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dynamic pricing for managed lanes with multiple entrances and exits

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2018年第Nov.期96卷 304-320页

作者： Pandey, Venktesh Boyles, Stephen D. Univ Texas Austin Dept Civil Architectural & Environm Engn Austin TX 78712 USA

Priced managed lanes are increasingly being used to better utilize the existing capacity of the roadway to relieve congestion and offer reliable travel time to road users. In this paper, we investigate the optimization problem for pricing managed lanes with multiple entrances and exits which seeks to maximize the revenue and minimize the total system travel time (TSTT) over a finite horizon. We propose a lane choice model where travelers make online decisions at each diverge point considering all routes on a managed lane network. We formulate the problem as a deterministic Markov decision process and solve it using the value function approximation (VFA) method for different initializations. We compare the performance of the toll policies predicted by the VFA method against the myopic revenue policy which maximizes the revenue only at the current timestep and two heuristic policies based on the measured densities on the managed and general purpose lanes (GPIs). We test the results on four different test networks. The primary findings from our research suggest the usefulness of the VFA method for determining dynamic tolls. The best-found objective value from the method at its termination is better than other heuristics for all test networks with average improvements in the objective ranging between 10% and 90% for revenue maximization and 0-27% for TSTT minimization. Certain VFA initializations obtain best-found toll profiles within first 5-50 iterations which warrants computational time savings. Our findings also indicate that the revenue-maximizing optimal policies follow the "jam-and-harvest" behavior where the GPLs are pushed towards congestion in the earlier time steps to generate higher revenue in the later time steps, a characteristic not observed for the policies minimizing TSTT.

关键词： Managed lanes dynamic pricing Route choices approximate dynamic programming Value function approximation

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Spare Parts Inventory Management with Substitution-Dependent Reliability

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INFORMS JOURNAL ON COMPUTING 2018年第3期30卷 507-521页

作者： Khademi, Amin Eksioglu, Burak Clemson Univ Dept Ind Engn Clemson SC 29634 USA

Motivated to apply sustainable supply chain principles to air-pollution control systems, this paper presents a dynamic inventory-management approach where substitution is possible to maintain these systems' equipment. An air-pollution control system's subsequent reliability depends on the replacement equipment selected. The corresponding problem is formulated as a stochastic dynamic program. Because the state and action space are prohibitively large, the approximate policy iteration algorithm is adapted to generate high-quality solutions. Therefore, this work replaces the value function with an affine combination of nonlinear basis functions and shows that a relaxation of the policy improvement step requires the solving of a mixed integer linear program. This approach helps in the designing of an algorithm that improves the quality of the approximation by solving a convex optimization problem. To assess the quality of resulting solutions, a lower bound is developed by considering a relaxation of the problem. In addition, two classes of heuristics are proposed based on a rolling-horizon two-stage stochastic programming formulation of the problem and a standard base-stock ordering policy. The performance of proposed policies is tested on a variety of settings, and results show that the approximate dynamic programming policies are near-optimal in the settings of interest and significantly outperform available benchmarks. The following analysis reveals that the proposed inventory replenishment policies resemble a base-stock policy with occasional deviations, and assignment and substitution decisions are determined by balancing the reliability with ordering, holding, and shortage costs.

关键词： inventory management substitution reliability approximate dynamic programming sustainable supply chain

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High performance super-twisting sliding mode control for a maritime autonomous surface ship (MASS) using ADP-Based adaptive gains and time delay estimation

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OCEAN ENGINEERING 2019年 191卷 106526-000页

作者： Esfahani, Hossein Nejatbakhsh Szlapczynski, Rafal Ghaemi, Hossein Gdansk Univ Technol Fac Ocean Engn & Ship Technol Gdansk Poland

This research addresses two kinds of problems related to optimal trajectory tracking of a Maritime Autonomous Surface Ship (MASS): those caused by the time-varying external disturbances including winds, waves and ocean currents as well as those resulting from inherent dynamical uncertainties. As the paper shows, an accurate and robust optimal controller can successfully deal with both issues. An improved Optimal Adaptive Super-Twisting Sliding Mode Control (OAST-SMC) algorithm is proposed here as a robust optimal adaptive strategy. In this strategy, in order to improve performance of the standard super-twisting approach, we apply an approximate dynamic programming (ADP)-based optimal tuning of gains and an underlying concept based on Time Delay Estimation (TDE). An ADP algorithm is implemented using an actor-critic neural network to deal with the curse of dimensionality in Hamilton-Jacobi-Bellman (HJB) equation. The critical role of TDE part in this algorithm is estimating the impact of disturbances and uncertainties on the MASS model. The results have shown that OAST-TDE significantly outperforms the ST-TDE and AST-TDE algorithm in terms of the optimal control efforts. Also, compared with a Nonlinear Model Predictive Control (NMPC), proposed controller meets the optimal control efforts and accurate tracking concurrently.

关键词： MASS Super-twisting SMC Time delay estimation approximate dynamic programming Actor-critic network Optimal adaptive gains

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A data-driven online ADP control method for nonlinear system based on policy iteration and nonlinear MIMO decoupling ADRC

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NEUROCOMPUTING 2018年 310卷 28-37页

作者： Huang, Zhijian Zhang, Cheng Zhang, Yanyan Zhang, Guichen Shanghai Maritime Univ Lab Intelligent Control & Computat Shanghai 201306 Peoples R China Tongji Univ Peoples Hosp 10 Shanghai 200072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

The action-critic approximate dynamic programming (ADP) depends on its network structure and training algorithm. Since there are some inherent shortcomings of the neural network, this paper proposes a data-driven nonlinear online ADP control method without the neural network. Firstly, a multi-input multi-output (MIMO) policy iteration method is utilized for the proposed ADP. For its policy evaluation, the cost function is approximated with a quadratic function and least square method;for its policy improvement, the optimal control is approximated by solving the quadratic function linearly. In this way, an optimal control equation in form of variable coefficients and system states is deduced. Secondly, a nonlinear MIMO decoupling Active Disturbance Rejection Control method is used to obtain the variable coefficients in real time, which endows the ADP method with a nonlinear performance during its policy improvement. Once the variable coefficients are determined, the data-driven nonlinear ADP control method is deduced. Finally, the examples of an under-actuated nonlinear system and a real application are taken to demonstrate the optimal control effect. Compared with some published methods and their simulation, this method and its simulation excel in the method of policy improvement, nonlinear ability and control performance etc. Thus, the proposed method explores a new way to the ADP, and overcomes the shortcomings of the neural-network-based ADP. Since it enables to work like a PID controller and does not require data collecting, training or extra learning, this proposed ADP is a real data-driven non-linear online optimal control method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Data driven Online approximate dynamic programming Linear quadratic function Least square method Assumed neural network ADRC

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Capacitated inspection scheduling of multi-unit systems

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 120卷 471-479页

作者： Sir, Esra Sisikoglu Pariazar, Mahmood Sir, Mustafa Y. Mayo Clin Rochester MN 55905 USA Amer Airlines Inc Ft Worth TX USA

We consider the inspection scheduling problem of multi-unit systems where the inspections of individual units are coupled via a capacity constraint. Although the optimal inspection policy of the majority of single-unit systems can be characterized by a threshold policy, finding an optimal policy for multi-unit systems is significantly harder. Therefore, the current state-of-the-practice uses a periodical inspection policy for all units. Instead, we propose using a dynamic programming (DP) approach to solve small-scale problem instances to optimality and use solutions optimized for a single-unit system in an approximation scheme to obtain near optimal solutions for large-scale problems. Our results show that taking individual properties of the units to be inspected into account and incorporating the single-unit solutions within an approximate DP framework significantly decrease the inspection cost compared to a periodical inspection policy. The proposed methods can help resource-constrained regulatory agencies such as US Food and Drug Administration (FDA) to optimize their inspection activities.

关键词： Scheduling Inspection and maintenance approximate dynamic programming Multi-unit systems

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Optimization of Battery Charging and Purchasing at Electric Vehicle Battery Swap Stations

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TRANSPORTATION SCIENCE 2018年第5期52卷 1211-1234页

作者： Schneider, Frank Thonemann, Ulrich W. Klabjan, Diego Univ Cologne Supply Chain Management & Management Sci D-50931 Cologne Germany Northwestern Univ Ind Engn & Management Sci Evanston IL 60208 USA

An operator of a network of battery swap stations for electric vehicles must make a long-term investment decision on the number of batteries and charging bays in the system and periodic short-term decisions on when and how many batteries to recharge. Both decisions must be made concurrently, because there exists a trade-off between the long-term investment in batteries and charging bays, and short-term expenses for operating the system. Costs for electric energy as well as demand rates for batteries are stochastic: We consider an infinite time horizon for operation of the system. We derive an optimization problem, which cannot be solved optimally in a reasonable time for real world instances. By optimally solving various small problem instances, we show the mechanics of the model and the influence of its parameters on the optimal cost. We then develop a near-optimal solution heuristic based on Monte Carlo sampling following the ideas of approximate dynamic programming for the infinite horizon dynamic program. We show that operating battery swap stations in a network where lateral transshipments are allowed can substantially decrease expected operating costs.

关键词： battery swapping approximate dynamic programming lateral transshipments stochastic optimzation electric vehicles

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Real-Time Ambulance Dispatching and Relocation

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2018年第3期20卷 467-480页

作者： Nasrollahzadeh, Amir Ali Khademi, Amin Mayorga, Maria E. Clemson Univ Dept Ind Engn Clemson SC 29634 USA North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27695 USA

In this study, we develop a flexible optimization framework for real-time ambulance dispatching and relocation. In addition to ambulance redeployment, we consider a general dispatching and relocation strategy by which the decision maker has the option to (i) select any available ambulance to dispatch to a call or to queue the call and (ii) send an idle ambulance to cover the location of an ambulance just dispatched to a call. We formulate the problem as a stochastic dynamic program, and, because the state space is unbounded, an approximate dynamic programming (ADP) framework is developed to generate high-quality solutions. We assess the quality of our solutions by developing a lower bound on the expected response time and computing a lower bound on the expected fraction of late calls of any relocation policy. We test the performance of our policies and available benchmarks on an emergency medical services system in Mecklenburg County, North Carolina. The results show that our policies are near optimal and significantly outperform available benchmarks. In particular, our ADP policy reduces the expected response time and fraction of high-priority late calls by 12% and 30.6%, respectively, over the best available static benchmarks in the case study. Moreover, the results provide insights on the contribution of each dispatching, redeployment, and reallocation strategy.

关键词： ambulance operations management dispatching redeployment reallocation approximate dynamic programming

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Iterative adaptive dynamic programming methods with neural network implementation for multi-player zero-sum games

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NEUROCOMPUTING 2018年 307卷 54-60页

作者： Jiang, He Zhang, Huaguang Han, Ji Zhang, Kun Northeastern Univ Coll Informat Sci & Engn Box 134 Shenyang 110819 Liaoning Peoples R China

This paper presents novel iterative learning methods along with the neural network implementation for multi-player zero-sum games. Solving zero-sum games depends on the solutions of Hamilton-Jacobi-Isaacs equations, which are nonlinear partial differential equations. These solutions are generally difficult or even impossible to be obtained analytically. To overcome this difficulty, iterative adaptive dynamic programming algorithms are utilized. In the related research works, three-network architecture, i.e., critic-actor-disturbance structure, is used to approximate the value function, control policies and disturbance policies. Different from the previous works, this paper employs single-network architecture, i.e., critic-only structure, to implement the proposed algorithms, which reduces the computation burden and the complexity of design procedure. Finally, two simulation examples are provided to illustrate the effectiveness of our proposed methods. (C) 2018 Published by Elsevier B.V.

关键词： Adaptive dynamic programming approximate dynamic programming Zero-sum games Neural networks

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Hedging Strategies: Electricity Investment Decisions under Policy Uncertainty

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ENERGY JOURNAL 2018年第1期39卷 101-122页

作者： Morris, Jennifer F. Srikrishnan, Vivek Webster, Mort D. Reilly, John M. MIT Joint Program Sci & Policy Global Change 77 Massachusetts Ave Cambridge MA 02139 USA Penn State Univ John & Willie Leone Family Dept Energy & Mineral State Coll PA USA MIT Joint Program Sci & Policy Global Change Cambridge MA USA

Given uncertainty in long-term carbon reduction goals, how much non-carbon generation should be developed in the near-term? This research investigates the optimal balance between the risk of overinvesting in non-carbon sources that are ultimately not needed and the risk of underinvesting in non-carbon sources and subsequently needing to reduce carbon emissions dramatically. We employ a novel framework that incorporates a computable general equilibrium (CGE) model of the U.S. into a two-stage stochastic approximate dynamic program (ADP) focused on decisions in the electric power sector. We solve the model using an ADP algorithm that is computationally tractable while exploring the decisions and sampling the uncertain carbon limits from continuous distributions. The results of the model demonstrate that an optimal hedge is in the direction of more non-carbon investment in the near-term, in the range of 20-30% of new generation. We also demonstrate that the optimal share of non-carbon generation is increasing in the variance of the uncertainty about the long-term carbon targets, and that with greater uncertainty in the future policy regime, a balanced portfolio of non-carbon, natural gas, and coal generation is desirable.

关键词： Energy policy Uncertainty Electricity Investment Emissions General equilibrium approximate dynamic programming

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Optimal Triggering of Networked Control Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第7期29卷 3011-3021页

作者： Heydari, Ali Southern Methodist Univ Dept Mech Engn Dallas TX 75205 USA

This paper is focused on bandwidth allocation in nonlinear networked control systems. The objective is optimal triggering/scheduling for transmitting sensor measurements to the controller through a communication network. An algorithm based on approximate dynamic programming is developed for problems with fixed final times and then the result is extended to problems with infinite horizon. Zero-order-hold (ZOH), generalized ZOH, and networks with packet dropouts are the investigated cases. Problems with unknown models are also addressed and a model-free scheme is established for learning the (approximate) optimal solution. The convergence, optimality, and stability of the results are analyzed and, afterwards, some numerical analyses are presented for demonstrating the potentials of the algorithms in practice.

关键词： approximate dynamic programming event triggering networked control systems (NCSs) optimal triggering

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