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Management of R&D projects under uncertainty: A multidimensional approach to managerial flexibility

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2005年第2期52卷 269-280页

作者： Santiago, LP Bifano, TG Univ Fed Minas Gerais Dept Prod Engn BR-31270901 Belo Horizonte MG Brazil Boston Univ Dept Mfg Engn Boston MA 02215 USA

In this paper, we describe the practical application of a flexibility-based management approach to new product development, highlighting advantages, and limitations of this methodology. The model is concerned with the resolution of uncertainty over the product development life cycle and deals with technical, market, and cost factors all together. To this end, we consider a real options model, which uses multidimensional decision trees, to assess the development process of a high-technology product, namely, the Adaptive Optics Scanning Laser Ophthalmoscope. Moreover, we show how this project could be managed by estimating its value and determining optimal managerial actions to be taken at each review stage of the new product development process. Finally, we draw conclusions about this model's general utility and particular challenges associated with its use as a product development tool, and emphasize the need to consider a multidimensional model, instead of a single dimensional one.

关键词： decision trees managerial flexibility new product development R&D projects real options stochastic dynamic programming

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Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and dynamic Control Model (DCM)

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WATER RESOURCES MANAGEMENT 2017年第11期31卷 3543-3555页

作者： Zhou, Jianzhong Xie, Mengfei He, Zhongzhen Qin, Hui Yuan, Liu Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and stochastic dynamic programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic dynamic programming with hindsight and advantages and disadvantages of the two methods are analyzed.

关键词： Medium-termhydro generation scheduling Chance constrainedmodel stochastic inflow stochastic dynamic programming dynamic control model

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A Markov chain model of military personnel dynamics

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第6期54卷 1863-1885页

作者： Zais, Mark Zhang, Dan Off Secretary Def Washington DC USA Univ Colorado Leeds Sch Business Boulder CO 80309 USA

Personnel retention is one of the most significant challenges faced by the US Army. Central to the problem is understanding the incentives of the stay-or-leave decision for military personnel. Using three years of data from the US Department of Defense, we construct and estimate a Markov chain model of military personnel. Unlike traditional classification approaches, such as logistic regression models, the Markov chain model allows us to describe military personnel dynamics over time and answer a number of managerially relevant questions. Building on the Markov chain model, we construct a finite-horizon stochastic dynamic programming model to study the monetary incentives of stay-or-leave decisions. The dynamic programming model computes the expected pay-off of staying versus leaving at different stages of the career of military personnel, depending on employment opportunities in the civilian sector. We show that the stay-or-leave decisions from the dynamic programming model possess surprisingly strong predictive power, without requiring personal characteristics that are typically employed in classification approaches. Furthermore, the results of the dynamic programming model can be used as an input in classification methods and lead to more accurate predictions. Overall, our work presents an interesting alternative to classification methods and paves the way for further investigations on personnel retention incentives.

关键词： Markov chain estimation stochastic dynamic programming reenlistment propensity

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Optimality of myopic inventory policy for a single-product, multi-period, stochastic inventory problem with batch ordering and capacity commitment

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IIE TRANSACTIONS 2013年第8期45卷 925-938页

作者： Xu, Ningxiong Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA

The single-product, multi-period, stochastic inventory problem with batch ordering has been studied for decades. However, most existing research focuses only on the case in which there is no capacity constraint on the ordered quantity. This article generalizes that research to the case in which the capacity is purchased at the beginning of a planning horizon and the total ordered quantity over the planning horizon is constrained by the capacity. The objective is to minimize the expected total cost (the cost of purchasing capacity plus the minimum expected sum of the ordering, storage, and shortage costs incurred over the planning horizon for the given capacity). The conditions that ensure that a myopic ordering policy is optimal for any given capacity commitment are obtained. The structure of the expected total cost is characterized under these conditions and an algorithm is presented that can be used to calculate the optimal capacity commitment. A simulation study is performed to better understand the impact of various parameters on the performance of the model.

关键词： Batch ordering capacity inventory myopic policy stochastic dynamic programming

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Learning and Bayesian updating in long cycle made-to-order (MTO) production

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年 69卷 29-42页

作者： Womer, K. Li, H. Camm, J. Osterman, C. Radhakrishnan, R. Univ Missouri St Louis MO 63121 USA Wake Forest Univ Winston Salem NC 27109 USA Naval Personnel Res Study & Technol Millington TN USA Integral Analyt Cincinnati OH USA

We model production planning for made-to-order (MTO) manufacturing by choosing production rate to minimize expected discounted cost incurred up to a promised delivery date. Products that are MTO are often unique and customized. The associated learning curve slope and other production parameters cannot be precisely estimated before production starts. In this paper, a dynamic and adaptive approach to estimate the effects of learning and to optimize next period production is developed. This approach offers a closed-loop solution through stochastic dynamic programming. Monthly production data are used to update the joint probability distributions of production parameters via Bayesian methods. Our approach is illustrated using historical earned-value data from the Black Hawk Helicopter Program. Managerial insights are obtained and discussed. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Made-to-order Project management Production planning Learning stochastic dynamic programming Nonlinear programming Incomplete information process Bayesian updating

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Control Strategy for an Electromechanical Transmission Vehicle Based on a Double Markov Process

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2021年第3期22卷 761-770页

作者： Liu, Hui Xu, Riming Han, Lijin Xiong, Shanshan Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Natl Key Lab Vehicle Transmiss 5 Zhongguancun South St Beijing 100081 Peoples R China

Based on an analysis of the driving demand and system dynamics of heavy-duty vehicles equipped with electromechanical transmission (EMT), a double Markov model is put forward to represent drivers' power demand for driving and electricity. Transfer probability matrices are calculated by utilizing the maximum likelihood estimation method. A power distribution control strategy based on stochastic dynamic programming (SDP) is proposed. With economy being the optimization goal, the model for power allocation control based on SDP is established while regarding the engine torque, motor speeds, vehicle speed and state of charge (SOC) as state variables' engine speed and motor torques as control variables' and power demands as interference variables. The SDP problem is solved by an improved policy iteration algorithm based on value iteration and policy iteration algorithms.

关键词： Electro-mechanical transmission Control strategy stochastic dynamic programming Double Markov model

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Thermopower generation investment in Brazil - economic conditions

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ENERGY POLICY 2004年第1期32卷 91-100页

作者： Moreira, A Rocha, K David, P IPEA Inst Appl Econ Res Brazilian Govt BR-20020010 Rio De Janeiro Brazil FURNAS Centrais Elect Power Utili R Real Grandeza BR-22283900 Rio De Janeiro Brazil

One of the main questions in electricity market deregulation is the aptitude of private capital for investments in power generation. This is especially important in Brazil, whose load has a strong growth trend (approximate to 6% per year). Thermopower is an attractive alternative for expanding generation, as it is complementary in many aspects to hydropower, which supplies most Brazil's power at a very low price most of the time, but makes the system vulnerable to seasonal water variations. This paper studies the competitiveness of thermopower generation in Brazil under current regulations;assesses under the real options theory approach the conditions for investments in thermopower generation, and finally presents and discusses a hydropower generation schedule model. (C) 2002 Elsevier Ltd. All rights reserved.

关键词： stochastic dynamic programming real options theory investments in power generation

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Bayesian degradation modelling for spare parts inventory management

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2021年第1期32卷 31-49页

作者： Ruiz, Cesar Pohl, Edward Liao, Haitao Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA

Decision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the availability of spare parts significantly affects the operational costs of such systems. Current sensor technologies enable the condition monitoring of critical components and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management for a new system. We propose a stochastic dynamic program to minimize the expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this management setting. The proposed methodology finds the optimal time between long stoppages and optimal spare parts order quantity when the prior information about the degradation process is accurate. The methodology can be used to analyse the sensitivity of the optimal solution to changes in the accuracy and bias of the prior distributions of the model parameters, the cost structure and the number of machines in the system.

关键词： Bayesian degradation modelling stochastic dynamic programming spare parts inventory management

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Simultaneous control of maintenance and production rates of a manufacturing system with defective products

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JOURNAL OF INTELLIGENT MANUFACTURING 2012年第2期23卷 323-332页

作者： Njike, Achille N. Pellerin, Robert Kenne, Jean Pierre Ecole Polytech Dept Math & Ind Engn Montreal PQ H3C 3A7 Canada Ecole Technol Super Dept Mech Engn Montreal PQ Canada

In the past three decades, studies of simultaneous maintenance and production planning have been focusing on age-dependent machine failure and inventory. This paper presents the interaction between defective products and optimal control of production rate, lead time and inventory. Our aim is to minimize the expected discounted overall cost due to maintenance activities, inventory holding and backlogs. Through Condition-Based Maintenance, we monitor in a real time the manufacturing system's health by describing N operational states. We consider two maintenance states of a machine controlled by two decision variables: production and maintenance rates. The optimal policy is characterized by the dynamic programming solution to a piecewise deterministic optimal control problem. A numerical illustration and a sensitive analysis are developed with a set of parameters calibrated on an existing manufacturing system.

关键词： Condition-based maintenance Manufacturing systems stochastic dynamic programming Numerical methods

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An SDP approach for multiperiod mixed 0-1 linear programming models with stochastic dominance constraints for risk management

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COMPUTERS & OPERATIONS RESEARCH 2015年第Jun.期58卷 32-40页

作者： Escudero, Laureano F. Francisco Monge, Juan Romero Morales, Dolores Univ Rey Juan Carlos Dept Estadist & Invest Operat Madrid Spain Univ Miguel Hernandez Ctr Invest Operat Alicante Spain Copenhagen Business Sch Dept Econ Copenhagen Denmark

In this paper we consider multiperiod mixed 0-1 linear programming models under uncertainty. We propose a risk averse strategy using stochastic dominance constraints (SDC) induced by mixed-integer linear recourse as the risk measure. The SDC strategy extends the existing literature to the multistage case and includes both the first-order and second-order constraints. We propose a stochastic dynamic programming (SDP) solution approach, where one has to overcome the negative impact of the cross-scenario constraints on the decomposability of the model. In our computational experience we compare our SDP approach against a commercial optimization package, in terms of solution accuracy and elapsed time. We use supply chain planning instances, where procurement production, inventory, and distribution decisions need to be made under demand uncertainty. We confirm the hardness of the testbed, where the benchmark cannot find a feasible solution for half of the test instances while we always find one, and show the appealing tradeoff of SDP, in terms of solution accuracy and elapsed time, when solving medium-to-large instances. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Multiperiod stochastic mixed 0-1 linear programming Risk averse stochastic dominance constraints stochastic dynamic programming Cross-scenario constraints

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