作者：
Park, SeJoonHa, ChunghunKangwon Natl Univ
Div Energy Resources Engn & Ind Engn KNU Chuncheon Campus1 Gangwondaehakgil Chuncheon Si 24341 Gangwon Do South Korea Hongik Univ
Dept Ind Engn 94 Wausan Ro Seoul 04066 South Korea
The ANOVA gauge repeatability and reproducibility study (AGRR) is one of the most popular assessment tools for evaluating the precision of a measurement system. Adequacy of a measurement system critically depends on e...
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The ANOVA gauge repeatability and reproducibility study (AGRR) is one of the most popular assessment tools for evaluating the precision of a measurement system. Adequacy of a measurement system critically depends on experimental design, namely, numbers of operators, sampled parts, and replicates. Some previous studies have suggested several rules of thumb and an optimization approach that determine a proper experimental design for AGRR. The usage of those, however, is limited because the procedures are not systematic and a disordered sequence in use exists. This research aims at proposing a systematic procedure to determine the optimal experimental design for AGRR with minimum prior knowledge. To achieve this goal, we adopted the sample average approximation for finding optimal solutions at possible ranges of parameters. Extensive simulation results show that there is a relationship between confidence interval of signal-to-noise ratio and optimal experimental design. Finally, incorporating a regression analysis, we developed a systematic procedure to determine an optimal experimental design before conducting the AGRR. (C) 2020 Elsevier Ltd. All rights reserved.
This paper presents a methodology based on auction theory to form a virtual power plant (VPP) coalition of heterogeneous distributed energy resources (DERs). Moreover, in this procedure, the competition between VPPs f...
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This paper presents a methodology based on auction theory to form a virtual power plant (VPP) coalition of heterogeneous distributed energy resources (DERs). Moreover, in this procedure, the competition between VPPs for attracting DERs is considered. In this model, the VPP has the ability to participate in the day-ahead energy market, become involved in the future energy market and sign bilateral contracts in a medium or long-term time horizon. Also, the ability of arbitrage between different markets is considered for the VPP. Moreover, the risk of energy trading is taken into account with the risk measure method, conditional value at risk. Due to the uncertain nature of pool prices, a stochastic programming approach consist of two stages is developed for modeling the decision-making problem. In the first stage, the VPP participates in an auction and signs the renting capacity contracts with DERs based on auction results. The future market contracts are signed in this stage as well. In the next stage, the decisions regarding bilateral contracts, pool participation, and DERs planning will be made. Also, the uncertainty of the quantity and the price of bilateral contracts are considered. The efficiency of the model is analyzed in a few case studies.
Selecting the most sustainable biomass sources for biofuel production can overcome sever obstacles in the reliable supply of biomass feedstock, such as its seasonal availability. Developing a biomass portfolio can gen...
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Selecting the most sustainable biomass sources for biofuel production can overcome sever obstacles in the reliable supply of biomass feedstock, such as its seasonal availability. Developing a biomass portfolio can generally improve the sustainability of future biofuel systems. Bioethanol is an attractive biofuel having the potential for enhancing energy security and environmental safety over fossil fuels. The decisions to be made comprise the optimum amount of bioethanol feedstocks, and the investments to make in alternative activities. Uncertainty is assumed for biomass conversion rate to bioethanol, which is described by a set of scenarios. Therefore, for the entire planning horizon, a tree of scenarios is organized. The problem is formulated as a multi-stage stochastic linear programming problem. The goal is to maximize the total financial benefit. The risk associated with biomass availability affects the viability of biomass utilization and, therefore, should be taken into account when evaluating and analyzing biomass portfolio. In addition, the conditional value at risk (CVaR) is adopted to consider the risk hedge. Using a real case study, computational experiments are presented, showing that the stochastic approach is worth considering in these types of problems. Case study results demonstrate the validity of the proposed model. (C) 2020 Elsevier Ltd. All rights reserved.
In order to achieve better system performance, the concept of an ad-hoc mobile cloud, whereby a mobile device can access resources such as processing, data or storage at other neighbouring nodes, has been proposed. Th...
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In order to achieve better system performance, the concept of an ad-hoc mobile cloud, whereby a mobile device can access resources such as processing, data or storage at other neighbouring nodes, has been proposed. The difficulty that arises with this concept is the mobility of nearby devices, i.e., a neighboring device may move out of range before it can communicate its results back to the source device. In this paper, we propose a workload assignment scheme between a source device and nearby mobile devices that takes into account the randomness of the connection time between these devices. In order to cope with this randomness, we adopt a multi-stage stochastic programming approach which is able to take posterior recourse actions to compensate for inaccurate predictions. Moreover, in order to motivate the available mobile devices to cooperate, we formulate a distributed multi-stage stochastic buyer-seller game (MSSBSG) in which different mobile devices attempt to maximize their utilities. Our results show that the stochastic programming approach outperforms several baseline schemes and the MSSBSG approach effectively promotes cooperation between mobile devices and achieves the best overall performance compared to simpler approaches that do not take stochastic operating conditions into account.
Deterministic equivalent models reformulate optimization problems from a computational perspective. Nonetheless, these models become computationally intractable quickly when the number of stages increase. In this cont...
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Deterministic equivalent models reformulate optimization problems from a computational perspective. Nonetheless, these models become computationally intractable quickly when the number of stages increase. In this context, a framework to reduce the size of scenario tree and multistage stochastic optimization problems is proposed. Scenario trees are generated using the Knuth transformation for a more compact representation. Moreover, the optimization model is described by using an implicit extensive form approach. The framework is tested in an asset-liability management multistage stochastic model with joint chance constraints, making it possible to acquire the optimal solution for large instances without any relaxation or decomposition mechanism. (C) 2020 Elsevier B.V. All rights reserved.
This paper addresses the multi-period ambulance redeployment planning problem in a two-tiered Emergency Medical System (EMS) where two types of ambulances are used to respond to two categories of emergency calls. In o...
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This paper addresses the multi-period ambulance redeployment planning problem in a two-tiered Emergency Medical System (EMS) where two types of ambulances are used to respond to two categories of emergency calls. In order to account for the uncertainty inherent to both categories of demand, we propose a two-stage stochastic programming model that aims at finding a cost-effective ambulance redeployment. The model tries to minimize the total cost, which encompasses ambulance relocation cost, the dispatching cost, and the penalty cost incurred by the unsatisfied demand, over a multi-period planning horizon. In order to overcome the computational complexity of the proposed model, two heuristics are proposed: a Temporal Decomposition Heuristic (HDT), and a Lagrangian Relaxation based Heuristic (SBG). A simulation model is then proposed to evaluate the service level of the EMS system and ambulance utilization while accounting for more realistic features of the problem. The computational experiments are carried out using real-world data provided by the EMS system of the northern region of Tunisia. The results show the excellent performance of HDT as it provides a near-optimal solution within a reasonable computational time. The simulation also demonstrates that the service level of the EMS system is higher if HDT is used. (C) 2020 Elsevier Ltd. All rights reserved.
This paper develops a multistage stochastic programming to optimally solve the distribution problem of refined products. The stochastic model relies on a time series analysis, as well as on a scenario tree analysis, i...
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This paper develops a multistage stochastic programming to optimally solve the distribution problem of refined products. The stochastic model relies on a time series analysis, as well as on a scenario tree analysis, in order to effectively deal and represent uncertainty in oil price and demand. The ARIMA methodology is explored to study the time series of the random parameters aiming to provide their future outcomes, which are then used in the scenario-based approach. As the designed methodology leads to a large scale optimization problem, a scenario reduction approach is employed to compress the problem size and improve its computational performance. A real-world example motivates the case study, based on the downstream oil supply chain of mainland Portugal, which is used to validate the applicability of the stochastic model. The results explicitly indicate the performance of the designed approach in tackling large and complex problems, where uncertainty is present. (C) 2017 Elsevier Ltd. All rights reserved.
The modular construction method has been adopted extensively by the construction sector for pursuing higher building quality and better project efficiency. However, the employment of this new construction method has n...
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The modular construction method has been adopted extensively by the construction sector for pursuing higher building quality and better project efficiency. However, the employment of this new construction method has not only altered the definition of construction supply chains, but also poses new challenges to the logistics system which has conventionally focused on raw material transportation. This challenge is exacerbated in the transport and inventory aspects when the project is executed in urban settings, owing to the frequent traffic congestion, crowded environment, as well as the bulkiness and delicacy of finished modules. This study develops a multi-stage stochastic programming model for identifying the optimal supply chain configuration for the modular construction method. Site demand is considered to be stochastic, forcing project managers to make several operational decisions at multiple time points during project execution. The developed model can provide the best production, transportation and inventory plans, as well as the most favourable initial inventory preparation schemes. Furthermore, we have proven that the implementation of multi-stage stochastic programming model can yield more economical and risk-averse solutions than the two-stage stochastic programming approach.
In this paper, we propose a stochastic programming approach to perform optimal and robust offshore flight scheduling from a service level perspective, reducing flight delays. The two-stage stochastic programming is re...
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In this paper, we propose a stochastic programming approach to perform optimal and robust offshore flight scheduling from a service level perspective, reducing flight delays. The two-stage stochastic programming is reduced to a deterministic equivalent linear program and, considering the combinatorial characteristic of scheduling problems, we use Sample Average Approximation to generate scenarios. A Discrete Event Simulation model is developed to compare the stochastic and deterministic approaches. Numerical results indicate that a stochastic approach to offshore flight scheduling can reduce unpredictable delays, which have a major impact on passengers, without significantly increasing aircraft idle time. In addition, the stochastic approach allows dealing with operational downtime windows with uncertainties in duration and occurrence.
There is no doubt that portfolio selection problems play an important role in finance, which recommend valuable choices among various investment strategies. In our study, we consider the portfolio optimization problem...
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ISBN:
(纸本)9781665449502
There is no doubt that portfolio selection problems play an important role in finance, which recommend valuable choices among various investment strategies. In our study, we consider the portfolio optimization problem as a stochastic linear programming problem. By transforming the stochastic programming problem to deterministic problems by applying the probability models, we establish the variant of Markowitz model, which is related to Sharpe ratios. Then the portfolio selection problem is transferred to an optimization problem over the efficient solution set of bi-objective programming problems. This equivalent problem is solved by a multi-objective evolutionary algorithm with less time consumption due to the population approach. An experiment on Vietnam stock market data will be implemented and gives detailed analysis about trade-off of objective functions.
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