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International Conference on Foundations of Computer-Aided Process Operations

作者： Choi, J Realff, MJ Lee, JH Georgia Inst Technol Sch Chem & Biomol Engn Ctr Proc Syst Engn Atlanta GA 30332 USA

The resource-constrained project scheduling problem (RCPSP) is a significant challenge in highly regulated industries, such as pharmaceuticals and agrochemicals, where a large number of candidate new products must undergo a set of tests for certification. We propose a novel way of addressing the uncertainties in the RCPSP including the uncertainties in task durations and costs, as well as uncertainties in the results of tasks (success or failure) by using a discrete time Markov chain, which enables us to model probabilistic correlation of the uncertain parameters. The resulting stochastic optimization problem can be solved by using dynamic programming, but the computational load renders this impractical. Instead, we develop a new way to combine heuristic solutions through dynamic programming in the state space that the heuristics generate. The proposed approach is tested on a simplified version of RCPSP that has a fairly complicated stochastic nature, with 1,214,693,756 possible parameter realizations (scenarios), and involves five projects and 17 tasks. As a result, an on-line policy is obtained, which can use the information states in on-line decision making and improve the heuristics rather than a fixed solution obtained by the previous mathematical programming (MILP) problem formulations. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： stochastic dynamic programming Markov chain project scheduling combinatorial optimization heuristics

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stochastic optimization to maximize water supply index in conjunctive use of surface and groundwater

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JOURNAL OF HYDROLOGY 2025年 648卷

作者： Khosravi, Mina Afshar, Abbas Perona, Paolo Barry, David Andrew Salavitabar, Abdolrahim Ghaheri, Abbas Iran Univ Sci & Technol Dept Civil Engn Tehran Iran Ecole Polytech Fed Lausanne EPFL Inst Civil Engn IIC Sch Architecture Civil & Environm Engn ENAC Hydraul Construct Platform PL LCH CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne EPFL Inst Environm Engn IIE Sch Architecture Civil & Environm Engn ENAC Ecol Engn Lab ECOL CH-1015 Lausanne Switzerland Mahab Ghodss Consulting Engn Co Tehran Iran

Systematic operation of water resources systems requires rules that consider the uncertainties affecting system performance. Here, stochastic dynamic programming (SDP) is used to structure and solve a full-scale surface and groundwater conjunctive use model to derive a multiparameter operational policy (rule) that maximizes the water supply index (WSI). The model uses a Markovian representation of inflows to account for hydrologic uncertainties. Both the reliability of allocated water and its vulnerability are employed to define the WSI. To overcome the computational burden inherent with SDP, infeasible solutions are identified and removed from the model solution process. Optimal expected values of WSI and surface and groundwater uses are assessed. The system simulation model with embedded rules is executed to assess the performance of the derived rules. The derived rules employ water rationing and account for long-term benefits during periods when the available surface and groundwater resources may suffice to meet demand. The simulation results demonstrate that the derived operational rules produce high WSI values for the long-term operation of the system and ensure sustainable groundwater use.

关键词： stochastic dynamic programming Uncertainty Operational rule Conjunctive use Water supply index Water resources management

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Mean-Variance optimization in discrete-time decision processes with general utility function☆

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AUTOMATICA 2025年 174卷

作者： Baeuerle, Nicole Jaskiewicz, Anna Nowak, Andrzej S. Karlsruhe Inst Technol Dept Math Karlsruhe Germany Wroclaw Univ Sci & Technol Fac Pure & Appl Math Wroclaw Poland Univ Zielona Gora Inst Math Zielona Gora Poland

We study general discrete-time Mean-Variance problems in a non-Markovian setting. The utility is a general, continuous function which may depend on the entire history of the process. It contains many recursive utility functions with non-linear aggregator as special cases. Under some continuity and compactness assumptions on the model data, we establish the existence of persistently optimal deterministic policies. For finite horizon problems this also yields a recursive solution algorithm. The theory which we develop here goes beyond Mean-Variance models and may be applied, e.g., to Optimized Certainty Equivalents. The Mean-Variance optimization framework is also applied to a multi-stage portfolio analysis with constraints on short selling. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and

关键词： Mean-variance optimization stochastic dynamic programming Decision process Portfolio selection Investment decisions similar technologies.

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Economic design of a self-healing policy with limited agents

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 199卷

作者： Zheng, Rui Xing, Yuan Peng, Zhanglin Ren, Xiangyun Tan, Kanlun Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Hefei Univ Technol Key Lab Proc Optimizat & Intelligent Decis Making Minist Educ Hefei 230009 Peoples R China Tianjin Univ Sch Mech Engn Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300350 Peoples R China State Key Lab Intelligent Vehicle Safety Technol Chongqing 400000 Peoples R China

Self-healing has been increasingly integrated into systems to enhance their reliability. Many popular intrinsic self-healing policies are not cost-effective because their self-healing actions are not always performed at the right time. This paper investigates the economic design of a self-healing policy with limited agents for an intelligent system that executes a mission of finite length. Several healing agents are embedded into the system before the mission. The system performs self-detection at equidistant epochs to reveal deterioration levels. The deterioration can be randomly healed by releasing healing agents, and the healing effect depends on the number of agents released. At each inspection epoch, a decision is made on how many healing agents to be released. The objective is to jointly determine the capacity of agents and the self-healing policy to minimize the expected total cost over the mission length. The optimization problem is formulated in the stochastic dynamic programming framework. A backward induction algorithm is developed to find the optimal solution. A numerical example is provided to illustrate the effectiveness of the proposed approach. The comparison with a control-limit policy confirms the outstanding performance of the proposed policy.

关键词： Self-healing Condition-based maintenance stochastic dynamic programming Backward induction algorithm

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Fluid Policies, Reoptimization, and Performance Guarantees in dynamic Resource Allocation

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OPERATIONS RESEARCH 2025年第2期73卷 iii-viii, 583-1150, C2-C3页

作者： Brown, David B. Zhang, Jingwei Duke Univ Fuqua Sch Business Durham NC 27708 USA Chinese Univ Hong Kong Sch Data Sci Shenzhen CUHK Shenzhen Shenzhen 518172 Peoples R China

Many sequential decision problems involve deciding how to allocate shared resources across a set of independent systems at each point in time. A classic example is the restless bandit problem, in which a budget constraint limits the selection of arms. Fluid relaxations provide a natural approximation technique for this broad class of problems. A recent stream of research has established strong performance guarantees for feasible policies based on fluid relaxations. In this paper, we generalize and improve these recent performance results. First, we provide easy-to-implement feasible fluid policies that achieve root ffiffififfi performance within O( N ) of optimal, where N is the number of subproblems. This result holds for a general class of dynamic resource allocation problems with heterogeneous subproblems and multiple shared resource constraints. Second, we show using a novel proof technique that a feasible fluid policy that chooses actions using a reoptimized fluid value root ffiffififfi function achieves performance within O( N ) of optimal as well. To the best of our knowledge, this performance guarantee is the first one for reoptimization for the general dynamic resource allocation problems that we consider. The scaling of the constants with respect to time in these results implies similar results in the infinite horizon setting. Finally, we develop and analyze a class of feasible fluid-budget balancing policies that stay "close" to actions selected by an optimal fluid policy while simultaneously using as much of the shared resources as possible. We show that this policy achieves performance within O(1) of optimal under particular nondegeneracy assumptions. This result generalizes recent advances for restless bandit problems by considering (a) any finite number of actions for each subproblem and (b) heterogeneous subproblems with a fixed number of types. We demonstrate the use of these techniques on dynamic multiwarehouse inventory problems and find empirical

关键词： stochastic dynamic programming Markov Lagrangian relaxation

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Optimal Cooling Controller Design for Battery Thermal Management System of Electric Vehicle

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 1529-1540页

作者： Park, Seongun Ahn, Changsun Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

This study presents a stochastic dynamic programming-based cooling controller for the battery thermal management system (BTMS) in electric vehicles (EVs). Addressing the complex interplay between battery performance and safety, our approach optimizes temperature regulation while minimizing power consumption. Notable contributions include minimum transitions between refrigeration and radiator modes, integration of an artificial neural network for computing efficiency, and an infinity-horizon expected cost formulation considering future heat disturbances. Comparative analyses demonstrate superior performance, showcasing the proposed controller's efficiency in achieving smaller battery temperature variation and consistently lower energy consumption across diverse ambient conditions. The proposed controller shows 56% lower energy consumption on average compared to rule-based controller.

关键词： Batteries Refrigeration Cooling Coolants Valves Thermal management Optimal control Battery thermal management system (BTMS) control electric vehicle (EV) stochastic dynamic programming

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Optimal investment and benefit strategies for a target benefit pension plan where the risky assets are jump diffusion processes

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INSURANCE MATHEMATICS & ECONOMICS 2025年 121卷 100-110页

作者： Josa-Fombellida, Ricardo Lopez-Casado, Paula Univ Valladolid Dept Estadist & Invest Operat IMUVA Valladolid Spain

In this paper, we study the optimal management of a target benefit pension plan. The fund manager adjusts the benefit to guarantee the plan stability. The fund can be invested in a riskless asset and several risky assets, where the uncertainty comes from Brownian and Poisson processes. The aim of the manager is to maximize the expected discounted utility of the benefit and the terminal fund wealth. A stochastic control problem is considered and solved by the programming dynamic approach. Optimal benefit and investment strategies are analytically found and analyzed, both in finite and infinite horizons. A numerical illustration shows the effect of some parameters on the optimal strategies and the fund wealth.

关键词： Target benefit pension plan Portfolio optimization stochastic optimal control Poisson process stochastic dynamic programming

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Valuing labor flexibility in cross-training using a real options approach

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2025年 1-21页

作者： Lian, Jie Liu, Chenguang Xu, Yi Li, Yue Xian Univ Technol Sch Econ & Management Xian 710048 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

Cross-training is an effective way to acquire labor flexibility which can be defined by multifunctionality and redundancy. This study concentrates on redundancy and considers how much capacity is required to satisfy present and future demand. A two-phase cross-training problem is investigated to discuss how much redundant capacity should be pooled for the bottleneck work station of a new product with the consideration of demand uncertainty. The problem determines the number of workers should be trained in the first phase and in which period they should fulfill the second phase. Workers participate in the first phase should be more than the current demand needed. These extra workers act as a capacity buffer against demand uncertainty, and training costs paid for them can be treated as an investment in flexibility. The decision process is analogous to an expansion option, and a real options approach is employed to solve the problem. A stochastic dynamic programming model, in which the dynamic of demand is approximated by a trinomial lattice, is formulated. Based on differences in the expected NPV brought by training extra workers, the monetary value of labor flexibility can be measured. Then, the impact of demand uncertainty, workers' multi-skill level and penalty cost for unsatisfied demand on the optimal training plan and the value of flexibility are tested by using numerical experiments. The results verify that flexibility brings more value when demand variation is great and pooling redundant capacity in advance is necessary to cope with demand uncertainty. Besides, the results have several implications. (1) In order to rise profits, a lower multi-skill level is suggested for products with great demand variation. (2) The quantity of capacity pooled for the bottleneck is crucial to the value of flexibility. (3) Excessive multifunctionality hurts the value of flexibility brought by redundancy. (4) Flexibility brought by capacity buffer is valuable for companies who a

关键词： Cross-training Labor flexibility Redundancy Real options stochastic dynamic programming

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Bayesian learning in dynamic portfolio selection under a minimax rule

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OR SPECTRUM 2025年第1期47卷 287-324页

作者： Cai, Xiaoqiang Yu, Gen Chinese Univ Hong Kong Shenzhen & Shenzhen Res Inst Big Data Shenzhen Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China

We are concerned about a multi-period portfolio selection problem where the issue of parameter uncertainty for the distribution of risky asset returns should be addressed properly. For analysis, we first propose a novel dynamic portfolio selection model with an l(infinity) risk function, instead of the classic portfolio variance, used as risk measure. The investor in our model is assumed to choose the optimal portfolio by maximizing the expected terminal wealth at a minimum level of cumulative risk, quantified by a weighted sum of the risks in subsequent periods. The proposed multi-period model has a closed-form optimal policy that can be constructed and interpreted intuitively. We introduce Bayesian learning to account for the uncertainty in estimates of unknown parameters and discuss the impact of Bayesian learning on the investor's decision making. Under an i.i.d. normal return-generating process with unknown means and covariance matrix, we show how Bayesian learning promotes diversification and reduces sensitivity of optimal portfolios to changes in model inputs. The numerical results based on real market data further support that the model with Bayesian learning can perform much better than a plug-in model out-of-sample with the extent of performance improvement affected by the investor's level of risk aversion and the amount of data available.

关键词： Investment analysis Bayesian learning Parameter uncertainty l(infinity ) risk function stochastic dynamic programming

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Managing perishable inventory when strategic customers form a reference on product availability

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ANNALS OF OPERATIONS RESEARCH 2025年第1期344卷 47-78页

作者： Arslan, Hasan Kim, Seokjin Suffolk Univ Dept Informat Syst & Operat Management 120 Tremont St Boston MA 02108 USA

Our framework deals with stochastic dynamic inventory models for stocking decisions of a retailer selling a single perishable product in the presence of strategic customers who time their purchases. Each short period, the retailer determines a stocking quantity before random demand is realized. Strategic customers use their reference on product availability to purchase at a regular price or wait for a markdown and learn from the retailer's stocking quantity to update their reference. We characterize the structural properties such as the concavity of single- and two-period profit functions. On an infinite horizon, we show that a steady-state reference distribution is ergodic and an optimal stocking quantity is unique for a given reference. We conduct extensive numerical studies on an infinite horizon to compare an optimal dynamic policy and the corresponding optimal static policy which sets a fixed stocking quantity over time. A near-optimal performance of optimal static policy with an average profit gap of less than 1% is remarkable and contrasts with that in the two-period model which may be far worse. Thus, a well-chosen fixed stocking quantity on a planning horizon with many short periods tends to yield a high performance without having to change stocking quantities over time.

关键词： Inventory Perishable product Strategic customers Product availability stochastic dynamic programming

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