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Relevant bibliographies by topics / BILEVEL PROGRAMMING FRAMEWORK

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Academic literature on the topic 'BILEVEL PROGRAMMING FRAMEWORK'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "BILEVEL PROGRAMMING FRAMEWORK"

1

Ryu, Jun-Hyung, Vivek Dua, and Efstratios N. Pistikopoulos. "A bilevel programming framework for enterprise-wide process networks under uncertainty." Computers & Chemical Engineering 28, no. 6-7 (2004): 1121–29. http://dx.doi.org/10.1016/j.compchemeng.2003.09.021.

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2

Alnowibet, Khalid A., Ahmad M. Alshamrani, and Adel F. Alrasheedi. "A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power." Energies 16, no. 7 (2023): 3256. http://dx.doi.org/10.3390/en16073256.

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Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The model enables the planner to make a trade-off between the market power indices and the investment cost. Using comparable results of the IEEE 118-bus system, we show that the proposed TEP outperforms the existing models in terms of market power indices and facilitates open access to the transmission network for all market participants.

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3

Park, Minyoung, and Amelia Regan. "Capacity Modeling in Transportation." Transportation Research Record: Journal of the Transportation Research Board 1906, no. 1 (2005): 97–104. http://dx.doi.org/10.1177/0361198105190600112.

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A conceptual framework is presented for modeling the capacity of multimodal freight transportation networks. A review is provided on the evolution of capacity models for use in transportation systems planning and investment, and recent advances toward a system-oriented, multimodal capacity model are discussed in depth. A logical network capacity model based on bilevel programming is proposed.

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4

Fernandez-Blanco, Ricardo, José M. Arroyo, and Natalia Alguacil. "A Unified Bilevel Programming Framework for Price-Based Market Clearing Under Marginal Pricing." IEEE Transactions on Power Systems 27, no. 1 (2012): 517–25. http://dx.doi.org/10.1109/tpwrs.2011.2161348.

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5

Zang, Guangzhi, Meng Xu, and Ziyou Gao. "High-occupancy Vehicle Lanes and Tradable Credits Scheme for Traffic Congestion Management: A Bilevel Programming Approach." PROMET - Traffic&Transportation 30, no. 1 (2018): 1–10. http://dx.doi.org/10.7307/ptt.v30i1.2300.

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High-occupancy vehicle (HOV) lanes, which are designed so as to encourage more people to use high-capacity travel modes and thus move more people in a single roadway lane, have been implemented as a lane management measure to deal with the growing traffic congestion in practice. However, the implementation has shown that some HOV lanes are not able to achieve the expected effects without proper HOV lane settings. In this study, the tradable credits scheme (TCS) is introduced to improve the HOV lane management and an optimal capacity of HOV lanes in a multilane highway is investigated to match TCSs. To approach the investigation, a bilevel programming model is proposed. The upper-level represents the decision of the highway authority and the lower-level follows the commuters’ user equilibrium with deterministic demand. The potential influence of TCSs is further investigated within the proposed framework. A modified genetic algorithm is proposed to solve the bilevel programming model. Numerical examples demonstrate that combining TCSs with the HOV lane management can obviously mitigate traffic congestion.

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6

Chen, Hui-Ju. "A two-level vertex-searching global algorithm framework for bilevel linear fractional programming problems." Systems Science & Control Engineering 8, no. 1 (2020): 488–99. http://dx.doi.org/10.1080/21642583.2020.1805815.

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7

Burgard, Anthony P., Priti Pharkya, and Costas D. Maranas. "Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization." Biotechnology and Bioengineering 84, no. 6 (2003): 647–57. http://dx.doi.org/10.1002/bit.10803.

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8

Epshteyn, A., and G. DeJong. "Generative Prior Knowledge for Discriminative Classification." Journal of Artificial Intelligence Research 27 (September 25, 2006): 25–53. http://dx.doi.org/10.1613/jair.1934.

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We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs) to utilize prior knowledge specified in the generative setting. The dual objective of fitting the data and respecting prior knowledge is formulated as a bilevel program, which is solved (approximately) via iterative application of second-order cone programming. To test our approach, we consider the problem of using WordNet (a semantic database of English language) to improve low-sample classification accuracy of newsgroup categorization. WordNet is viewed as an approximate, but readily available source of background knowledge, and our framework is capable of utilizing it in a flexible way.

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9

Yang, Xia, Chenyang Wang, Xiaozheng He, Hedi Zhang, and Guangming Xu. "Location Optimization for Community Smart Parcel Lockers Based on Bilevel Programming." Journal of Advanced Transportation 2023 (June 2, 2023): 1–18. http://dx.doi.org/10.1155/2023/1998188.

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With the rapid development of e-commerce and the dramatic upsurge in direct-to-consumer deliveries, the last-mile problem has become increasingly apparent. With the distinct advantages of bringing economies of scale and providing 24/7 contactless self-service, smart parcel lockers play a critical role in solving the last-mile problem. However, due to a lack of planning, myopia expansion, and an ambiguous profit model, smart parcel locker suppliers in China have been suffering huge economic losses, restricting their further development. In the study, on the basis of an in-depth analysis of the cost elements and major revenue sources of smart parcel lockers, we propose a bilevel programming model to optimize the location of community smart parcel lockers with the upper-level model maximizing the profit of a third-party smart parcel locker supplier and the lower-level model maximizing user satisfaction. Then, a solution algorithm based on the genetic algorithm is proposed. Finally, some numerical experiments are carried out based on a medium-scale residential community in Jiading District, Shanghai. The sensitivity analyses conducted in this study reveal how the user satisfaction evaluation and the investment budget influence the expected profit. The modelling framework and numerical results can provide third-party smart parcel locker suppliers with significant theoretical support and practical guidance on planning the investment budget and optimizing the smart parcel locker locations to maximize their profit.

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10

Kalashnikov, Vyacheslav V., and Roger Z. Ríos-Mercado. "A natural gas cash-out problem: A bilevel programming framework and a penalty function method." Optimization and Engineering 7, no. 4 (2006): 403–20. http://dx.doi.org/10.1007/s11081-006-0347-z.

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