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Technician routing and scheduling with employees' learning through implicit cross-training strategy

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2024年 271卷

作者： Chen, Xi Li, Kaiwen Lin, Sidian Ding, Xiaosong Beijing Foreign Studies Univ Int Business Sch Beijing 100089 Peoples R China Harvard Univ Grad Sch Arts & Sci Cambridge MA 02138 USA Univ Iowa Tippie Coll Business Dept Management Sci Iowa City IA 52242 USA Stockholm Univ Dept Comp & Syst Sci DECIDE SE-16407 Kista Sweden

With record high talent shortages and skill mismatches around the world, this paper investigates a variant of multi -period dynamic technician and routing problem that can be modeled as a Markov decision process. To deal with the double tradeoffs between the routing and service time costs, as well as the current and future costs, we propose an approximate dynamic programming (ADP) -based cost function approximation (CFA) algorithm - the implicit cross -training strategy (ICT). A two-phase routing and scheduling heuristic is developed to account for both employees' learning and future information, and to facilitate an efficient implementation of CFA. Extensive computational results show that ICT can provide a better solution in the current decision with a global view in comparison with the myopic strategy. In depth analysis demonstrates that ICT trains the workforce with more balanced skillsets and workloads, which ensures the flexibility of the workforce and helps buffer against the future uncertainties with substantial routing cost savings. Additionally, ICT has much more advantages in large-scale problems with more diversified service requests and randomly distributed customers.

关键词： Routing and scheduling approximate dynamic programming Learning Cost function approximation Workforce management

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The dynamic Freight Routing Problem for Less-Than-Truckload Carriers

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TRANSPORTATION SCIENCE 2023年第3期57卷 717-740页

作者： Baubaid, Ahmad Boland, Natashia Savelsbergh, Martin King Fahd Univ Petr & Minerals Ind & Syst Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

Less-than-truckload (LTL) carriers transport freight shipments from origins to destinations by consolidating freight using a network of terminals. As daily freight quantities are uncertain, carriers dynamically decide freight routes on the day of operations. We introduce the dynamic freight routing problem (DFRP) and model this problem as a Markov decision process (MDP). To overcome the curses of dimensionality of the MDP model, we introduce an approximate dynamic programming (ADP) solution approach that uses a lookup table to store value function approximations and present and compares a number of aggregation approaches that use features of the postdecision states (PDSs) to aggregate the PDS space and reduce the number of entries in the lookup table. Furthermore, because the decision subproblems are integer programs (IPs), we present a framework for integrating lookup tables into the decision subproblem IPs. This framework consists of (1) a modeling approach for the integration of lookup table value function approximations into subproblem IPs to form extended subproblem IPs;(2) a solution approach, PDS-IP-bounding, which decomposes the extended subproblem IPs into many smaller IPs and uses dynamic bounds to reduce the number of small IPs that have to be solved;and (3) an adaptation of the e-greedy exploration-exploitation algorithm for the IP setting. Our computational experiments show that despite the DFRP having high-dimensional PDS vectors, a two-dimensional aggregation of the space can produce policies that outperform standard myopic policies. Moreover, they demonstrate that the PDS-IP-bounding algorithm provides computational advantages over solving the extended subproblem IPs using a commercial solver.

关键词： less-than-truckload freight transportation approximate dynamic programming value function approximation load planning

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Identifying proactive ICU patient admission, transfer and diversion policies in a public-private hospital network

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期295卷 306-320页

作者： Marquinez, Jose Tomas Saure, Antoine Cataldo, Alejandro Ferrer, Juan-Carlos Pontificia Univ Catolica Chile Dept Ingn Ind & Sistemas Vicuna Mackenna 4860 Santiago Chile Univ Ottawa Telfer Sch Management 55 Laurier Ave E Ottawa ON K1N 6N5 Canada Pontificia Univ Catolica Chile Sch Engn Inst Math & Computat Engn Vicuna Mackenna 4860 Santiago Chile

Management of hospital beds is a high-impact issue for two-tier healthcare systems, due principally to their critical importance and high costs. Bed capacity in the public sector is generally insufficient to provide immediate care to all critical patients and thus a significant proportion of public expenditure is assigned to the diversion of patients for treatment in the private sector. We formulate and approximately solve a discounted infinite-horizon Markov Decision Process (MDP) that seeks to identify cost-effective policies for transferring ICU patients between hospitals or diverting them to private clinics. The solution approach employs an affine architecture for approximating the value function of the MDP model and solves the equivalent linear programming model using column generation. The approach can handle a high level of dimensionality, enabling it to consider the arriving patients' many different diagnostic groups and their corresponding lengths of stay. The decisions generated through this approach often differ from the intuitive ones produced in a typical day-by-day decision process, that does not consider the impact of the current day's decisions on the future performance of the system. In particular, the resulting policies will in many cases proactively transfer patients to a different public facility or divert them to a private one even though the hospital they first arrived at had beds available. The performance of the proposed method was evaluated by simulating a case study based on data from a hospital network in Santiago, Chile, producing savings of almost 49% due mostly to reduced usage of private services. (c) 2021 Elsevier B.V. All rights reserved.

关键词： OR in health services Intensive care units Hospital network Patient admission and transfer policies approximate dynamic programming

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Differential Temporal Difference Learning

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第10期66卷 4652-4667页

作者： Devraj, Adithya M. Kontoyiannis, Ioannis Meyn, Sean P. Stanford Univ Dept Elect Engn Stanford CA 94305 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Value functions derived from Markov decision processes arise as a central component of algorithms as well as performance metrics in many statistics and engineering applications of machine learning. Computation of the solution to the associated Bellman equations is challenging in most practical cases of interest. A popular class of approximation techniques, known as temporal difference (TD) learning algorithms, are an important subclass of general reinforcement learning methods. The algorithms introduced in this article are intended to resolve two well-known issues with TD-learning algorithms. Their slow convergence due to very high central limit theorem variance, and the fact that, for the problem of computing the relative value function, consistent algorithms exist only in special cases. First we show that the gradients of these value functions admit a representation that lends itself to algorithm design. Based on this result, a new class of differential TD-learning algorithms is introduced. For Markovian models on Euclidean space with smooth dynamics, the algorithms are shown to be consistent under general conditions. Numerical results show dramatic variance reduction in comparison to standard methods.

关键词： Approximation algorithms Markov processes Heuristic algorithms Convergence Mathematical model Machine learning algorithms Reinforcement learning approximate dynamic programming Poisson equation reinforcement learning stochastic optimal control temporal difference (TD) learning

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Policy Rollout Action Selection in Continuous Domains for Sensor Path Planning

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第4期57卷 2247-2264页

作者： Hoffmann, Folker Charlish, Alexander Ritchie, Matthew Griffiths, Hugh Fraunhofer FKIE D-53343 Wachtberg Germany UCL London WC1E 6BT England

Policy rollout is a method for the online computation of future costs in approximate dynamic programming and has been utilized for various problems, including sensor management. In previous work, it has predominately been applied to the selection of actions from discrete sets. In this article, we present methods for action selection from continuous sets and analyze their tradeoffs. The methods are evaluated on the problem of sensor path planning, with the intent of minimizing the time to localize an emitter using bearing measurements.

关键词： Path planning Uncertainty Time measurement Monte Carlo methods Markov processes Unmanned aerial vehicles Random variables approximate dynamic programming emitter localization Markov decision process (MDP) partially observable Markov decision process (POMDP) policy rollout sensor management sensor path planning stochastic optimization

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Warehousing platform's revenue management: A dynamic model of coordinating space allocation for self-use and rent

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期293卷 167-176页

作者： Shi, Ye Yu, Yugang Dong, Yuxuan Univ Sci & Technol China Sch Management 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China Shanghai Jiao Tong Univ Sino US Global Logist Inst 1954 Huashan Rd Shanghai 200030 Peoples R China

Recently, some giant retail platforms have begun to invest large amounts of money in building warehouses. With abundant warehousing space, these platforms are renting out space to third-party retailers (TPRs) with insufficient storage space. However, such rental programs generate a new operational problem for these platforms, namely, how to divide their space between space for self-use and space for rent. To address the optimal space allocation decisions for this operational problem, we study a finite horizon and periodic review warehouse model where some items are sold by the platform, and other items are sold by TPRs. More specifically, we start with a base model with two items and prove the optimality of a base stock policy for the base model and the monotonicity of the optimal space allocation decisions regarding several key system parameters (such as inventory and capacity parameters). We then study several extensions of the base model by considering demand forecasts, demand correlation, and multiple items (more than two). Particularly, when addressing the extension with multiple items, we propose a heuristic based on the idea of approximate dynamic programming to overcome the curse of dimensionality. To examine the effectiveness of our heuristic, we present a computational study that is based on real-life datasets provided by the largest furniture retail platform in China (Red-Star Macalline). The numerical results demonstrate that our heuristic has the potential to solve medium- and large-scale problems effectively and efficiently. (C) 2020 Elsevier B.V. All rights reserved.

关键词： E-commerce Space allocation Rent Warehouse revenue management approximate dynamic programming

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Managing Shutdown Decisions in Merchant Commodity and Energy Production: A Social Commerce Perspective

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2021年第2期23卷 311-330页

作者： Trivella, Alessio Nadarajah, Selvaprabu Fleten, Stein-Erik Mazieres, Denis Pisinger, David Swiss Fed Inst Technol Inst Transport Planning & Syst CH-8092 Zurich Switzerland Univ Illinois Coll Business Adm Chicago IL 60607 USA Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management N-7491 Trondheim Norway Birkbeck Univ London Dept Econ Math & Stat London WC1E 7HX England Tech Univ Denmark Dept Management Engn DK-2800 Lyngby Denmark

Problem definition: Merchant commodity and energy production assets operate in markets with volatile prices and exchange rates. Plant closures adversely affect societal entities beyond the specific plant being shut down, such as the parent company and the local community. Motivated by an aluminum producer, we study if mitigating these hard-to-assess broader impacts of a shutdown is financially viable using the plant's operating flexibility. Academic/practical relevance: Our social commerce perspective toward managing shutdown decisions deviates from the commonly used asset value maximization objective in merchant operations. Identifying operating policies that delay or decrease the likelihood of a shutdown without incurring a significant asset value loss supports socially responsible plant shutdown decisions. Methodology: We formulate a constrained Markov decision process to manage shutdown decisions and limit the probability of future plant closures. We provide theoretical support for approximating this intractable model using unconstrained stochastic dynamic programs with modified shutdown costs and explore two classes of operating policies. Our first policy leverages anticipated regret theory, and the second policy generalizes, using machine learning, production-margin heuristics used in practice. We compute the former and latter policies using a least squares Monte Carlo method and combining this method with binary classification, respectively. Results: Anticipated-regret policies possess desirable asymptotic properties absent in classification-based policies. On instances created using real data, anticipated-regret and classification-based policies outperform practice-based production-margin strategies. Significant reductions in shutdown probability and delays in plant closures are possible while incurring small asset value losses. Managerial implications: A plant's operating flexibility provides an effective lever to balance the social objective to reduce clos

关键词： merchant production energy and commodity operations socially responsible operations approximate dynamic programming least squares Monte Carlo shutdown decisions

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dynamic tuberculosis screening for healthcare employees

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COMPUTERS & OPERATIONS RESEARCH 2024年 169卷

作者： Kiani, Mahsa Isik, Tugce Eksioglu, Burak Pirrallo, Ronald G. Clemson Univ Dept Ind Engn Clemson SC 29631 USA Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA Univ South Carolina Dept Emergency Med Sch Med Greenville SC 29605 USA

Regular tuberculosis (TB) screening is required for healthcare employees. TB is a potentially deadly disease. Early detection prevents the spread of the disease and helps with treatment. Two types of TB diagnostic tests, which vary in terms of cost and accuracy, are available on the market. Thus, hospitals need to carefully consider the available screening options to effectively monitor TB prevalence at low cost. We develop the first optimization model and solution approach in the literature that can be used to obtain effective TB screening policies where testing decisions are differentiated based on employee characteristics. We categorize healthcare employees based on the department they work in, the specific job they do, and their history of vaccination against TB. We develop a Markov decision process (MDP) model to determine which TB test should be utilized for each employee category to minimize the total costs related to testing, undetected infections, and employees' time lost due to testing. Due to the size of the problem, we use approximate dynamic programming (ADP) to obtain near -optimal solutions. We analyze the ADP solutions under varying assumptions to develop a screening policy that specifies not only the type of the tests that should be used but also the frequency with which each test should be administered. We further examine the impact of system parameters such as costs and TB prevalence on our policy recommendation via a sensitivity analysis. The results indicate that our approach yields a simple policy that can be used by healthcare providers that do not have the expertise or the resources to develop and solve sophisticated optimization models on an ongoing basis.

关键词： Healthcare employee screening Tuberculosis testing Markov decision process approximate dynamic programming

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Robust Reinforcement Learning with Diffusion Wavelets

Robust Reinforcement Learning with Diffusion Wavelets

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作者： Seyedmazloom, Ali George Mason University

学位级别：Ph.D., Doctor of Philosophy

Reinforcement Learning is a method of learning from the environment by constantly observing it and evaluating its response to a set of actions. Long-term learning of a dynamic system for aforementioned interactions where its constituents evolve temporally, accumulates valuable knowledge about system’s history and actions taken. Reinforcement Learning (RL) takes advantage of this gathered knowledge to identify an optimal policy that essentially dictates which decision(s) should be made when the system is in a certain state in order to guide it to achieve the best objective value in the long run. Since system’s objective value is nothing but accumulated discounted rewards over various system states, it is crucial to define and retrieve rewards from the environment as genuinely as possible. However, there are several instances where we might be receiving noisy, corrupt, or even intentionally perturbed state perceptions that contain distorted rewards. In robotics for example, it is quite common to receive noisy feedback through faulty sensors or adverse environment because of rain, wind, lack of light, etc. The focus of this research is to study and utilize methods to properly handle intentionally corrupted discrete-state perceptions and their associated reward channel that are crafted in a way which will increase the likelihood of the model taking actions not in line with its genuine objectives but of an unknown adversary’s. This may seriously compromise RL model’s functionality and addressing it, is of utmost importance in Secure Machine Learning domain as it has been the case with Artificial Neural Network models targeted by the “Adversarial Attacks”. A value-based RL model needs to assess all possible decisions based on the quality of the upcoming states and these quality values (known as q-values) should be obtained from a robust process. While many state-of-the-art RL models use Artificial Neural Networks for providing state values to the RL agent, in this researc

关键词： approximate dynamic programming Diffusion wavelets Operations research Reinforcement learning Robust learning

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An ADP framework for flexibility and cost aggregation: Guarantees and open problems

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 234卷

作者： Bandeira, Maisa Beraldo Faulwasser, Timm Engelmann, Alexander TU Dortmund Univ Inst Energy Syst Energy Efficiency & Energy Econ i Dortmund Germany

With the increasing amount of Distributed Energy Resources (DERs), coordination of Distribution Grid Operators (DSOs) and Transmission Grid Operators (TSOs) is of paramount importance. Managing a large number of DERs at the TSO level is, however, challenging. To address this problem, flexibility aggregation is a topic of frequent research activities. Aggregation means to describe the combined flexibility of the DERs at the vertical grid coupling between DSO and TSO. Existing works are often limited with respect to guaranteeing feasibility, with respect to efficient numerical implementation, and in terms of quantification of the cost of DER usage. In the present paper, we investigate aggregation based on approximate dynamic programming (ADP). We propose efficient numerical aggregation schemes using tools from computational geometry thus avoiding the need to solve multiple OPF problems. We rely on different variants of the DistFlow model for radial grids, which are computationally efficient. This allows to model of current and voltage limits and enables the consideration of voltage dependencies in the aggregation. Furthermore, we propose a method for cost aggregation and identify open problems of flexibility aggregation.

关键词： Flexibility aggregation Congestion management Feasible operation region TSO-DSO coordination approximate dynamic programming

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