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Optimal Control Policy (optimal + control_policy)
Selected AbstractsDynamic inventory management with cash flow constraintsNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 8 2008Xiuli Chao Abstract In this article, we consider a classic dynamic inventory control problem of a self-financing retailer who periodically replenishes its stock from a supplier and sells it to the market. The replenishment decisions of the retailer are constrained by cash flow, which is updated periodically following purchasing and sales in each period. Excess demand in each period is lost when insufficient inventory is in stock. The retailer's objective is to maximize its expected terminal wealth at the end of the planning horizon. We characterize the optimal inventory control policy and present a simple algorithm for computing the optimal policies for each period. Conditions are identified under which the optimal control policies are identical across periods. We also present comparative statics results on the optimal control policy. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2008 [source] Solving multi-objective dynamic optimization problems with fuzzy satisfying methodOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 5 2003Cheng-Liang Chen Abstract This article proposes a novel algorithm integrating iterative dynamic programming and fuzzy aggregation to solve multi-objective optimal control problems. First, the optimal control policies involving these objectives are sequentially determined. A payoff table is then established by applying each optimal policy in series to evaluate these multiple objectives. Considering the imprecise nature of decision-maker's judgment, these multiple objectives are viewed as fuzzy variables. Simple monotonic increasing or decreasing membership functions are then defined for degrees of satisfaction for these linguistic objective functions. The optimal control policy is finally searched by maximizing the aggregated fuzzy decision values. The proposed method is rather easy to implement. Two chemical processes, Nylon 6 batch polymerization and Penicillin G fed-batch fermentation, are used to demonstrate that the method has a significant potential to solve real industrial problems. Copyright © 2003 John Wiley & Sons, Ltd. [source] Approximate dynamic programming based optimal control applied to an integrated plant with a reactor and a distillation column with recycleAICHE JOURNAL, Issue 4 2009Thidarat Tosukhowong Abstract An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal "cost - to - go" function, which parameterizes the optimal control policy with respect to the state variable. With the approximate "cost - to - go" function computed, a multistage optimization problem that needs to be solved online at every sample time can be reduced to a single-stage optimization, thereby significantly lessening the real-time computational load. Moreover, stochastic uncertainties can be addressed relatively easily within this framework. Nonetheless, the existing ADP method requires excessive offline computation when applied to a high-dimensional system. A case study of a reactor and a distillation column with recycle was used to illustrate this issue. Then, several ways were proposed to reduce the computational load so that the ADP method can be applied to high-dimensional integrated plants. The results showed that the approach is much more superior to NMPC in both deterministic and stochastic cases. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Design and control of agile automated CONWIP production linesNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 1 2009Wallace J. Hopp Abstract In this article, we study the design and control of manufacturing cells with a mix of manual and automated equipment, operating under a CONWIP pull protocol, and staffed by a single agile (cross-trained) worker. For a three-station line with one automated station, we fully characterize the structure of the optimal control policy for the worker and show that it is a static priority policy. Using analytical models and extensive simulation experiments, we also evaluate the effectiveness of practical heuristic control policies and provide managerial insights on automation configuration design of the line. This characterization of the worker control policy enables us to develop managerial insights into the design issues of how best to locate and concentrate automation in the line. Finally, we show that, in addition to ease of control and greater design flexibility, the CONWIP protocol also offers higher efficiency and robustness than does the push protocol. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2009 [source] Optimal empty vehicle redistribution for hub-and-spoke transportation systemsNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 2 2008Dong-Ping Song Abstract This article considers the empty vehicle redistribution problem in a hub-and-spoke transportation system, with random demands and stochastic transportation times. An event-driven model is formulated, which yields the implicit optimal control policy. Based on the analytical results for two-depot systems, a dynamic decomposition procedure is presented which produces a near-optimal policy with linear computational complexity in terms of the number of spokes. The resulting policy has the same asymptotic behavior as that of the optimal policy. It is found that the threshold-type control policy is not usually optimal in such systems. The results are illustrated through small-scale numerical examples. Through simulation the robustness of the dynamic decomposition policy is tested using a variety of scenarios: more spokes, more vehicles, different combinations of distribution types for the empty vehicle travel times and loaded vehicle arrivals. This shows that the dynamic decomposition policy is significantly better than a heuristics policy in all scenarios and appears to be robust to the assumptions of the distribution types. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008 [source] Optimal and sub-optimal control in Dengue epidemicsOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2001Marco Antonio Leonel Caetano Abstract This work concerns the application of the optimal control theory to Dengue epidemics. The dynamics of this insect-borne disease is modelled as a set of non-linear ordinary differential equations including the effect of educational campaigns organized to motivate the population to break the reproduction cycle of the mosquitoes by avoiding the accumulation of still water in open-air recipients. The cost functional is such that it reflects a compromise between actual financial spending (in insecticides and educational campaigns) and the population health (which can be objectively measured in terms of, for instance, treatment costs and loss of productivity). The optimal control problem is solved numerically using a multiple shooting method. However, the optimal control policy is difficult to implement by the health authorities because it is not practical to adjust the investment rate continuously in time. Therefore, a suboptimal control policy is computed assuming, as the admissible set, only those controls which are piecewise constant. The performance achieved by the optimal control and the sub-optimal control policies are compared with the cases of control using only insecticides when Breteau Index is greater or equal to 5 and the case of no-control. The results show that the sub-optimal policy yields a substantial reduction in the cost, in terms of the proposed functional, and is only slightly inferior to the optimal control policy. Copyright © 2001 John Wiley & Sons, Ltd. [source] Dynamic On-Line Reoptimization Control of a Batch MMA Polymerization Reactor Using Hybrid Neural Network ModelsCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 9 2004Y. Tian Abstract A hybrid neural network model based on-line reoptimization control strategy is developed for a batch polymerization reactor. To address the difficulties in batch polymerization reactor modeling, the hybrid neural network model contains a simplified mechanistic model covering material balance assuming perfect temperature control, and recurrent neural networks modeling the residuals of the simplified mechanistic model due to imperfect temperature control. This hybrid neural network model is used to calculate the optimal control policy. A difficulty in the optimal control of batch polymerization reactors is that the optimization effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. With the presence of an unknown amount of reactive impurities, the off-line calculated optimal control profile will be no longer optimal. To address this issue, a strategy combining on-line reactive impurity estimation and on-line reoptimization is proposed in this paper. The amount of reactive impurities is estimated on-line during the early stage of a batch by using a neural network based inverse model. Based on the estimated amount of reactive impurities, on-line reoptimization is then applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimization control of a simulated batch methyl methacrylate polymerization process. [source] | ||||||||||