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Meso-parametric value function approximation for dynamic customer acceptances in delivery routing

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第1期285卷 183-195页

作者： Ulmer, Marlin W. Thomas, Barrett W. Tech Univ Carolo Wilhelmina Braunschweig Carl Friedrich Gauss Fak Muhlenpfordtstr 23 D-38106 Braunschweig Germany Univ Iowa Tippie Coll Business 108 John Pappajohn Business Bldg Iowa City IA 52242 USA

The rise of mobile communication, ample computing power, and Amazon's training of customers has led to last-mile delivery challenges and created struggles for companies seeking to budget their limited delivery resources efficiently to generate enough revenue. In this paper, we examine the capacitated customer acceptance problem with stochastic requests (CAPSR), a problem in which a company seeks to maximize expected revenue by accepting or rejecting requests. Each accepted request generates revenue and must be routed, consuming driver time and vehicle capacity. To solve the problem, we introduce a novel method of value function approximation (VFA). Conventionally, VFAs are either parametric (P-VFAs) or non-parametric (N-VFAs). Both VFAs have advantages and shortcomings and their performances rely significantly on the structure of the underlying problem. To combine the advantages and to alleviate the shortcomings of P-VFA and N-VFA used individually, we present a novel method, meso-parametric value function approximation (M-VFA). The results of computational experiments show that the M-VFA outperforms benchmarks for the CAPSR and show M-VFA offers the advantages of the individual VFAs while alleviating their shortcomings. Most importantly, we demonstrate that simultaneous approximations lead to better outcomes than either N- and P-VFA individually or some ex-post combination. (C) 2019 Published by Elsevier B.V.

关键词： dynamic customer acceptances dynamic vehicle routing dynamic multi-dimensional knapsack problem approximate dynamic programming Value function approximation

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Least squares policy iteration with instrumental variables vs. direct policy search: comparison against optimal benchmarks using energy storage

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INFOR 2020年第1期58卷 141-166页

作者： Moazehi, Somayeh Scott, Warren R. Powell, Warren B. Stevens Inst Technol Sch Business Hoboken NJ 07030 USA Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

This article studies least-squares approximate policy iteration (API) methods with parametrized value-function approximation. We study several variations of the policy evaluation phase, namely, Bellman error minimization, Bellman error minimization with instrumental variables, projected Bellman error minimization, and projected Bellman error minimization with instrumental variables. For a general discrete-time stochastic control problem, Bellman error minimization policy evaluation using instrumental variables is equivalent to both variants of the projected Bellman error minimization. An alternative to these API methods is direct policy search based on knowledge gradient. The practical performance of these three approximate dynamic programming methods, (i) least squares API with Bellman error minimization, (ii) least squares API with Bellman error minimization with instrumental variables, and (iii) direct policy search, are investigated in the context of an application in energy storage operations management. We create a library of test problems using real-world data and apply value iteration to find their optimal policies. These optimal benchmarks are then used to compare the developed approximate dynamic programming policies. Our analysis indicates that least-squares API with instrumental variables Bellman error minimization prominently outperforms least-squares API with Bellman error minimization. However, these approaches underperform our direct policy search implementation.

关键词： dynamic programming approximate dynamic programming approximate policy iteration Bellman error minimization direct policy search energy storage

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Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2020年 132卷 60-75页

作者： Lei, Chao Jiang, Zhoutong Ouyang, Yanfeng Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA

Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Ridesharing dynamic pricing Self-driving vehicle Bi-level optimization MPEC approximate dynamic programming

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Sectorial reflectance-based cleaning policy of heliostats for Solar Tower power plants

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RENEWABLE ENERGY 2020年 166卷 176-189页

作者： Truong-Ba, Huy Cholette, Michael E. Picotti, Giovanni Steinberg, Theodore A. Manzolini, Giampaolo Queensland Univ Technol 2 George St Brisbane Qld 4001 Australia Politecn Milan Dipartimento Energia Via Lambruschini 4 I-20156 Milan Italy Int Univ VNUHCM Ho Chi Minh City Vietnam

For concentrating Solar Tower (ST) power plants, heliostats must be cleaned to maintain high productivity, but this comes at the cost of cleaning expenditures. Striking the correct balance remains challenging, due in part to the fact that soiling losses are location-dependent, stochastic, seasonal, and spatially inhomogeneous across the field. In this paper, novel reflectance-based cleaning policies are developed that trigger and prioritize cleaning of different solar field sectors based on reflectance measurements. In contrast to existing approaches, these policies have the potential to mitigate the effect of stochastic soiling losses and allocate finite cleaning resources by considering the spatial inhomogeneity of soiling. The optimization of the policy is conducted using the approximate Markov Decision Process (MDP) paradigm that utilizes a simulation model based on a recently developed physical soiling model. The proposed approach is applied to a case study on a hypothetical ST plant located in South Australia. The proposed policies are benchmarked with other traditional time-based cleaning policies and a previously developed reflectance-based policy. The results indicate a considerable benefit of sectorial reflectance-based cleaning strategies to other benchmarked policies (i.e. similar to 2% savings on total cleaning costs). Moreover, in case where the per-cleaning costs (e.g. water, fuel) are significant compared to the fixed costs (e.g. truck depreciation), the savings of proposed sectorial cleaning policies are greater (similar to 10% savings). (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Heliostat cleaning optimization approximate dynamic programming Reflectance Cleaning Heliostats Solar tower Concentrating solar power

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A Multi-Critic Reinforcement Learning Method: An Application to Multi-Tank Water Systems

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IEEE ACCESS 2020年 8卷 173227-173238页

作者： Martinez-Piazuelo, Juan Ochoa, Daniel E. Quijano, Nicanor Giraldo, Luis Felipe Univ los Andes Dept Ingn Elect & Elect Bogota 111711 Colombia Univ Colorado Dept Elect Comp & Energy Engn Boulder CO 80309 USA

This paper investigates the combination of reinforcement learning and neural networks applied to the data-driven control of dynamical systems. In particular, we propose a multi-critic actor-critic architecture that eases the value function learning task by distributing it into multiple neural networks. We also propose a filtered multi-critic approach that offers further performance improvements as it eases the training process of the control policy. All the studied methods are evaluated with several numerical experiments on multi-tank water systems with nonlinear coupled dynamics, where control is known to be a challenging task. The simulation results show that the proposed multi-critic scheme is able to outperform the standard actor-critic approach in terms of speed and sensitivity of the learning process. Moreover, the results show that the filtered multi-critic strategy outperforms the unfiltered one under these same terms. This document highlights the benefits of the multi-critic methodology on a state of the art reinforcement learning algorithm, the deep deterministic policy gradient, and demonstrates its application to multi-tank water systems relevant for industrial process control.

关键词： Neural networks Learning (artificial intelligence) Task analysis Training Control systems Process control Sensitivity Data-driven control approximate dynamic programming reinforcement learning actor-critic methods deep deterministic policy gradient water-tank systems

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dynamic multi-priority, multi-class patient scheduling with stochastic service times

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第1期280卷 254-265页

作者： Saure, Antoine Begen, Mehmet A. Patrick, Jonathan Univ Ottawa Telfer Sch Management 55 Laurier Ave East Ottawa ON K1N 6N5 Canada Western Univ Ivey Sch Business 1255 Western Rd London ON N6G 0N1 Canada

Efficient patient scheduling has significant operational, clinical and economical benefits on health care systems by not only increasing the timely access of patients to care but also reducing costs. However, patient scheduling is complex due to, among other aspects, the existence of multiple priority levels, the presence of multiple service requirements, and its stochastic nature. Patient appointment (allocation) scheduling refers to the assignment of specific appointment start times to a set of patients scheduled for a particular day while advance patient scheduling refers to the assignment of future appointment days to patients. These two problems have generally been addressed separately despite each being highly dependent on the form of the other. This paper develops a framework that incorporates stochastic service times into the advance scheduling problem as a first step towards bridging these two problems. In this way, we not only take into account the waiting time until the day of service but also the idle time/overtime of medical resources on the day of service. We first extend the current literature by providing theoretical and numerical results for the case with multi-class, multi-priority patients and deterministic service times. We then adapt the model to incorporate stochastic service times and perform a comprehensive numerical analysis on a number of scenarios, including a practical application. Results suggest that the advance scheduling policies based on deterministic service times cannot be easily improved upon by incorporating stochastic service times, a finding that has important implications for practice and future research on the combined problem. (C) 2019 Elsevier B.V. All rights reserved.

关键词： OR in health services Patient scheduling Markov decision processes approximate dynamic programming Linear programming

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MIDAS: A mixed integer dynamic approximation scheme

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MATHEMATICAL programming 2020年第1期181卷 19-50页

作者： Philpott, A. B. Wahid, F. Bonnans, J. F. Univ Auckland Elect Power Optimizat Ctr Auckland New Zealand Artelys Consulting Paris France Ecole Polytech INRIA Saclay France

Mixed integer dynamic approximation scheme (MIDAS) is a new sampling-based algorithm for solving finite-horizon stochastic dynamic programs with monotonic Bellman functions. MIDAS approximates these value functions using step functions, leading to stage problems that are mixed integer programs. We provide a general description ofMIDAS, and prove its almost-sure convergence to a 2T e-optimal policy for problems with T stages when the Bellman functions are known to be monotonic, and the sampling process satisfies standard assumptions.

关键词： Stochastic programming approximate dynamic programming Sampling Mixed-integer programming

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Robust optimal control for a class of nonlinear systems with unknown disturbances based on disturbance observer and policy iteration

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NEUROCOMPUTING 2020年 390卷 185-195页

作者： Song, Ruizhuo Lewis, Frank L. Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Texas Arlington UTA Res Inst Ft Worth TX 76118 USA

A robust optimal control method for a class of nonlinear systems with unknown disturbances is addressed in this paper. In this framework, adaptive dynamic programming (ADP) is presented to obtain the optimal control. On-policy learning allows the performance index function and the optimal control to be obtained iteratively. It is shown that the iterative performance index function is non-increasing. A nonlinear disturbance observer is designed to estimate external disturbances. The compensation control is used to compensate for the influence of the disturbances. It is proven that the disturbance observer error is exponentially stable, under some conditions. The properties of the nonlinear system with unknown disturbance steered by the robust optimal control input are also proven. Simulation results demonstrate the performance of the proposed robust optimal control scheme for the nonlinear system with unknown disturbance. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Adaptive critic designs Adaptive dynamic programming approximate dynamic programming Optimal control On-policy Disturbance

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Delay-Sensitive Energy-Harvesting Wireless Sensors: Optimal Scheduling, Structural Properties, and Approximation Analysis

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第4期68卷 2509-2524页

作者： Sharma, Nikhilesh Mastronarde, Nicholas Chakareski, Jacob SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA New Jersey Inst Technol Coll Comp Newark NJ 07103 USA

We consider an energy harvesting sensor transmitting latency-sensitive data over a fading channel. We aim to find the optimal transmission scheduling policy that minimizes the packet queuing delay given the available harvested energy. We formulate the problem as a Markov decision process (MDP) over a state-space spanned by the transmitter's buffer, battery, and channel states, and analyze the structural properties of the resulting optimal value function, which quantifies the long-run performance of the optimal scheduling policy. We show that the optimal value function (i) is non-decreasing and has increasing differences in the queue backlog;(ii) is non-increasing and has increasing differences in the battery state;and (iii) is submodular in the buffer and battery states. Taking advantage of these structural properties, we derive an approximate value iteration algorithm that provides a controllable tradeoff between approximation accuracy, computational complexity, and memory, and we prove that it converges to a near-optimal value function and policy. Our numerical results confirm these properties and demonstrate that the resulting scheduling policies outperform a greedy policy in terms of queuing delay, buffer overflows, energy efficiency, and sensor outages.

关键词： Sensors Batteries Scheduling Energy harvesting Optimal scheduling Approximation algorithms Wireless sensor networks Markov decision processes energy harvesting latency-sensitive wireless sensing approximate dynamic programming structural properties

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Influence of unmanned combat aerial vehicle agility on short-range aerial combat effectiveness

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AEROSPACE SCIENCE AND TECHNOLOGY 2020年 96卷

作者： Wang, Maolin Wang, Lixin Yue, Ting Liu, Hailiang Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China

The flight agility of an unmanned combat aerial vehicle (UCAV) determines its ability to rapidly transition from one state to another. In this paper, the influence of flight agility on short-range aerial combat effectiveness is quantitatively investigated. This research is based on one-on-one three-dimensional aerial combat engagements. First, a 6-DOF mathematical model of the UCAV is established, and a nonlinear dynamic inverse controller is designed. The flight agility is calculated based on the 180 degrees flight heading reverse maneuver, and the influence of the control law parameters is studied. To implement autonomous intelligent aerial combat engagements, a three-dimensional approximate dynamic programming method is proposed. The performance of the designed algorithm is validated through both Monte Carlo simulations with various initial conditions and engagements with another algorithm for comparison. After training, the aerial combat simulation framework is constructed by combining the point-mass-based guidance law and a nonlinear UCAV model. Combat engagements are conducted between UCAVs with different configurations. The quantitative results are evaluated through Monte Carlo simulations and correlation analysis. (C) 2019 Elsevier Masson SAS. All rights reserved.

关键词： Aerial combat UCAV Flight agility approximate dynamic programming

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