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Joint pricing and inventory management under servitisation

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2020年第6期71卷 893-909页

作者： Xu, Chao Duan, Yongrui Huo, Jiazhen Tongji Univ Sch Econ & Management Sipring Rd Shanghai 200092 Peoples R China

We analyse a problem involving the joint pricing of a product and associated service, together with the management of inventory under servitisation. For a finite planning horizon, the firm sells both a product and a product-centric service whose market size depends on the past sales of the product. At the beginning of each period, the firm simultaneously decides the price of the product, the price of the service, and the replenishment quantity. We prove that a modified base-stock list-price policy is optimal to this problem. In addition, we find that there is a trade-off between realising current profit from the product and stimulating demand to increase future profit, and that overlooking this trade-off results in overall loss of profit for the firm. Moreover, the optimal policy that takes the trade-off into account leads to a lower optimal product price compared with a myopic policy, and the optimal price increases as a function of the past sales of the product.

关键词： Pricing inventory management stochastic dynamic programming servitisation product-service system

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An alternative approach to the operation of multinational reservoir systems:: Application to the Amistad & Falcon system (Lower Rio Grande Rio Bravo)

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WATER RESOURCES MANAGEMENT 2007年第4期21卷 677-698页

作者： Serrat-Capdevila, Aleix Valdes, Juan B. Univ Arizona Dept Hydrol & Water Resources Tucson AZ 85721 USA Univ Arizona SAHRA Sustainabil Semiarid Hydrol & Riparian Area Tucson AZ 85721 USA Univ Arizona Dept Civil Engn & Engn Mech Tucson AZ 85721 USA

An optimization approach for the operation of international multi-reservoir systems is presented. The approach uses stochastic dynamic programming (SDP) algorithms - both steady-state and real-time - to develop two models. In the first model, the reservoirs and flows of the system are aggregated to yield an equivalent reservoir, and the obtained operating policies are disaggregated using a non-linear optimization procedure for each reservoir and for each nation's water balance. In the second model a multi-reservoir approach is applied, disaggregating the releases for each country's water share in each reservoir. The non-linear disaggregation algorithm uses SDP-derived operating policies as boundary conditions for a local time-step optimization. Finally, the performance of the different approaches and methods is compared. These models are applied to the Amistad-Falcon International Reservoir System as part of a binational dynamic modeling effort to develop a decision support system tool for a better management of the water resources in the Lower Rio Grande Basin, currently enduring a severe drought.

关键词： stochastic dynamic programming multinational reservoir systems multi-reservoir modeling approach country water budgets quantitative model comparison

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Modeling uncertainty in limit order execution

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2016年第1-3期31卷 143-150页

作者： Agliardi, Rossella Univ Bologna Dept Math IMATI CNR I-40126 Pavia Italy

A model for limit order execution is developed where several sources of uncertainty are taken into account. We focus on the optimal trading strategy of an investor who has to buy a block of shares throughout the submission of limit orders. This trading problem is explicitly solved and we analyze how the state of the limit order book and the investor's subjective beliefs affect the optimal strategy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Limit orders stochastic dynamic programming Martingales Fuzzy numbers

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Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system

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APPLIED NUMERICAL MATHEMATICS 2008年第2期58卷 180-194页

作者： Kenne, J. P. Nkeungoue, L. J. Univ Quebec Ecole Technol Super Dept Engn Mech Lab Prod Technol Integrat Montreal PQ H3C 1K3 Canada

This paper deals with the control of corrective and preventive maintenance rates in the production planning of a manufacturing system with machines subject to random failures and repairs. The introduction of preventive and corrective maintenance will increase the availability of the production system which guarantees the improvement of the system's productivity if the production planning is well done. The objective of this study is to minimize a discounted overall cost consisting of maintenance cost, inventory holding and backlog cost. The decision variables are the production, the machine preventive and repair rates which influence the inventory levels and the system capacity respectively. In the proposed model. the failure rate of a machine depends on its age;hence, the corrective and preventive maintenance policies are machine-age dependent. A computational algorithm, based on numerical methods, is used for solving the optimal control problem. Finally, a numerical example and a sensitive analysis are presented to illustrate the usefulness of the proposed approach. The structure of the optimal control policy is presented and extensions to more complex manufacturing systems are discussed. (c) 2006 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： corrective maintenance preventive maintenance production planning stochastic dynamic programming numerical methods

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Assessing hydropower operational profitability considering energy and reserve markets

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IET RENEWABLE POWER GENERATION 2017年第13期11卷 1640-1647页

作者： Helseth, Arild Fodstad, Marte Askeland, Magnus Mo, Birger Nilsen, Odd Bjarte Ignacio Perez-Diaz, Juan Chazarra, Manuel Guisandez, Ignacio SINTEF Energy Res Sem Saelands Vei 11 N-7465 Trondheim Norway Lyse Prod AS N-4069 Stavanger Norway Tech Univ Madrid UPM Dept Hydraul Energy & Environm Engn Madrid 28040 Spain

The authors analyse the operational profitability of a hydropower system selling both energy and reserve capacity in a competitive market setting. A mathematical model based on stochastic dynamic programming is used to compute the water values for the system considering different power plant configurations. The uncertainties in inflow and both energy and reserve capacity prices are considered through a discrete Markov chain. Subsequently, the system operation is simulated based on the obtained water values to assess system performance and expected revenues from the two markets. The model is applied in a case study for a Norwegian hydropower producer, showing how the power plant operation changes and profitability increases when considering sale of reserve capacity. The authors emphasise on how the water values are influenced by the opportunity to sell reserve capacity, and assess how the representation of non-convex relationships in the water value computations as well as simulation influence the profitability.

关键词： profitability power generation economics power markets hydroelectric power stations hydroelectric power stochastic programming dynamic programming pricing hydropower operational profitability assessment energy markets reserve markets energy capacity prices reserve capacity prices competitive market setting mathematical model stochastic dynamic programming water value computation power plant configurations Norwegian hydropower producer

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Hybrid Model Predictive Control for Unmanned Ground Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1537-1546页

作者： Khan, Subhan Guivant, Jose Li, Yonghui Liu, Wanchun Li, Xuesong Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ New South Wales Sydney Sch Mech & Mfg Engn Sydney NSW 2052 Australia Australian Natl Univ Coll Sci Canberra ACT 2601 Australia

This article presents a hybrid approach to enhance the path planning for unmanned ground vehicles (UGVs) by combining stochastic dynamic programming (SDP) based hybrid model predictive control (HMPC) with Dijkstra-based pseudo priority queues (PPQ). The proposed approach employs a platform-specific model that takes into account skid-slip effects, a global cost-to-go (CTG) function, and dynamic obstacles. To achieve efficient path planning on large maps, the proposed approach employs a Dijkstra algorithm and PPQ to generate the CTG function. Moreover, the HMPC process incorporates the latest CTG information, the vehicle model, and the perceived environment, encompassing both static and dynamic obstacles. Extensive simulations have been conducted to comprehensively evaluate the performance of the proposed technique. Through a comparative analysis against existing path planners, the results demonstrate the effectiveness and superiority of the hybrid approach.

关键词： Heuristic algorithms Vehicle dynamics Costs Planning Robots Optimal control Collision avoidance Cost-to-go function model predictive control motion planning stochastic dynamic programming unmanned ground vehicle

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Analysis of generation investment under different market designs

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IEEE TRANSACTIONS ON POWER SYSTEMS 2008年第3期23卷 859-867页

作者： Doorman, Gerard L. Botterud, Audun Norwegian Univ Sci & Technol Dept Elect Power Engn N-7493 Trondheim Norway Argonne Natl Lab Decis & Informat Sci Div CEEESA Argonne IL 60439 USA

In this paper a stochastic dynamic optimization model is used to analyze the effect of different market designs on generation investment and demand. The expansion decisions of profit-maximizing investors are simulated under four different market designs: energy only, capacity payment, capacity obligation, and capacity subscription. The results show that the overall social welfare is reduced compared to a centralized social welfare optimization for the first three policies. In particular, an energy only market with a low price cap leads to insufficient generation investments. Capacity payments and obligations give additional investment incentives and more generating capacity, but also result in a considerable transfer of wealth from consumers to producers due to the capacity payments. In contrast, the capacity subscription policy increases the social welfare, and both producers and consumers benefit. This is possible because capacity subscription explicitly utilizes differences in consumers' preferences for uninterrupted supply, This advantage must be weighed against the cost of implementation, which is not included in the model.

关键词： generation investment market design restructured power systems simulation stochastic dynamic programming

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stochastic Model Predictive Energy Management of Electric Trucks in Connected Traffic

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第4期72卷 4294-4307页

作者： Du, Wei Murgovski, Nikolce Ju, Fei Gao, Jingzhou Zhao, Shengdun Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Xi An Jiao Tong Univ Xian Key Lab Intelligent Equipment & Control Xian 710049 Peoples R China

This paper proposes a cost-effective power management strategy utilizing the data provided by V2I communication techniques for dual electric machine coupling propulsion trucks. We formulate a bilevel program where the high-level optimizes operation mode implicitly, while the low-level computes an explicit policy for power distribution of two electric machines. stochastic model predictive control (SMPC) strategy is employed at the highlevel, the performance of which highly depends on the prediction accuracy of future driving information. To establish a position dependent stochastic velocity predictor using limited amount of historical data, two improved approaches are developed: 1) Predictor using multiple features;2) Predictor combining data and model. Simulations are performed to validate the performance of the proposed predictors compared with a benchmark. The results show that the controllers using the proposed predictors can reduce driving cost by 3.36 % and 4.26 %, respectively.

关键词： Energy management Model predictive control stochastic dynamic programming Dual electric machine coupling powertrain Markov chain

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Maximizing a new quantity in sequential reserve selection

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ENVIRONMENTAL CONSERVATION 2014年第2期41卷 198-205页

作者： Schapaugh, Adam W. Tyre, Andrew J. Univ Nebraska Sch Nat Resources Lincoln NE 68510 USA

The fundamental goal of conservation planning is biodiversity persistence, yet most reserve selection methods prioritize sites using occurrence data. Numerous empirical studies support the notion that defining and measuring objectives in terms of species richness (where the value of a site is equal to the number of species it contains, or contributes to an existing reserve network) can be inadequate for maintaining biodiversity in the long-term. An existing site-assessment framework that implicitly maximized the persistence probability of multiple species was integrated with a dynamic optimization model. The problem of sequential reserve selection as a Markov decision process was combined with stochastic dynamic programming to find the optimal solution. The approach represents a compromise between representation-based approaches (maximizing occurrences) and more complex tools, like spatially-explicit population models. The method, the inherent problems and interesting conclusions are illustrated with a land acquisition case study on the central Platte River.

关键词： reserve selection Bayesian network Markov decision process stochastic dynamic programming reserve adequacy

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Optimal charging of an electric vehicle using a Markov decision process

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APPLIED ENERGY 2014年 123卷 1-12页

作者： Iversen, Emil B. Morales, Juan M. Madsen, Henrik Tech Univ Denmark DTU Compute DK-2800 Lyngby Denmark

The combination of electric vehicles and renewable energy is taking shape as a potential driver for a future free of fossil fuels. However, the efficient management of the electric vehicle fleet is not exempt from challenges. It calls for the involvement of all actors directly or indirectly related to the energy and transportation sectors, ranging from governments, automakers and transmission system operators, to the ultimate beneficiary of the change: the end-user. An electric vehicle is primarily to be used to satisfy driving needs, and accordingly charging policies must be designed primarily for this purpose. The charging models presented in the technical literature, however, overlook the stochastic nature of driving patterns. Here we introduce an efficient stochastic dynamic programming model to optimally charge an electric vehicle while accounting for the uncertainty inherent to its use. With this aim in mind, driving patterns are described by an inhomogeneous Markov model that is fitted using data collected from the utilization of an electric vehicle. We show that the randomness intrinsic to driving needs has a substantial impact on the charging strategy to be implemented. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Electric vehicles Driving patterns Optimal charging Markov processes stochastic dynamic programming

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