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Joint Initial Stocking and TransshipmentAsymptotics and Bounds

PRODUCTION AND OPERATIONS MANAGEMENT 2016年第2期25卷 273-289页

作者： Yao, David D. Zhou, Sean X. Zhuang, Weifen Columbia Univ Dept Ind Engn & Operat Res New York NY 10027 USA Chinese Univ Hong Kong CUHK Business Sch Dept Decis Sci & Managerial Econ Shatin Hong Kong Peoples R China Xiamen Univ Sch Management Fujian 361005 Peoples R China

A common problem faced by many firms in their supply chains can be abstracted as follows. Periodically, or at the beginning of some selling season, the firm needs to distribute finished goods to a set of stocking locations, which, in turn, supply customer demands. Over the selling season, if and when there is a supply-demand mismatch somewhere, a re-distribution or transshipment will be needed. Hence, there are two decisions involved: the one-time stocking decision at the beginning of the season and the supply/transshipment decision throughout the season. Applying a stochastic dynamic programming formulation to a two-location model with compound Poisson demand processes, we identify the optimal supply/transshipment policy and show that the optimal initial stocking quantities can be obtained via maximizing a concave function whereas the contribution of transshipment is of order square-root-of T. Hence, in the context of high-volume, fast-moving products, the initial stocking quantity decision is a much more important contributor to the overall profit. The bounds also lead to a heuristic policy, which exhibits excellent performance in our numerical study;and we further prove both the bounds and the heuristic policy are asymptotically optimal when T approaches infinity. Extension to multiple locations is also discussed.

关键词： inventory transshipment stochastic dynamic programming bounds asymptotic optimality

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Scheduling servers in a two-stage queue with abandonments and costs

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2023年第3期37卷 833-851页

作者： Zayas-Caban, Gabriel Cochran, Amy L. Univ Wisconsin BerbeeWalsh Dept Emergency Med Ind & Syst Engn Madison WI 53705 USA Univ Wisconsin Dept Math Populat Hlth Sci Madison WI USA

We consider the assignment of servers to two phases of service in a two-stage tandem queueing system when customers can abandon from each stage of service. New jobs arrive at both stations. Jobs arriving at station 1 may go through both phases of service and jobs arriving at station 2 may go through only one phase of service. Stage-dependent holding and lump-sum abandonment costs are incurred. Continuous-time Markov decision process formulations are developed that minimize discounted expected and long-run average costs. Because uniformization is not possible, we use the continuous-time framework and sample path arguments to analyze control policies. Our main results are conditions under which priority rules are optimal for the single-server model. We then propose and evaluate threshold policies for allocating one or more servers between the two stages in a numerical study. These policies prioritize a phase of service before "switching" to the other phase when total congestion exceeds a certain number. Results provide insight into how to adjust the switching rule to significantly reduce costs for specific input parameters as well as more general multi-server situations when neither preemption or abandonments are allowed during service and service and abandonment times are not exponential.

关键词： Operations research Queueing theory stochastic dynamic programming stochastic modeling

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Preventive maintenance and upgrade system: Optimizing the whole performance system by components' replacement or rearrangement

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2008年第1期112卷 236-244页

作者： Rachaniotis, N. P. Pappis, C. P. Univ Piraeus Dept Ind Management Piraeus 18534 Greece

A decision-making model for reassembling different deteriorating subsystems and components of a complex system from used and new parts is proposed. The objective is to find the proper reassembly policies in a period of time so as to maximize the systems' overall performance values, under limited budget, and reassembly and compatibility constraints. Environmental gains are incurred from these policies, since the used components' life cycle, at least in some cases, is extended instead of ending by entering the waste stream. A stochastic dynamic programming approach is proposed, and an example in the case of personal computers is presented. (c) 2007 Elsevier B.V. All rights reserved.

关键词： reassembly benchmarking deteriorating components stochastic dynamic programming

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Online Resource Allocation for Energy Harvesting Downlink Multiuser Systems: Precoding With Modulation, Coding Rate, and Subchannel Selection

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2015年第10期14卷 5780-5794页

作者： Zeng, Weiliang Zheng, Yahong Rosa Schober, Robert Missouri Univ Sci & Technol Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Univ Erlangen Nurnberg Dept Elect Elect & Commun Engn EEI D-91054 Erlangen Germany

This paper proposes an online resource allocation algorithm for weighted sum rate maximization in energy harvesting downlink multiuser multiple-input-multiple-output (MIMO) systems, where the base station transmitter is powered by both a regular energy source and an energy buffer that is connected to an energy harvester. Taking into account the discrete nature of the modulation and coding rates (MCRs) used in practice, we formulate a stochastic dynamic programming (SDP) problem to jointly design the MIMO precoders, select the MCRs, assign the subchannels, and optimize the energy consumption over multiple time slots with causal and statistical energy arrival information and statistical channel state information. Solving this high-dimensional SDP entails several difficulties: the SDP has a nonconcave objective function, the optimization variables are of mixed binary and continuous types, and the number of optimization variables is on the order of thousands. We propose a new method to solve this NP-hard SDP by decomposing the high-dimensional SDP into an equivalent three-layer optimization problem and develop efficient algorithms to solve each layer separately. The decomposition reduces the computational burden and breaks the curse of dimensionality successfully. We analyze the complexity of the proposed algorithm and demonstrate the performance gains based on numerical examples.

关键词： Energy harvesting multiuser systems finite-alphabet inputs statistical channel state information online resource allocation stochastic dynamic programming

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dynamic container slot allocation with empty container repositioning under stochastic demand

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 187卷

作者： Liang, Jinpeng Ma, Zhongyuan Wang, Shuang Liu, Haitao Tan, Zhijia Dalian Maritime Univ Sch Transportat Engn Dalian 116026 Peoples R China Sichuan Univ Business Sch Chengdu 610065 Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

Container slot allocation represents a critical operational decision -making challenge within the liner shipping industry, which necessitates making decisions on the transportation of loaded containers and the repositioning of empty ones under stochastic demand. We study novel dynamic allocation policy that leverage sequentially -revealed demand information to determine the slot allocation decision for both loaded and empty containers at each stage. In this paper, we develop a stochastic dynamic programming (DP) model to optimize the slot allocation decision for maximizing the expected total revenue over the planning horizon. To solve this model, we design an efficient allocation policy that makes slot allocations at each stage based on current empty container stocks, realized demand, and the mean demand at future stages. Comprehensive numerical experiments on both synthetic and realistic data demonstrate substantial revenue improvement of our approach over the commonly -used benchmark policies in practice and literature.

关键词： Container slot allocation Empty container repositioning stochastic demand stochastic dynamic programming Approximate dynamic programming

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Production and changeover control policies of a class of failure prone buffered flow-shops

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PRODUCTION PLANNING & CONTROL 2009年第8期20卷 785-800页

作者： Hajji, Adnene Gharbi, Ali Kenne, Jean-Pierre Hidehiko, Yamamoto Univ Quebec Dept Automated Prod Engn LCCSP Lab Ecole Technol Super Quebec City PQ Canada Univ Quebec Dept Mech Engn LITP Lab Ecole Technol Super Quebec City PQ Canada Gifu Univ Fac Engn Dept Human & Informat Syst Gifu 50111 Japan

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.

关键词： production and setup control buffered flow-shops stochastic dynamic programming numerical methods production control real-time scheduling

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Capacity expansion and replacement in growing markets with uncertain technological breakthroughs

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MANAGEMENT SCIENCE 1998年第1期44卷 12-30页

作者： Rajagopalan, S Singh, MR Morton, TE Univ So Calif Marshall Sch Business Los Angeles CA 90089 USA Dartmouth Coll Amos Tuck Sch Business Adm Hanover NH 03755 USA Carnegie Mellon Univ Grad Sch Ind Adm Pittsburgh PA 15213 USA

The accelerated pace of technological change has led to rapid obsolescence of productive capacity in electronics and other industries. Managers must consider the impact of future technologies while making acquisition and replacement decisions in such environments. We consider a problem where a sequence of technological breakthroughs are anticipated but their magnitude and timing are uncertain. A firm, operating in such an environment, must decide how much capacity of the current technology to acquire to meet future demand growth. It must also determine whether to upgrade any of the older vintages. We formulate this problem and present some structural results. Using these results, we then develop a highly efficient regeneration point-based dynamic programming algorithm. The effectiveness of the proposed algorithm is illustrated through a computational study. The sensitivity of the first period decision to various parameters is also explored.

关键词： capacity expansion equipment replacement technology adoption models stochastic dynamic programming management of technology

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Evaluation of Regulatory Impacts on dynamic Behavior of Investments in Electricity Markets: A New Hybrid DP/GAME Framework

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IEEE TRANSACTIONS ON POWER SYSTEMS 2010年第4期25卷 1978-1986页

作者： Barforoushi, Taghi Moghaddam, Mohsen P. Javidi, M. Hossein Sheikh-El-Eslami, Mohammad K. Tarbiat Modares Univ Dept Elect Engn Tehran Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran

A new hybrid framework based on game theory and dynamic programming (DP) with random demands and prices is proposed for studying the impacts of regulatory interventions on the dynamics of investment in power generation in electricity markets. In our approach, using Markov chains, the electric demand and growth of fuel prices have been modeled. DP has been used for solving the generation expansion planning (GEP) problem. Investment strategies of other investors in the market are modeled as constraints. The income of the investor is calculated by modeling strategic interactions among market players in the spot energy market. The Cournot game concept has been applied and the Nash equilibrium is calculated for each state and stage of DP. Simulation results confirm that the proposed framework is an appropriate decision-support tool that provides useful information about dynamics of investment.

关键词： Electricity markets game theory generation expansion planning investment dynamics stochastic dynamic programming

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stochastic Linear programming for Reservoir Operation with Constraints on Reliability and Vulnerability

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WATER 2018年第2期10卷 175-175页

作者： Chen, Cheng Kang, Chuanxiong Wang, Jinwen Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

Reliability and vulnerability (RV) are two very important performance measures but, due to their stage-inseparable nature, they cannot be explicitly incorporated in stochastic dynamic programming (SDP), which is extensively used in reservoir operation. With inflows described as a Markov chain, a stochastic linear programming (SLP) model is formulated in this paper to explicitly incorporate the RV constraints in the reservoir operation, aimed at maximizing the expected power generation by determining the optimal scheduling decisions and their probabilities. Simulation results of the SLP and SDP models indicate the equivalence of the proposed SLP and SDP models without considering the RV constraints, as well as the strength of the SLP in explicitly incorporating the RV constraints. A simulated scheduling solution also reveals a reduction of power generation fluctuation, with the reservoir capacity emptied in advance to meet given reliability and vulnerability.

关键词： stochastic linear programming Markov chain reliability vulnerability reservoir operation stochastic dynamic programming

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Allocating resources via price management systems: a dynamic programming-based approach

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INTERNATIONAL JOURNAL OF CONTROL 2021年第8期94卷 2123-2143页

作者： Forootani, Ali Liuzza, Davide Tipaldi, Massimo Glielmo, Luigi Univ Sannio Dept Engn Piazza Roma 21 I-82100 Benevento Italy ENEA Fus & Nucl Safety Dept Frascati Rome Italy

In this paper, a novel model for price management systems in resource allocation problems is proposed. stochastic customer requests for resource allocations and releases are modelled as constrained parallel Birth-Death Processes (BDP). We address both instant (i.e. the customer requires a resource to be allocated immediately) and advance (i.e. the customer books a resource for future use) reservation requests, the latter with both bounded and unbounded time interval options. Algorithms based on dynamic programming (DP) principles are proposed for the calculation of suitable price profiles. At the core of such algorithms, there is the resolution of stochastic optimisation problems. In particular, the maximisation of the expected total revenue is formulated via a constrained stochastic dynamic programming (SDP) approach, which becomes time-variant in case of advance reservation requests. Approximate dynamic programming (ADP) techniques are adopted in case of large state spaces. Simulations are performed to show the effectiveness of the proposed models and the related algorithms.

关键词： Price management systems resource allocation problems stochastic dynamic programming Markov decision process approximate dynamic programming

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