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An economic analysis of ecological monitoring

ECOLOGICAL MODELLING 2005年第3-4期189卷 241-250页

作者： White, B Univ Western Australia Sch Agr & Resource Econ Perth WA 6009 Australia

A large number of schemes exist around the world to conserve or establish target natural vegetation communities. These include voluntary agri-environmental contracts, which aim to establish a target vegetation community by compensating farmers to reduce stocking rates. These contracts initiate a stochastic vegetation succession which increases the probability of establishing the target vegetation community. To ensure the scheme is achieving its objectives, regulators monitor the vegetation succession and decide whether a contract should stop or continue. If vegetation succession can be represented by a Markov chain, the regulator's problem of when to monitor, the best monitoring method and when to stop or continue a contract can be solved by a partially observed Markov decision process (POMDP). The results, for the conservation and restoration of heather Moorland in the Cambrian Mountains of Wales, show that the frequency and quality of monitoring depends upon monitoring costs and the regulators prior probabilities for the vegetation state. (c) 2005 Elsevier B.V. All fights reserved.

关键词： environmental monitoring stochastic dynamic programming POMDP Markov chain

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Optimal production and admission control for a stochastic SOM system with demands for product and PSS

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第23-24期51卷 7270-7288页

作者： Wang, Kangzhou Jiang, Zhibin Li, Na Geng, Na Shanghai Jiao Tong Univ Sino US Global Logist Inst Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Logist Management Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Res Ctr Serv Sci & Enterprise Innovat Shanghai 200030 Peoples R China

In decades, many traditional manufacturing firms are making the transition from products manufacturers to the providers of product-service systems (PSSs). The new business mode to support this transition is termed service-oriented manufacturing (SOM) paradigm by researchers and practitioners. In this paper, we address the problem of optimal control for a SOM system in which one factory manufactures products in the first stage and then one service centre realises PSSs by adding some product-based services on the products in the second stage. There are two-class external demands: demands for products and PSSs. The PSSs orders have higher priority and are fully accepted whereas the product orders can either be accepted or rejected. The optimal dynamic admission control of the product orders and the optimal dynamic production control of products are examined simultaneously. We first model the system in the context of a simple production-inventory-queue system and formulate the optimal integrated control problem as a continuous Markov decision process in which the objective is to maximise the profit of the firm. Then we characterise the optimal integrated dynamic control by two state-dependent threshold functions. The monotonicity of two optimal control curves verifies that, to achieve an effective management for the SOM system, the controls of manufacturing subsystem and service subsystem in the SOM system must be coo. We also illustrate the impacts of the revenues of both product orders and PSS orders on the optimal policy numerically. It is interesting that the optimal control policy is independent of the revenue of PSS order.

关键词： service-oriented manufacturing inventory control yield management stochastic dynamic programming Markov decision process

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Improved delivery policies for future drone-based delivery systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期294卷 1181-1201页

作者： Chen, Heng Hu, Zhangchen Solak, Senay Univ Nebraska Coll Business Lincoln NE 68583 USA Univ Massachusetts Isenberg Sch Management Amherst MA 01003 USA

It is expected that commercial use of drones in the near future will involve delivery service operations by e-commerce companies. We consider relevant strategic and tactical decisions that these retailers will face in drone-based delivery operations, and derive policies on when to offer drone delivery, what delivery capacity to maintain, and what amount to charge for such deliveries. To this end, we develop a Markov decision process (MDP) framework, and introduce two heuristic procedures, through which near-optimal closed-form solutions can be obtained. The results are aimed at helping online retailers to determine in real time whether and to what extent to offer drone-based delivery for given product categories in different service zones. In addition, we study delivery fee structures and identify drone-based delivery pricing strategies under two widely used delivery pricing schemes. For capacity planning decisions, we describe an algorithm to identify the fleet size to utilize to fulfill uncertain demand in a given service region. We also identify structural characteristics on how these decisions and the expected profit are affected by changes in various problem parameters, which can generate generic insights on drone-based delivery operations for e-commerce companies. We find that retailers should prioritize more profitable items when allocating drone delivery capacity, and invest in adding more drones when per order opportunity costs are higher and promised delivery time thresholds are shorter. Retailers can potentially boost their net profits by increasing the effective promised delivery time threshold and/or decreasing the effective delivery delay costs and per order opportunity costs. (c) 2021 Elsevier B.V. All rights reserved.

关键词： E-commerce Drone-based delivery Capacity management Delivery fee stochastic dynamic programming

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An elective surgery scheduling problem considering patient priority

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COMPUTERS & OPERATIONS RESEARCH 2010年第6期37卷 1091-1099页

作者： Min, Daiki Yih, Yuehwern Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

This paper addresses a scheduling problem where patients with different priorities are scheduled for elective surgery in a surgical facility. which has a limited capacity. When the capacity is available, patients with a higher priority are selected from the waiting list and put on the schedule. At the beginning of each period, a decision of the number of patients to be scheduled is made based on the trade-offs between the cost for overtime work and the cost for surgery postponement. A stochastic dynamic programming model is formulated to address this problem. A structural analysis of the proposed model is conducted to understand the properties of an optimal schedule policy. Based on the structural analysis, bounds on feasible actions are incorporated into a value iteration algorithm, and a brief computation experiment shows the improvement in computational efficiency. Numerical examples show that the consideration of patient priority results in significant differences in surgery schedules from the schedule that ignores the patient priority. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Surgery scheduling problem Patient priority stochastic dynamic programming

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On traveling wave solutions to a Hamilton-Jacobi-Bellman equation with inequality constraints

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JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS 2013年第1期30卷 51-67页

作者： Ishimura, Naoyuki Sevcovic, Daniel Hitotsubashi Univ Grad Sch Econ Kunitachi Tokyo 1868601 Japan Comenius Univ Dept Appl Math & Stat Fac Math Phys & Informat Bratislava 84248 Slovakia

The aim of this paper is to construct and analyze solutions to a class of Hamilton-Jacobi-Bellman equations with range bounds on the optimal response variable. Using the Riccati transformation we derive and analyze a fully nonlinear parabolic partial differential equation for the optimal response function. We construct monotone traveling wave solutions and identify parametric regions for which the traveling wave solution has a positive or negative wave speed.

关键词： Hamilton-Jacobi-Bellman equation Traveling wave solution Riccati transformation stochastic dynamic programming

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A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第1期258卷 216-229页

作者： Goodson, Justin C. Thomas, Barrett W. Ohlmann, Jeffrey W. St Louis Univ John Cook Sch Business Dept Operat & Informat Technol Management 3674 Lindell Blvd St Louis MO 63108 USA Univ Iowa Tippie Coll Business Dept Management Sci Iowa City IA 52242 USA

Rollout algorithms have enjoyed success across a variety of domains as heuristic solution procedures for stochastic dynamic programs (SDPs). However, because most rollout implementations are closely tied to specific problems, the visibility of advances in rollout methods is limited, thereby making it difficult for researchers in other fields to extract general procedures and apply them to different areas. We present a rollout algorithm framework to make recent advances in rollout methods more accessible to researchers seeking heuristic policies for large-scale, finite-horizon SDPs. We formalize rollout variants exploiting the pre- and post-decision state variables as a means of overcoming computational limitations imposed by large state and action spaces. We present a unified analytical discussion, generalizing results from the literature and introducing new results that relate the performance of the rollout variants to one another. Relative to the literature, our policy-based approach to presenting and proving results makes a closer connection to the underpinnings of dynamic programming. Finally, we illustrate our framework and analytical results via application to a dynamic and stochastic multi-compartment knapsack problem. (C) 2016 Published by Elsevier B.V.

关键词： dynamic programming Rollout algorithm stochastic dynamic programming Approximate dynamic programming

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Competitors-Aware stochastic Lap Strategy Optimisation for Race Hybrid Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3074-3089页

作者： Paparusso, Luca Riani, Manuel Ruggeri, Fabio Braghin, Francesco Politecn Milan Dept Mech Engn I-20133 Milan Italy STMicroelectronics R&D Div Cornaredo MI Italy Philip Morris Int Bologna Italy

World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in LMP1 race hybrid vehicles, has to respect energy usage constraints set by the technical regulation. Considering absence of competitors, i.e. traffic conditions, the optimal energy usage strategy for lap time minimisation is typically computed through a constrained optimisation problem. To the best of our knowledge, the majority of state-of-the-art works neglects competitors. This leads to a mismatch with the real world, where traffic generates considerable time losses. To bridge this gap, we propose a new framework to offline compute optimal strategies for the powertrain energy management considering competitors. Through analysis of the available data from previous events, statistics on the sector times and overtaking probabilities are extracted to encode the competitors' behaviour. Adopting a multi-agent model, the statistics are then used to generate realistic Monte Carlo (MC) simulation of their position along the track. The simulator is then adopted to identify the optimal strategy as follows. We develop a longitudinal vehicle model for the ego-vehicle and implement an optimisation problem for lap time minimisation in absence of traffic, based on Genetic Algorithms. Solving the optimisation problem for a variety of constraints generates a set of candidate optimal strategies. stochastic dynamic programming is finally employed to online choose the best strategy at the beginning of each lap, considering the competitors' initial position and their forecast future motion provided by the MC simulator. Our approach, validated on data from a real stint of race, allows to significantly reduce the lap time.

关键词： stochastic dynamic programming genetic algorithms lap time minimisation traffic race hybrid vehicles

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Using real options to determine optimal funding strategies for CO₂ capture, transport and storage projects in the European Union

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ENERGY POLICY 2014年第0期66卷 115-134页

作者： Eckhause, Jeremy Herold, Johannes Logist Management Inst Mclean VA 22102 USA Berlin Univ Technol Workgrp Infrastruct Policies D-10623 Berlin Germany

Several projects in the European Union (EU) are currently under development to implement the carbon capture, transport and storage (CCS) technology on a large scale and may be subject to public funding under EU support initiatives. These CCS projects may develop any combination of three types of operating levels: pilot, demonstration and full-scale, representing progressing levels of electric power generation capability. Several projects have commenced at the demonstration level, with full-scale commercial levels planned for approximately 2020. Taking the perspective of a funding agency, we employ a real options framework for determining an optimal project selection and funding strategy for the development of full-scale CCS plants. Specifically, we formulate and solve a stochastic dynamic program (SDP) for obtaining optimal funding solutions in order to achieve at least one successfully operating full-scale CCS plant by a target year. The model demonstrates the improved risk reduction by employing such a multi-stage competition. We then extend the model to consider two sensitivities: (1) the flexibility to spend that budget among the time periods and (2) optimizing the budget, but specifying each time period's allocation a priori. State size and runtimes of the SDP model are provided. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： CCS Real options stochastic dynamic programming

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Optimal gear shift strategies for fuel economy and driveability

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2013年第10期227卷 1398-1413页

作者： Viet Dac Ngo Navarrete, Jose A. Colin Hofman, Theo Steinbuch, Maarten Serrarens, Alex Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands Drivetrain Innovat BV Eindhoven Netherlands

This paper aims at designing optimal gear shift strategies for conventional passenger vehicles equipped with discrete ratio transmissions. In order to study quantitatively an optimal trade-off between the fuel economy and the driveability, the vehicle driveability is addressed in a fuel-optimal gear shift algorithm based on dynamic programming by three methods: method 1, weighted inverse of power reserve;method 2, constant power reserve;method 3, variable power reserve. Furthermore, another method based on stochastic dynamic programming is proposed to derive an optimal gear shift strategy over a number of driving cycles in an average sense, hence taking into account the vehicle driveability. In contrast with the dynamic-programming-based strategy, the obtained gear shift strategy based on stochastic dynamic programming is real time implementable. A comparative analysis of all proposed gear shift methods is given in terms of the improvements in the fuel economy and the driveability. The variable-power-reserve method achieves the highest fuel economy without sacrificing the driveability.

关键词： Gear shift strategy automated manual transmission power reserve optimal control dynamic programming fuel economy driveability stochastic dynamic programming

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dynamic CONTROL OF A SINGLE-SERVER SYSTEM WHEN JOBS CHANGE STATUS

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2018年第3期32卷 353-395页

作者： Zayas-Caban, Gabriel Ann, Hyun-soo Univ Michigan Ctr Healthcare Engn & Patient Safety Ann Arbor MI 48109 USA Univ Michigan Ross Sch Business Ann Arbor MI 48109 USA

From health care to maintenance shops, many systems must contend with allocating resources to customers or jobs whose initial service requirements or costs change when they wait too long. We present a new queueing model for this scenario and use a Markov decision process formulation to analyze assignment policies that minimize holding costs. We show that the classic c mu rule is generally not optimal when service or cost requirements can change. Even for a two-class customer model where a class 1 task becomes a class 2 task upon waiting, we show that additional orderings of the service rates are needed to ensure the optimality of simple priority rules. We then show that seemingly-intuitive switching curve structures are also not optimal in general. We study these scenarios and provide conditions under which they do hold. Lastly, we show that results from the two-class model do not extend to when there are n >= 3 customer classes. More broadly, we find that simple priority rules are not optimal. We provide sufficient conditions under which a simple priority rule holds. In short, allowing service and/or cost requirements to change fundamentally changes the structure of the optimal policy for resource allocation in queueing systems.

关键词： operations research queues stochastic dynamic programming stochastic modeling

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