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Control of a production-inventory system with returns under imperfect advance return information

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期218卷 392-400页

作者： Flapper, S. D. P. Gayon, J. P. Vercraene, S. Grenoble Inst Technol Lab G SCOP F-38031 Grenoble France Tech Univ Eindhoven NL-5600 MB Eindhoven Netherlands

We consider a production-inventory system with product returns that are announced in advance by the customers. Demands and announcements of returns occur according to independent Poisson processes. An announced return is either actually returned or cancelled after a random return lead time. We consider both lost sale and backorder situations. Using a Markov decision formulation, the optimal production policy, with respect to the discounted cost over an infinite horizon, is characterized for situations with and without advance return information. We give insights in the potential value of this information. Also some attention is paid to combining advance return and advance demand information. Further applications of the model as well as topics for further research are indicated. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Reverse logistics Inventory control stochastic dynamic programming Advance return information

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Production planning of a hybrid manufacturing-remanufacturing system under uncertainty within a closed-loop supply chain

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2012年第1期135卷 81-93页

作者： Kenne, Jean-Pierre Dejax, Pierre Gharbi, Ali IRCCyN Ecole Mines Nantes Logist & Prod Syst Grp Dept Automat Control & Ind Engn F-44307 Nantes 3 France Univ Quebec Dept Mech Engn Lab Integrated Prod Technol Ecole Technol Super Montreal PQ H3C 1K3 Canada Univ Quebec Automated Prod Engn Dept Prod Syst Design & Control Lab Ecole Technol Super Montreal PQ H3C 1K3 Canada

This paper deals with the production planning and control of a single product involving combined manufacturing and remanufacturing operations within a closed-loop reverse logistics network with machines subject to random failures and repairs. While consumers traditionally dispose of products at the end of their life cycle, recovery of the used products may be economically more attractive than disposal, while remanufacturing of the products also pursues sustainable development goals. Three types of inventories are involved in this network. The manufactured and remanufactured items are stored in the first and second inventories. The returned products are collected in the third inventory and then remanufactured or disposed of. The objective of this research is to propose a manufacturing/remanufacturing policy that would minimize the sum of the holding and backlog costs for manufacturing and remanufacturing products. The decision variables are the production rates of the manufacturing and the remanufacturing machines. The optimality conditions are developed using the optimal control theory based on stochastic dynamic programming. A computational algorithm, based on numerical methods, is used for solving the optimal control problem. Finally, a numerical example and a sensitivity analysis are presented to illustrate the usefulness of the proposed approach. The structure of the optimal control policy is discussed depending on the value of costs and parameters and extensions to more complex reverse logistics networks are discussed. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Reverse logistics Closed-loop supply chains Manufacturing/remanufacturing Optimal control Production planning stochastic dynamic programming Numerical methods

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GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

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ROBOTICS AND AUTONOMOUS SYSTEMS 2012年第12期60卷 1457-1471页

作者： Thakur, Atul Svec, Petr Gupta, Satyandra K. Univ Maryland Dept Mech Engn College Pk MD 20742 USA Univ Maryland Syst Res Inst College Pk MD 20742 USA

This paper describes CPU based algorithms to compute state transition models for unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations of vehicle-wave interaction. A state transition model is a key component of the Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty. The USV trajectory planning problem is characterized by the presence of large and somewhat stochastic forces due to ocean waves, which can cause significant deviations in their motion. Feedback controllers are often employed to reject disturbances and get back on the desired trajectory. However, the motion uncertainty can be significant and must be considered in the trajectory planning to avoid collisions with the surrounding obstacles. In case of USV missions, state transition probabilities need to be generated on-board, to compute trajectory plans that can handle dynamically changing USV parameters and environment (e.g., changing boat inertia tensor due to fuel consumption, variations in damping due to changes in water density, variations in sea-state, etc.). The 6 DOF dynamics simulations reported in this paper are based on potential flow theory. We also present a model simplification algorithm based on temporal coherence and its CPU implementation to accelerate simulation computation performance. Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 min. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan. Using this approach, we are able to generate dynamically feasible trajectories for USVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles. (C) 2012 Elsevier B.V. All rights reserved.

关键词： USV Vehicle simulation Fluid-rigid interaction GP-GPU Trajectory planning MDP State transition map stochastic dynamic programming Motion uncertainty

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An Integer programming Approach for Evaluating R&D Funding Decisions With Optimal Budget Allocations

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2012年第4期59卷 679-691页

作者： Eckhause, Jeremy M. Gabriel, Steven A. Hughes, Danny R. Logist Management Inst Mclean VA 22102 USA Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA Univ Oklahoma Dept Hlth Adm & Policy Oklahoma City OK 73019 USA

The use of real options techniques for research and development (R&D) project selection to mitigate the uncertainties has been shown to increase overall project value. While recent approaches employing stochastic dynamic programs (SDP) produce optimal solutions for many applications, this approach does not easily accommodate the inclusion of an optimal a priori budget allocation or side constraints, since their formulations are scenario specific. We formulate an integer program (IP) whose solution is equivalent to previous SDP real options models but facilitates the incorporation of these additional features and may be solved using commercial IP solvers. This IP formulation can solve what would otherwise be a nested two-level problem where the lower level problem is an SDP. We then compare the performance of the IP to that received by the SDP using a case study from the literature.

关键词： Integer programming (IP) project selection real options research and development (R&D) portfolios stochastic dynamic programming two-level problems

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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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Joint pricing and inventory replenishment decisions with returns and expediting

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第1期216卷 105-112页

作者： Zhu, Stuart X. Univ Groningen Dept Operat Fac Econ & Business Groningen Netherlands

We study a single-item periodic-review model for the joint pricing and inventory replenishment problem with returns and expediting. Demand in consecutive periods are independent random variables and their distributions are price sensitive. At the end of each period, after the demand is realized, a buyer can return excess stocks to a supplier. Or, if there are stockouts, the buyer can place an expediting order at the supplier to reduce the amount of shortage. Unfilled demands are fully backlogged. We characterize the optimal dynamic policy that determines the pricing, inventory replenishment, and adjustment decisions in each period so that the total expected discounted profit is maximized. For a very general stochastic demand function, we can show that the optimal replenishment policy is a modified base-stock policy, the optimal pricing policy is a modified base-stock-list-price policy, and the optimal policy for inventory adjustment follows a dual-threshold policy. We further study the operational effect of returns and expediting. Analytical and numerical results demonstrate that returns and expediting lead to a significant profit increase in a number of situations, including limited supply capacity, sufficient flexibility of the expediting order, high demand uncertainty, and a price-sensitive market. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Pricing Inventory Return and expediting stochastic dynamic programming Optimization

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Optimal Inventory Control with Dual-Sourcing, Heterogeneous Ordering Costs and Order Size Constraints

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PRODUCTION AND OPERATIONS MANAGEMENT 2012年第3期21卷 564-575页

作者： Zhang, Wei Hua, Zhongsheng Benjaafar, Saif Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China Univ Minnesota Minneapolis MN 55455 USA

We consider a dual-sourcing inventory system, where procuring from one supplier involves a high variable cost but negligible fixed cost whereas procuring from the other supplier involves a low variable cost but high fixed cost, as well as an order size constraint. We show that the problem can be reduced to an equivalent single-sourcing problem. However, the corresponding ordering cost is neither concave nor convex. Using the notion of quasi-convexity, we partially characterize the structure of the optimal policy and show that it can be specified by multiple thresholds which determine when to order from each supplier and how much. In contrast to previous research, which does not consider order size constraints, we show that it is optimal to simultaneously source from both suppliers when the beginning inventory level is sufficiently low. We also show that the decision to source from the low-cost supplier is not monotonic in the inventory level. Our results require that the variable costs satisfy a certain condition which guarantees quasi-convexity. However, extensive numerical results suggest that our policy is almost always optimal when the condition is not satisfied. We also show how the results can be extended to systems with multiple capacitated suppliers.

关键词： inventory systems optimal policy dual-sourcing stochastic dynamic programming quasi-convexity

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A stochastic Control Approach to Optimally Designing Hierarchical Flash Sets in P300 Communication Prostheses

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2012年第1期20卷 102-112页

作者： Ma, Rui Aghasadeghi, Navid Jarzebowski, Julian Bretl, Timothy Coleman, Todd P. Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif San Diego Inst Neural Computat La Jolla CA 92093 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Neurosci Program Urbana IL 61801 USA Motorola Inc Schaumburg IL 60196 USA

The P300-based speller is a well-established brain-computer interface for communication. It displays a matrix of objects on the computer screen, flashes each object in sequence, and looks for a P300 response induced by flashing the desired object. Most existing P300 spellers uses a fixed set of flash objects. We demonstrate that performance can be significantly improved by sequential selections from a hierarchy of flash sets containing variable number of objects. Theoretically, the optimal hierarchy of flash sets-with respect to a given statistical language model-can be found by solving a stochastic control problem of low computational complexity. Experimentally, statistical analysis demonstrates that the average time per output character at 85% accuracy is reduced by over 50% using our variable-flash-set approach as compared to traditional fixed-flash-set spellers.

关键词： Brain-computer interfaces (BCIs) hierarchical flash sets stochastic dynamic programming P300

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Managing an Available-to-Promise Assembly System with dynamic Short-Term Pseudo-Order Forecast

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MANAGEMENT SCIENCE 2012年第4期58卷 770-790页

作者： Gao, Long Xu, Susan H. Ball, Michael O. Univ Calif Riverside A Gary Anderson Grad Sch Management Riverside CA 92521 USA Penn State Univ Smeal Coll Business University Pk PA 16802 USA Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA

We study an order promising problem in a multiclass, available-to-promise (ATP) assembly system in the presence of pseudo orders. A pseudo order refers to a tentative customer order whose attributes, such as the likelihood of an actual order, order quantity, and confirmation timing, can change dynamically over time. A unit demand from any class is assembled from one manufactured unit and one inventory unit, where the manufactured unit takes one unit of capacity and needs a single period to produce. An accepted order must be filled before a positive delivery lead time. The underlying order acceptance decisions involve trade-offs between committing resources (production capacity and component inventory) to low-reward firm orders and reserving resources for high-reward orders. We develop a Markov chain model that captures the key characteristics of pseudo orders, including demand lumpiness, nonstationarity, and volatility. We then formulate a stochastic dynamic program for the ATP assembly system that embeds the Markov chain model as a short-term forecast for pseudo orders. We show that the optimal order acceptance policy is characterized by class prioritization, resource-imbalance-based rationing, and capacity-inventory-demand matching. In particular, the rationing level for each class is determined by a critical value that depends on the resource imbalance level, defined as the net difference between the production capacity and component inventory levels. Extensive numerical tests underscore the importance of the key properties of the optimal policy and provide operational and managerial insights on the value of the short-term demand forecast and the robustness of the optimal policy.

关键词： available-to-promise pseudo orders Markov stochastic dynamic programming prioritization resource and demand matching resource-imbalance-based rationing short-term and long-term forecasts robustness

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Habit Formation from Correlation Aversion

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OPERATIONS RESEARCH 2012年第3期60卷 625-637页

作者： Lichtendahl, Kenneth C., Jr. Chao, Raul O. Bodily, Samuel E. Univ Virginia Darden Sch Business Charlottesville VA 22903 USA

Making plans about how much to consume and how much to invest in risky assets over an uncertain lifetime is a fundamental economic challenge. The leading models of this planning problem use either additive or habit-forming preferences. For the most part, these models assume an individual is either correlation neutral or correlation seeking in consumption, respectively. In this paper, we introduce two habit-forming, correlation-averse preference models. With these preferences, we find closed-form solutions to the classic consumption and portfolio planning problem. Our solutions recommend that a correlation-averse decision maker follow a habit in their consumption plans. While such habits traditionally have been associated with correlation-seeking preferences, our model leads to consumption habits from correlation-averse preferences.

关键词： decision analysis multiattribute utility stochastic dynamic programming consumption and portfolio planning

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