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Heterogeneous multi-agent resource allocation through multi-bidding with applications to precision agriculture⁎

IFAC-PapersOnLine 2018年第23期51卷 194-199页

作者： Cobbenhagen, A.T.J.R. Antunes, D.J. van de Molengraft, M.J.G. Heemels, W.P.M.H. Control Systems Technology group Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands

In this paper we consider the problem of allocating multiple resources to a number of clients by a group of heterogeneous agents over time such that the clients can produce products while maximizing a profit function. We propose an approximate optimization framework in which every client provides multiple bids from which the agents choose such that an allocation is feasible and that the profit function is maximized over time. The proposed framework exploits decomposition techniques that can be used for large-scale multi-agent resource allocation problems in which the cost objective is additive, the dynamics of product generation is non-linear and the agents have different capabilities. Interestingly, the decomposition can be solved in a distributed fashion, enabling application to large-scale problems. We apply this decomposition to the management of resources and agents in precision agriculture as an inspirational and important application domain of the obtained results. We show that our framework can be used in order to schedule the time, location and quantity of resources that every agent must provide whilst optimizing the profit of the entire farm over the growing season. © 2018

关键词： dynamic programming Agriculture Multi agent systems Precision agriculture Profitability Resource allocation approximate dynamic programming approximate optimization Decomposition technique Distributed optimization Heterogeneous agents Multi agent Optimal controls Resource allocation problem

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dynamic Patient Scheduling for Multi-Appointment Health Care Programs

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PRODUCTION AND OPERATIONS MANAGEMENT 2018年第1期27卷 58-79页

作者： Diamant, Adam Milner, Joseph Quereshy, Fayez York Univ Schulich Sch Business 111 Ian Macdonald Blvd Toronto ON M3J 1P3 Canada Univ Toronto Rotman Sch Management 105 St George St Toronto ON M5S 3E6 Canada Toronto Western Hosp Gen Surg 399 Bathurst St Toronto ON M5T 2S8 Canada

We investigate the scheduling practices of a multidisciplinary, multistage, outpatient health care program. Patients undergo a series of assessments before being eligible for elective surgery. Such systems often suffer from high rates of attrition and appointment no-shows leading to capacity underutilization and treatment delays. We propose a new scheduling model where the clinic assigns patients to an appointment day but postpones the decision of which assessments patients undergo pending the observation of who arrives. In doing so, the clinic gains flexibility to improve system performance. We formulate the scheduling problem as a Markov decision process and use approximate dynamic programming to solve it. We apply our approach to a dataset collected from a bariatric surgery program at a large tertiary hospital in Toronto, Canada. We examine the quality of our solutions via structural results and compare them with heuristic scheduling practices using a discrete-event simulation. By allowing multiple assessments, delaying their scheduling, and by optimizing over an appointment book, we show significant improvements in patient throughput, clinic profit, use of overtime, and staff utilization.

关键词： service systems appointment scheduling approximate dynamic programming no-shows multiple appointments

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Management of a stage-structured insect pest: an application of approximate optimization

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ECOLOGICAL APPLICATIONS 2018年第4期28卷 938-952页

作者： Hackett, Sean C. Bonsall, Michael B. Univ Oxford Dept Zool Math Ecol Res Grp South Parks Rd Oxford OX1 3PS England St Peters Coll New Inn Hall St Oxford OX1 2DL England

Ecological decision problems frequently require the optimization of a sequence of actions over time where actions may have both immediate and downstream effects. dynamic programming can solve such problems only if the dimensionality is sufficiently low. approximate dynamic programming (ADP) provides a suite of methods applicable to problems of arbitrary complexity at the expense of guaranteed optimality. The most easily generalized method is the look-ahead policy: a brute-force algorithm that identifies reasonable actions by constructing and solving a series of temporally truncated approximations of the full problem over a defined planning horizon. We develop and apply this approach to a pest management problem inspired by the Mediterranean fruit fly, Ceratitis capitata. The model aims to minimize the cumulative costs of management actions and medfly-induced losses over a single 16-week season. The medfly population is stage-structured and grows continuously while management decisions are made at discrete, weekly intervals. For each week, the model chooses between inaction, insecticide application, or one of six sterile insect release ratios. Look-ahead policy performance is evaluated over a range of planning horizons, two levels of crop susceptibility to medfly and three levels of pesticide persistence. In all cases, the actions proposed by the look-ahead policy are contrasted to those of a myopic policy that minimizes costs over only the current week. We find that look-ahead policies always out-performed a myopic policy and decision quality is sensitive to the temporal distribution of costs relative to the planning horizon: it is beneficial to extend the planning horizon when it excludes pertinent costs. However, longer planning horizons may reduce decision quality when major costs are resolved imminently. ADP methods such as the look-ahead-policy-based approach developed here render questions intractable to dynamic programming amenable to inference but should be

关键词： approximate dynamic programming Ceratitis capitata continuous state space dynamic programming look-ahead policy sequential decision problem stage structure sterile insect release

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Learning Output Reference Model Tracking for Higher-Order Nonlinear Systems with Unknown dynamics

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ALGORITHMS 2019年第6期12卷

作者： Radac, Mircea-Bogdan Lala, Timotei Politehn Univ Timisoara Dept Automat & Appl Informat 2 Bd V Parvan Timisoara 300223 Romania

Linearly and nonlinearly parameterized approximate dynamic programming approaches used for output reference model (ORM) tracking control are proposed. The ORM tracking problem is of significant interest in practice since, with a linear ORM, the closed-loop control system is indirectly feedback linearized and value iteration (VI) offers the means to achieve ORM tracking without using process dynamics. Ranging from linear to nonlinear parameterizations, a successful approximate VI implementation for continuous state-action spaces depends on several key parameters such as: problem dimension, exploration of the state-action space, the state-transitions dataset size, and suitable selection of the function approximators. We show that using the same transitions dataset and under a general linear parameterization of the Q-function, high performance ORM tracking can be achieved with an approximate VI scheme, on the same performance level as that of a neural-network (NN)-based implementation that is more complex and takes significantly more time to learn. However, the latter proves to be more robust to hyperparameters selection, dataset size, and to exploration strategies, recommending it as the de facto practical implementation. The case study is aimed at ORM tracking of a real-world nonlinear two inputs-two outputs aerodynamic process with ten internal states, as a representative high order system.

关键词： approximate dynamic programming reinforcement learning data-driven control model-free control reference trajectory tracking output reference model multivariable control aerodynamic rotor system neural networks learning systems

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A Bayesian Machine Learning Approach for Optimizing dynamic Treatment Regimes

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2018年第523期113卷 1255-1267页

作者： Murray, Thomas A. Yuan, Ying Thall, Peter F. Univ Texas MD Anderson Canc Ctr Dept Biostat Houston TX 77030 USA

Medical therapy often consists of multiple stages, with a treatment chosen by the physician at each stage based on the patient's history of treatments and clinical outcomes. These decisions can be formalized as a dynamic treatment regime. This article describes a new approach for optimizing dynamic treatment regimes, which bridges the gap between Bayesian inference and existing approaches, like Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, in reverse sequential order. Each model uses as a response variable the remaining payoff assuming optimal actions are taken at subsequent stages, and as covariates the current history and relevant actions at that stage. The key difficulty is that the optimal decision rules at subsequent stages are unknown, and even if these decision rules were known the relevant response variables may be counterfactual. However, posterior distributions can be derived from the previously fitted regression models for the optimal decision rules and the counterfactual response variables under a particular set of rules. The proposed approach averages over these posterior distributions when fitting each regression model. An efficient sampling algorithm for estimation is presented, along with simulation studies that compare the proposed approach with Q-learning. Supplementary materials for this article are available online.

关键词： approximate dynamic programming Backward induction Bayesian additive regression trees Gibbs sampling Potential outcomes

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Path-based dynamic Pricing for Vehicle Allocation in Ridesharing Systems with Fully Compliant Drivers

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Transportation Research Procedia 2019年 38卷 77-97页

作者： Chao Lei Zhoutong Jiang Yanfeng Ouyang Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign 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.

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

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approximate dynamic programming for dynamic capacity allocation with multiple priority levels

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IIE TRANSACTIONS 2011年第2期43卷 129-142页

作者： Erdelyi, Alexander Topaloglu, Huseyin Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA

This article considers a quite general dynamic capacity allocation problem. There is a fixed amount of daily processing capacity. On each day, jobs of different priorities arrive randomly and a decision has to made about which jobs should be scheduled on which days. Waiting jobs incur a holding cost that is a function of their priority levels. The objective is to minimize the total expected cost over a finite planning horizon. The problem is formulated as a dynamic program, but this formulation is computationally difficult as it involves a high-dimensional state vector. To address this difficulty, an approximate dynamic programming approach is used that decomposes the dynamic programming formulation by the different days in the planning horizon to construct separable approximations to the value functions. Value function approximations are used for two purposes. First, it is shown that the value function approximations can be used to obtain a lower bound on the optimal total expected cost. Second, the value function approximations can be used to make the job scheduling decisions over time. Computational experiments indicate that the job scheduling decisions made by the proposed approach perform significantly better than a variety of benchmark strategies.

关键词： approximate dynamic programming capacity allocation scheduling

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approximate dynamic programming with state aggregation applied to UAV perimeter patrol

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2011年第12期21卷 1396-1409页

作者： Krishnamoorthy, K. Pachter, M. Darbha, S. Chandler, P. USAF Control Design & Anal Branch Res Lab RBCA Wright Patterson AFB OH 45433 USA USAF Inst Technol Dept Elect Engn Wright Patterson AFB OH 45433 USA Texas A&M Univ Dept Mech Engn College Stn TX 77843 USA

One encounters the curse of dimensionality in the application of dynamic programming to determine optimal policies for large-scale controlled Markov chains. In this paper, we provide a reward-based aggregation method to construct suboptimal policies for a perimeter surveillance control problem which gives rise to a large scale Markov chain. The novelty of this approach lies in circumventing the need for value iteration over the entire state space. Instead, the state space is partitioned and the value function is approximated by a constant over each partition. We associate a meta-state with each partition, where the transition probabilities between these meta-states are known. The state aggregation approach results in a significant reduction in the computational burden and lends itself to value iteration over the aggregated state-space. We provide bounds to assess the quality of the approximation and give numerical results that support the proposed methodology. Published 2011. This article is a US Government work and is in the public domain in the USA.

关键词： controlled Markov chain perimeter patrol approximate dynamic programming state aggregation

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Automated Feature Synthesis From Relational Database For Data Science Related Problems 16

Automated Feature Synthesis From Relational Database For Dat...

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16th International Conference on Frontiers of Information Technology (FIT)

作者： Fatima, Afifa Khan, Faizan Ali Raza, Aneeq Kamran, Abdul Basit Sir Syed CASE Inst Technol Comp Sci Islamabad Pakistan

ISBN: (纸本)9781538693551

Feature Synthesis is the most important aspect of Data Science. This process requires domain knowledge of the data to produce meaningful features that support decision-making algorithms. The biggest technical obstacle which a data scientist must subdue is the synthesis of such features that would assist AI algorithms to predict the target variable with the least error. Feature synthesis is a crucial step toward the application of machine learning which consumes time and resources. Automated Feature Synthesis obviates the tedious task of data scientists. Data Science Machine and One Button Machine are pioneering researches that explored the possibility of feature synthesis from raw relational data by applying linear exploratory algorithms which increase the dimensionality of the feature matrix exponentially, and hence, they consume a lot of time and memory. Our research revolves around the process of feature synthesis from the structured and unstructured data types for relational data which will provide an effective automation of a hectic manual task for the non-experts consuming a reasonable amount of resources. We devised a solution to reduce the time complexity of the operation by reducing dimensionality while considering the target variable by using approximate dynamic programming. The model is designed utilizing the concept of sparse reward setting, and it tries to achieve maximum reward as it explores the feature set. The target variable plays its part to compute the reward eventually leading to an optimum feature set. This process reduces data exploration time, cost and effort for both data scientists and non-experts.

关键词： approximate dynamic programming Automated Feature Synthesis

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ADP-based Home Energy Management System: A Case Study using DYNAMO

ADP-based Home Energy Management System: A Case Study using ...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Faqiry, M. N. Wang, L. Wu, H. Krishnamurthy, D. Palmintier, B. Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA Natl Renewable Energy Lab Power Syst Engn Ctr Golden CO 80401 USA

ISBN: (纸本)9781538677032

This paper explores Home Energy Management System (HEMS) algorithms to minimize household cost while maintaining comfort when faced with uncertain weather, and demand. Specifically, we consider a HEMS that optimizes forward looking schedules for a home's heating, ventilation, and air conditioning (HVAC);water heater (WH);and electric vehicle (EV) charging while considering uncertainty in outside temperature, hot water usage, and non-controllable load (NCL). We adopt a dynamic programming (DP) formulation and utilize the dynamic programming for Adaptive Modeling and Optimization (DYNAMO) toolkit to implement DP and approximate dynamic programming (ADP) algorithms. Simulation results under a single tariff plan compare the quality of the solution generated by ADP to that of DP, and show significant improvement in computation time while maintaining acceptable solution accuracy.

关键词： approximate dynamic programming demand response electric vehicle home energy management system stochastic mixed-integer programming

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