, Archie C. Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

This paper presents a computationally efficient smart home energy management system (SHEMS) using an approximate dynamic programming (ADP) approach with temporal difference learning for scheduling distributed energy resources. This approach improves the performance of an SHEMS by incorporating stochastic energy consumption and PV generation models over a horizon of several days, using only the computational power of existing smart meters. In this paper, we consider a PV-storage (thermal and battery) system, however, our method can extend to multiple controllable devices without the exponential growth in computation that other methods such as dynamic programming (DP) and stochastic mixed-integer linear programming (MILP) suffer from. Specifically, probability distributions associated with the PV output and demand are kernel estimated from empirical data collected during the Smart Grid Smart City project in NSW, Australia. Our results show that ADP computes a solution much faster than both DP and stochastic MILP, and provides only a slight reduction in quality compared to the optimal DP solution. In addition, incorporating a thermal energy storage unit using the proposed ADP-based SHEMS reduces the daily electricity cost by up to 263% without a noticeable increase in the computational burden. Moreover, ADP with a two-day decision horizon reduces the average yearly electricity cost by a 4.6% over a daily DP method, yet requires less than half of the computational effort.

关键词： Demand response smart home energy management distributed energy resources approximate dynamic programming dynamic programming stochastic mixed-integer linear programming value function approximation temporal difference learning

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Stochastic Optimization for Controlling the End State of Hydrogenation of Edible Oil in the Presence of Process Failure

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International Journal of Operations Research 2019年第4期16卷 91-104页

作者： Soham Chakraborty Pathik Mandal

Controlling the end state of a growth process by terminating it at the right time is defined as a problem of passive regulation. Within this class, the periodic sampling based methods are used in many practical situations. However, it appears that this type of passive regulation system has received no systematic attention in the past. The problem of controlling the final melting point of a batch of edible oil during hydrogenation belongs to this category. This problem is formulated in terms of two stochastic optimization problems. The first problem is solved following a scenario tree based approach, while the second one is formulated as a Stochastic dynamic Program (SDP). The SDP is solved by evaluating a quality cost function through simulation. It is shown that the performance of the hydrogenation process is expected to improve significantly under the proposed passive regulation scheme.

关键词： Simulation Hydrogenation of edible oil Growth model Control chart approximate dynamic programming

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Heuristics for the stochastic dynamic task-resource allocation problem with retry opportunities

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第1期266卷 291-303页

作者： Gulpinar, Nalan Canakoglu, Ethem Branke, Juergen Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England Bahgesehir Univ Ind Engn Istanbul Turkey

This paper deals with a stochastic multi-period task-resource allocation problem. A team of agents with a set of resources is to be deployed on a multi-period mission with the goal to successfully complete as many tasks as possible. The success probability of an agent assigned to a task depends on the resources available to the agent. Unsuccessful tasks can be tried again at later periods. While the problem can in principle be solved by dynamic programming, in practice this is computationally prohibitive except for tiny problem sizes. To be able to tackle also larger problems, we propose a construction heuristic that assigns agents and resources to tasks sequentially, based on the estimated marginal utility, Based on this heuristic, we furthermore propose various approximate dynamic programming approaches and an Evolutionary Algorithm. All suggested approaches are empirically compared on a number of randomly generated problem, instances. We show that the construction heuristic is very fast and provides good results. For even better results, at the expense of longer computational time, approximate dynamic programming seems a suitable alternative. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Task-resource allocation approximate dynamic programming Heuristics Retry opportunities

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Budgeting Time for dynamic Vehicle Routing with Stochastic Customer Requests

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TRANSPORTATION SCIENCE 2018年第1期52卷 20-37页

作者： Ulmer, Marlin W. Mattfeld, Dirk C. Koester, Felix Tech Univ Carolo Wilhelmina Braunschweig D-38106 Braunschweig Germany

Parcel services route vehicles to pick up parcels in the service area. Pickup requests occur dynamically during the day and are unknown before their actual request. Because of working hour restrictions, service vehicles only have a limited time to serve dynamic requests. As a result, not all requests can be confirmed. To achieve an overall high number of confirmed requests, dispatchers have to budget their time effectively by anticipating future requests. To determine the value of a decision, i. e., the expected number of future confirmations given a point of time and remaining free time budget, we present an anticipatory time budgeting heuristic (ATB) drawing on methods of approximate dynamic programming. ATB frequently simulates a problem's realization to subsequently approximate the values for every vector of point of time and free time budget to achieve an approximation of an optimal decision policy. Since the number of vectors is vast, we introduce the dynamic lookup table (DLT), a general approach adaptively partitioning the vector space to the approximation process. Compared with state- of- the- art benchmark heuristics, ATB allows an effective use of the time budget resulting in anticipatory decision making and high solution quality. Additionally, the DLT significantly strengthens and accelerates the approximation process.

关键词： dynamic vehicle routing stochastic customer requests time budget subset selection approximate dynamic programming dynamic lookup table

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