With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational s...
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With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamicprogramming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based Approximate dynamicprogramming and the Rolling-Horizon-based Approximate dynamicprogramming are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to stochastic dynamic programming.
This paper proposes a cost-effective power management strategy utilizing the data provided by V2I communication techniques for dual electric machine coupling propulsion trucks. We formulate a bilevel program where the...
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This paper proposes a cost-effective power management strategy utilizing the data provided by V2I communication techniques for dual electric machine coupling propulsion trucks. We formulate a bilevel program where the high-level optimizes operation mode implicitly, while the low-level computes an explicit policy for power distribution of two electric machines. stochastic model predictive control (SMPC) strategy is employed at the highlevel, the performance of which highly depends on the prediction accuracy of future driving information. To establish a position dependent stochastic velocity predictor using limited amount of historical data, two improved approaches are developed: 1) Predictor using multiple features;2) Predictor combining data and model. Simulations are performed to validate the performance of the proposed predictors compared with a benchmark. The results show that the controllers using the proposed predictors can reduce driving cost by 3.36 % and 4.26 %, respectively.
This paper presents a medium-term scheduling model of a hybrid hydro-solar power plant. The study examines how hybridization enhances the security of supply of a hydro-only system, particularly in Nordic weather condi...
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ISBN:
(纸本)9798350381757;9798350381740
This paper presents a medium-term scheduling model of a hybrid hydro-solar power plant. The study examines how hybridization enhances the security of supply of a hydro-only system, particularly in Nordic weather conditions. A stochastic dynamic programming (SDP) algorithm is implemented to properly address the uncertainty of Nordic weather conditions and calculate water values. With solar power modeled as a photovoltaic (PV) installation, a hybrid system was created to optimize the load fulfillment. The results from the hybrid system were compared with the reference hydro-only system and an energy-upgraded hydro-only system using a creek inlet. The Ormsetfossen power plant, a Norwegian hydropower system with two reservoirs, is used as a reference for the data collection and topology modeling. Scenarios from 30 weather years of inflow and solar irradiation data were used to produce production plans for the hybrid and hydro-only configurations. Results indicate that hybridization enhances the security of supply to a greater extent than the energy-upgraded hydro-only system. The energy-upgraded hydro-only system is on the other hand more self-sufficient and less reliant on power imported from the market.
In this article, we consider the case of a multinational company realizing profits in a country other than its base country. The currencies used in the base and foreign countries are referred to as the domestic and fo...
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In this article, we consider the case of a multinational company realizing profits in a country other than its base country. The currencies used in the base and foreign countries are referred to as the domestic and foreign currencies respectively. For its quarterly and yearly financial statements, the company transfers its profits from a foreign bank account to a domestic bank account. Thus, the foreign currency liquidation task consists formally in exchanging over a period T a volume V of cash in the foreign currency f for a maximum volume of cash in the domestic currency d. The foreign exchange (FX) rate that prevails at time t is denoted X-d/f(t) and is defined as the worth of one unit of currency d in the currency f. We assume in this article that the natural logarithm of the FX rate x(t) = log X-d/f (t) follows a discrete generalized Ornstein-Uhlenbeck (OU) process, a process which generalizes the Brownian motion and mean-reverting processes. We also assume minimum and maximum volume constraints on each transaction. Foreign currency liquidation exposes the multinational company to financial risks and can have a significant impact on its final revenues, since FX rates are hard to predict and often quite volatile. We introduce a Reinforcement Learning (RL) framework for finding the liquidation strategy that maximizes the expected total revenue in the domestic currency. Despite the huge success of Deep Reinforcement Learning (DRL) in various domains in the recent past, existing DRL algorithms perform sub-optimally in this task and the stochastic dynamic programming (SDP) algorithm - which yields the optimal strategy in the case of discrete state and action spaces - is rather slow. Thus, we propose here a novel algorithm that addresses both issues. Using SDP, we first determine numerically the optimal solution in the case where the state and decision variables are discrete. We analyse the structure of the computed solution and derive an analytical formula for the o
SDP (stochastic dynamic programming) control strategy can mine the travel data of drivers, so that the energy-saving potential of vehicles could be improved. However, there are two problems need to be solved in the cu...
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SDP (stochastic dynamic programming) control strategy can mine the travel data of drivers, so that the energy-saving potential of vehicles could be improved. However, there are two problems need to be solved in the current SDP: firstly, the analysis and construction method of driver's typical driving cycle is not clear;secondly, the adaptability of SDP algorithm to a typical driving cycle is insufficient. To solve the above problems, a driving cycle construction method for off-line SDP solution is proposed, which is based on "analysis, dimension reduction and clustering" process. In addition, a coupled control strategy (ECMS-SDP) based on driving conditions identification is developed. Because it is difficult to predict the driving conditions in real time, the working part of the coupled control strategy is calculated by the method of improved random forest. The simulation results show that ECMS-SDP control strategy can save 8%-15% fuel on average compared with CD-CS control strategy, and can save 4%-7% fuel on average compared with ECMS control strategy. The results prove that the ECMS-SDP coupled control strategy can respond well to the changing driving environment, and the fuel economy of vehicles is enhanced.
The construction and operation of linear infrastructure has major impacts on biodiversity through loss of habitat, increased mortality and loss of connectivity. In particular, minimising the impact of roads which pass...
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The construction and operation of linear infrastructure has major impacts on biodiversity through loss of habitat, increased mortality and loss of connectivity. In particular, minimising the impact of roads which pass through ecologically sensitive areas on surrounding species at the construction and operational phases is critical for conservation. However, potential impacts are rarely known perfectly at the construction phase and early in the operational phase. To address this problem, a company could build flexibility into road operation so that it can respond rapidly to future ecological impacts if necessary. In this paper we analyse the value of this flexibility using stochastic dynamic programming and use the results to guide a global search algorithm to find high value roads in the region. We consider flexibility in terms of the proportion of traffic volume routed along the road, with the remainder passing along an existing higher-cost, lower-impact road. We applied this to an example scenario where a road must be routed through a region with a vulnerable species present. By incorporating flexibility, the proposed model was able to find a road that met a desired ending population of animals and was more valuable than roads found under existing design alternatives.
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 ...
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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.
Empirical studies have shown strong evidence that a seller with a better customer feedback score is able to charge a higher price for his/her product or service. We study the problem faced by the seller of setting the...
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Empirical studies have shown strong evidence that a seller with a better customer feedback score is able to charge a higher price for his/her product or service. We study the problem faced by the seller of setting the price with the objective of maximizing expected revenue over a finite number of periods. Modeling the problem requires building a system of processes that includes: (1) the customer arrival and formation of customer reservation price;(2) the customer feedback collection and aggregation into a Seller Service Rating (SSR);and (3) determining how much to charge customers. Because of the technical difficulty in finding analytical solutions that fully reflect the closed-loop interconnections between these processes, we develop a simulation-optimization approach to solve the problem. We present a computational study and report on results of numerical experiments providing interesting insights on how the retailer could startegically align the price he charges with his service quality performance.
This study covers the model predictive control of linear discrete-time systems subject to stochastic additive disturbances and state chance constraints. The stochastic optimal control problem is reformulated in a dyna...
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This study covers the model predictive control of linear discrete-time systems subject to stochastic additive disturbances and state chance constraints. The stochastic optimal control problem is reformulated in a dynamicprogramming fashion to obtain a closed-loop performance and is solved using the interior-point method combined with a Riccati-based approach. The proposed method eliminates active sets in conventional explicit model predictive control and does not suffer from the curse of dimensionality because it finds the value function and feedback policy only for a given initial state using the interior-point method. Moreover, the proposed method is proven to converge globally to the optimal solution Q-superlinearly. The numerical experiment shows that the proposed method achieves a less conservative performance with a low computational complexity compared to existing methods.
Patient-reported outcomes (PROs) play an increasingly important role in medical decision making. Yet, patients whose objectives differ from their physician's may strategically report symptoms to alter treatment de...
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Patient-reported outcomes (PROs) play an increasingly important role in medical decision making. Yet, patients whose objectives differ from their physician's may strategically report symptoms to alter treatment decisions. For example, athletes may underreport symptoms to expedite return-to-play (RTP) from sports-related concussion (SRC). Thus, clinicians must implement treatment policies that mitigate the Price of Naivete, that is, the reduction in health outcomes due to naively believing strategically reported symptoms. In this study, we analyze dynamic treatment cessation decisions with strategic patients. Specifically, we formulate the Behavior-Aware Partially Observable Markov Decision Process (BA-POMDP), which optimizes the timing of treatment cessation decisions while accounting for known symptom-reporting behaviors. We then analytically characterize the BA-POMDP's optimal policy, leading to several practical insights. Next, we formulate the Behavior-Learning Partially Observable Markov Decision Process (BL-POMDP), which extends the BA-POMDP by learning a patient's symptom-reporting behavior over time. We show that the BL-POMDP is decomposable into several BA-POMDPs, allowing us to leverage the BA-POMDP's structural properties for solving the BL-POMDP. Then, we apply the BL-POMDP to RTP from SRC using data from 29 institutions across the United States. We estimate the Price of Naivete by comparing the BL-POMDP to naive benchmark policies. Accordingly, the BL-POMDP reduces premature RTP by over 44% and provides up to 3.63 additional health-adjusted athletic exposures per athlete compared to current practice. Overall, changing the interpretation of reported symptoms can better reduce the Price of Naivete over adjusting treatment cessation thresholds. Therefore, to improve patients' health outcomes, clinicians must understand how strategic behavior manifests in PROs.
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