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dynamic assignment of a multi-skilled workforce in job shops: An approximate dynamic programming approach

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期306卷 1109-1125页

作者： Annear, Luis Mauricio Akhavan-Tabatabaei, Raha Schmid, Verena Univ Los Andes Dept Ind Engn Cra 1 18A-12 Bogota Colombia Sabanci Univ Sabanci Business Sch Istanbul Turkiye Univ Vienna Dept Business Adm Christian Doppler Lab Efficient Intermodal Transpo Oskar Morgenstern Pl 1 A-1090 Vienna Austria

We propose an approximate algorithm to dynamically assign a multi-skilled workforce to the stations of a job shop, with demand uncertainty and variability in the availability of the resources, to maximize *** proposed model is inspired by automotive glass manufacturing, where maximizing the surface area of manufactured safety glass during a given time frame is the key performance measure. We first develop the model of a traditional job shop with a set of stations, each with a particular number of machines, with distinct production performance levels, according to their utilization stage. Each product type needs to be processed on a subset of these stations according to a predefined sequence. Customers place their orders independently over time, specifying the units required of each product type. The inter-arrival of orders (demand) and processing times are assumed to be stochastic. We also suppose that the techni-cians have varied skill sets, according to which they can only work at a certain subgroup of stations, and variable availability depending on sick leave, vacations, etc. Hence, in order to maximize the predefined productivity index, the optimal assignment of technicians to the stations based on their skill sets and availability during each shift becomes a complex decision-making process. Given the stochastic and dynamic nature of this problem, we model the setting as a Markov Decision Process (MDP).Given its size, we propose to solve it using approximate dynamic programming (ADP). We address the exponential growth of the action space by using a hill-climbing algorithm for action selection. To show the performance and effectiveness of the proposed algorithm, we use real company data and compare the results of the algorithm with the current policy in use, as well as other proposed policies. Applying our proposed method resulted in an average improvement of 15% in productivity compared to the best performing benchmark policy.(c) 2022 Elsevier B.V.

关键词： dynamic programming approximate dynamic programming Workforce planning Job-shops

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Two-stage robust optimization of power cost minimization problem in gunbarrel natural gas networks by approximate dynamic programming

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Petroleum Science 2022年第5期19卷 2497-2517页

作者： Yi-Ze Meng Ruo-Ran Chen Tian-Hu Deng Department of Industrial Engineering Tsinghua UniversityBeijing100084China

In short-term operation of natural gas network,the impact of demand uncertainty is not *** address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas *** demands between pipelines and compressor stations are uncertain with a budget parameter,since it is unlikely that all the uncertain demands reach the maximal deviation *** solving the two-stage robust model we encounter a bilevel problem which is challenging to *** formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the *** results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming *** results also verify the advantage of robust model compared with deterministic model when facing *** findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties.

关键词： Natural gas Gunbarrel gas pipeline networks Robust optimization approximate dynamic programming

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approximate dynamic programming in Tracking Control of a Robotic Manipulator

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2016年第1期13卷 16-16页

作者： Szuster, Marcin Gierlak, Piotr Rzeszow Univ Technol Rzeszow Poland

This article focuses on the implementation of an approximate dynamic programming algorithm in the discrete tracking control system of the three-degrees of freedom Scorbot-ER 4pc robotic manipulator. The controlled system is included in an articulated robots group which uses rotary joints to access their work space. The main part of the control system is a dual heuristic dynamic programming algorithm that consists of two structures designed in the form of neural networks: an actor and a critic. The actor generates the suboptimal control law while the critic approximates the difference of the value function from Bellman's equation with respect to the state. The residual elements of the control system are the PD controller, the supervisory term and an additional control signal. The structure of the supervisory term derives from the stability analysis performed using the Lyapunov stability theorem. The control system works online, the neural networks' weights-adaptation procedure is performed in every iteration step, and the neural networks' preliminary learning process is not required. The performance of the control system was verified by a series of computer simulations and experiments performed using the Scorbot-ER 4pc robotic manipulator.

关键词： approximate dynamic programming Dual Heuristic dynamic programming Neural Network Robotic Manipulator Tracking Control

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An approximate dynamic programming approach to network-based scheduling of chemotherapy treatment sessions

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第12期62卷 4314-4330页

作者： Wenzel, Arturo Saure, Antoine Cataldo, Alejandro Rey, Pablo A. Sanchez, Cesar Pontificia Univ Catolica Chile Sch Engn Dept Ind & Syst Engn Santiago Chile Univ Ottawa Telfer Sch Management Ottawa ON Canada Pontificia Univ Catolica Chile Inst Math & Computat Engn Sch Engn Santiago Chile Univ Tecnol Metropolitana Fac Ingn Santiago Chile Univ Tecnol Metropolitana Programa Inst Fomento Invest Desarrollo Innovac Santiago Chile Pontificia Univ Catolica Chile Fac Med Dept Hematol Oncol Santiago Chile 55 Laurier Ave E Ottawa ON K1N 6N5 Canada

A solution approach is proposed for the interday problem of assigning chemotherapy sessions at a network of treatment centres with the goal of increasing the cost-efficiency of system-wide capacity use. This network-based scheduling procedure is subject to the condition that both the first and last sessions of a patient's treatment protocol are administered at the same centre the patient is referred to by their oncologist. All intermediate sessions may be administered at other centres. It provides a systematic way of identifying effective multi-appointment scheduling policies that exploit the total capacity of a networked system, allowing patients to be treated at centres other than their home centre. The problem is modelled as a Markov decision process which is then solved approximately using techniques of approximate dynamic programming. The benefits of the approach are evaluated and compared through simulation with the existing manual scheduling procedures at two treatment centres in Santiago, Chile. The results suggest that the approach would obtain a 20% reduction in operating costs for the whole system and cut existing first-session waiting times by half. A key conclusion, however, is that a network-based scheduling procedure brings no real benefits if it is not implemented in conjunction with a proactive assignment policy like the one proposed in this paper.

关键词： OR in health services chemotherapy scheduling markov decision processes approximate dynamic programming linear programming simulation

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From Single Commodity to Multiattribute Models for Locomotive Optimization: A Comparison of Optimal Integer programming and approximate dynamic programming

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TRANSPORTATION SCIENCE 2016年第2期50卷 366-389页

作者： Bouzaiene-Ayari, Belgacem Cheng, Clark Das, Sourav Fiorillo, Ricardo Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Norfolk Southern Corp Atlanta GA 30309 USA

We present a general optimization framework for locomotive models that captures different levels of detail, ranging from single and multicommodity flow models that can be solved using commercial integer programming solvers, to a much more detailed multiattribute model that we solve using approximate dynamic programming (ADP). Both models have been successfully implemented at Norfolk Southern for different planning applications. We use these models, presented using a common notational framework, to demonstrate the scope of different modeling and algorithmic strategies, all of which add value to the locomotive planning problem. We demonstrate how ADP can be used for both deterministic and stochastic models that capture locomotives and trains at a very high level of detail.

关键词： approximate dynamic programming locomotive planning

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Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2016年第Sep.期91卷 178-210页

作者： Yin, Jiateng Tang, Tao Yang, Lixing Gao, Ziyou Ran, Bin Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Univ Wisconsin Dept Civil & Environm Engn Madison WI 53706 USA

In a heavily congested metro line, unexpected disturbances often occur to cause the delay of the traveling passengers, infeasibility of the current timetable and reduction of the operational efficiency. Due to the uncertain and dynamic characteristics of passenger demands, the commonly used method to recover from disturbances in practice is to change the timetable and rolling stock manually based on the experiences and professional judgements. In this paper, we develop a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains. To capture the complexity of passenger traveling characteristics, the arriving ratio of passengers at each station is modeled as a non-homogeneous Poisson distribution, in which the intensity function is treated as time-varying origin-to-destination passenger demand matrices. By considering the number of on-board passengers, the total energy usage is modeled as the difference between the tractive energy consumption and the regenerative energy. Then, we design an approximate dynamic programming based algorithm to solve the proposed model, which can obtain a high-quality solution in a short time. Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Metro train rescheduling Passenger delay Energy efficiency approximate dynamic programming Stochastic programming

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Bayesian Exploration for approximate dynamic programming

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OPERATIONS RESEARCH 2019年第1期67卷 198-214页

作者： Ryzhov, Ilya O. Mes, Martijn R. K. Powell, Warren B. van den Berg, Gerald Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA Univ Maryland Inst Syst Res A James Clark Sch Engn College Pk MD 20742 USA Univ Twente Ind Engn & Business Informat Syst NL-7500 AE Enschede Netherlands Princeton Univ Operat Res & Financial Engn Princeton NJ 08540 USA

approximate dynamic programming (ADP) is a general methodological framework for multistage stochastic optimization problems in transportation, finance, energy, and other domains. We propose a new approach to the exploration/exploitation dilemma in ADP that leverages two important concepts from the optimal learning literature: first, we show how a Bayesian belief structure can be used to express uncertainty about the value function in ADP;second, we develop a new exploration strategy based on the concept of value of information and prove that it systematically explores the state space. An important advantage of our framework is that it can be integrated into both parametric and non-parametric value function approximations, which are widely used in practical implementations of ADP. We evaluate this strategy on a variety of distinct resource allocation problems and demonstrate that, although more computationally intensive, it is highly competitive against other exploration strategies.

关键词： approximate dynamic programming optimal learning Bayesian learning correlated beliefs value of information

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An approximate dynamic programming Algorithm for Large-Scale Fleet Management: A Case Application

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TRANSPORTATION SCIENCE 2009年第2期43卷 178-197页

作者： Simao, Hugo P. Day, Jeff George, Abraham P. Gifford, Ted Nienow, John Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Schneider Natl Green Bay WI 54306 USA

We addressed the problem of developing a model to simulate at a high level of detail the movements of over 6,000 drivers for Schneider National, the largest truckload motor carrier in the United States. The goal of the model was not to obtain a better solution but rather to closely match a number of operational statistics. In addition to the need to capture a wide range of operational issues, the model had to match the performance of a highly skilled group of dispatchers while also returning the marginal value of drivers domiciled at different locations. These requirements dictated that it was not enough to optimize at each point in time (something that could be easily handled by a simulation model) but also over time. The project required bringing together years of research in approximate dynamic programming, merging math programming with machine learning, to solve dynamic programs with extremely high-dimensional state variables. The result was a model that closely calibrated against real-world operations and produced accurate estimates of the marginal value of 300 different types of drivers.

关键词： fleet management truckload trucking approximate dynamic programming driver management

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Region-based approximation in approximate dynamic programming

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INTERNATIONAL JOURNAL OF CONTROL 2024年第2期97卷 306-315页

作者： Sardarmehni, Tohid Song, Xingyong Calif State Univ Coll Engn & Comp Sci Dept Mech Engn Northridge CA 77843 USA Texas A&M Univ Coll Engn Dept Engn Technol & Ind Distribut College Stn TX 77843 USA Texas A&M Univ Coll Engn Dept Mech Engn College Stn TX 77843 USA Texas A&M Univ Coll Engn Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Coll Engn Dept Mech Engn Dept Elect & Comp EngnDept Engn Technol & Ind Dis College Stn TX 77843 USA

This paper introduces a region-based approximation method to solve optimal control problems with approximate dynamic programming (ADP). The backbone of the proposed solution is partitioning the domain of training to smaller regions in which the value function varies slowly. Afterward, for each region, a Linear in Parameter Neural Network (LIPNN) is trained to capture the behaviour of the value function in that region. It is shown that the method improves the precision in value function approximation, which leads to improvement in the performance of the closed-loop system. Meanwhile, the possibility of expanding the domain of training in ADP solutions by region-based approximation is discussed. At last, it is shown how the method can potentially eliminate the need for trial & error to select a proper neural network in classical ADP solutions.

关键词： Region-based approximation approximate dynamic programming optimal control

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What You Should Know About approximate dynamic programming

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NAVAL RESEARCH LOGISTICS 2009年第3期56卷 239-249页

作者： Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well-known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. (C) 2009 Wiley Periodicals, Inc. Naval Research Logistics 56: 239-249,2009

关键词： approximate dynamic programming reinforcement learning neuro-dynamic programming stochastic optimization Monte Carlo simulation

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