Literatura académica sobre el tema "Bid Optimization"
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Artículos de revistas sobre el tema "Bid Optimization"
Balakrishnan, Raju y Rushi P. Bhatt. "Real-Time Bid Optimization for Group-Buying Ads". ACM Transactions on Intelligent Systems and Technology 5, n.º 4 (23 de enero de 2015): 1–21. http://dx.doi.org/10.1145/2532441.
Texto completoMilano, Michela y Alessio Guerri. "Bid evaluation in combinatorial auctions: optimization and learning". Software: Practice and Experience 39, n.º 13 (10 de septiembre de 2009): 1127–55. http://dx.doi.org/10.1002/spe.930.
Texto completoTandale, Akshaykumar, Chaitanya Shirsath, Bharat Vigne, Yash Dane y Dr Ayub Sheikh. "Analysis & Optimization to Improve the Tedious Tendering Process in Construction Industry". International Journal for Research in Applied Science and Engineering Technology 11, n.º 5 (31 de mayo de 2023): 951–54. http://dx.doi.org/10.22214/ijraset.2023.51635.
Texto completoArya, A., SPS Mathur y M. Dubey. "Impact of emission trading and renewable energy support scheme on the optimality of generator side bidding". E3S Web of Conferences 167 (2020): 05008. http://dx.doi.org/10.1051/e3sconf/202016705008.
Texto completoZhu, Zhong Rong, Xin Zhe Li y Zheng Song Wu. "Analysis on Optimization of Dividing Construction Bid-Section Based on Safety Risks". Advanced Materials Research 912-914 (abril de 2014): 1571–75. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1571.
Texto completoXinyi, Yang, Chen Han, Chen Liu, Xie Ying y Chen Bing. "Research on Intelligent Verification Technology of Bid Evaluation Results". BCP Business & Management 45 (27 de abril de 2023): 402–6. http://dx.doi.org/10.54691/bcpbm.v45i.4961.
Texto completoNuara, Alessandro, Francesco Trovò, Nicola Gatti y Marcello Restelli. "Online joint bid/daily budget optimization of Internet advertising campaigns". Artificial Intelligence 305 (abril de 2022): 103663. http://dx.doi.org/10.1016/j.artint.2022.103663.
Texto completoYan, Fang, Yanfang Ma, Manjing Xu y Xianlong Ge. "Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective". Mathematical Problems in Engineering 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/1728512.
Texto completoIslam, Md Mainul y Sherif Mohamed. "Bid-Winning Potential Optimization for Concession Schemes with Imprecise Investment Parameters". Journal of Construction Engineering and Management 135, n.º 8 (agosto de 2009): 690–700. http://dx.doi.org/10.1061/(asce)co.1943-7862.0000032.
Texto completoKuyzu, Gültekin, Çağla Gül Akyol, Özlem Ergun y Martin Savelsbergh. "Bid price optimization for truckload carriers in simultaneous transportation procurement auctions". Transportation Research Part B: Methodological 73 (marzo de 2015): 34–58. http://dx.doi.org/10.1016/j.trb.2014.11.012.
Texto completoTesis sobre el tema "Bid Optimization"
Yu, Zhenjian. "Strategic sourcing and bid optimization for ocean freight /". View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?IEEM%202004%20YU.
Texto completoWang, Qian. "Pre-bid network analysis for transportation procurement auction under stochastic demand". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41727.
Texto completoIncludes bibliographical references (p. 67-68).
Transportation procurement is one of the most critical sourcing decisions to be made in many companies. This thesis addresses a real-life industrial problem of creating package bids for a company's transportation procurement auction. The purpose of offering package bids is to increase the carriers' capacity and to improve the reliability of services. In this thesis, we investigate the possibility of forming packages using the company's own distribution network. Effective distribution of packages requires balanced cycles. A balanced cycle is a cycle containing no more than 3 nodes with equal loads (or volume of package) on every link in the cycle. We develop mixed-integer programs to find the maximum amount of network volume that can be covered by well-balanced cycles. These models are deterministic models that provide a rough guide on the optimal way of package formation when loads are known in advance. Since demand is random in real life, we perform a stochastic analysis of the problem using various techniques including simulation, probabilistic analysis and stochastic programming. Results from the stochastic analysis show that the effectiveness of package distribution depends on how we allocate the volumes on the lanes to create balanced cycles. If we always assign a fixed proportion of the lanes' volumes to the cycles, then it is only possible to have well-balanced cycles when the average volumes on the lanes are very large, validating the advantage of joint bids between several companies. However, if we assign a different proportion of the lanes' volumes to the cycles each time demand changes, then it is possible to create cycles that are balanced most of the time. An approximated solution method is provided to obtain a set of balanced cycles that can be bid out.
by Qian Wang.
S.M.
Aly, Mazen. "Automated Bid Adjustments in Search Engine Advertising". Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210651.
Texto completoI digital marknadsföring tillåter de dominerande sökmotorerna en annonsör att ändra sina bud med hjälp av så kallade budjusteringar baserat på olika dimensioner i sökförfrågan, i syfte att kompensera för olika värden de dimensionerna medför. I det här arbetet tas en modell fram för att sätta budjusteringar i syfte att öka mängden konverteringar och samtidigt minska kostnaden per konvertering. En statistisk modell används för att välja kampanjer och dimensioner som behöver justeringar och flera olika tekniker för att bestämma justeringens storlek, som kan spänna från -90% till 900%, undersöks. Utöver detta tas en evalueringsmetod fram som använder en kampanjs historiska data för att utvärdera de olika metoderna och validera olika tillvägagångssätt. Vi studerar interaktionsproblemet mellan olika dimensioners budjusteringar och en lösning formuleras. Realtidsexperiment visar att vår modell för budjusteringar förbättrade prestandan i marknadsföringskampanjerna med statistisk signifikans. Konverteringarna ökade med 9% och kostnaden per konvertering minskade med 10%.
Mikheev, Sergej [Verfasser]. "Portfolio optimization in arbitrary dimensions in the presence of small bid-ask spreads / Sergej Mikheev". Kiel : Universitätsbibliothek Kiel, 2018. http://d-nb.info/1155760778/34.
Texto completoBalkan, Kaan. "Robust Optimization Approach For Long-term Project Pricing". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12612104/index.pdf.
Texto completoinflation rates. We propose a Robust Optimization (RO) approach that can deal with the uncertainties during the project lifecycle through the identification of several discrete scenarios. The bid project&rsquo
s performance measures, other than the monetary measures, for R&
D projects are identified and the problem is formulated as a multi-attribute utility project pricing problem. In our RO approach, the bid pricing problem is decomposed into two parts which are v solved sequentially: the Penalty-Model, and the RO model. In the Penalty-Model, penalty costs for the possible violations in the company&rsquo
s workforce level due to the bid project&rsquo
s workhour requirements are determined. Then the RO model searches for the optimum bid price by considering the penalty cost from the Penalty-Model, the bid project&rsquo
s performance measures, the probability of winning the bid for a given bid price and the deviations in the bid project&rsquo
s cost. Especially for the R&
D type projects, the model tends to place lower bid prices in the expected value solutions in order to win the bid. Thus, due to the possible deviations in the project cost, R&
D projects have a high probability of suffering from a financial loss in the expected value solutions. However, the robust solutions provide results which are more aware of the deviations in the bid project&rsquo
s cost and thus eliminate the financial risks by making a tradeoff between the bid project&rsquo
s benefits, probability of winning the bid and the financial loss risk. Results for the probability of winning in the robust solutions are observed to be lower than the expected value solutions, whereas expected value solutions have higher probabilities of suffering from a financial loss.
Lyu, Ke. "Studies on Auction Mechanism and Bid Generation in the Procurement of Truckload Transportation Services". Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0032.
Texto completoTruckload transportation is a common mode of freight transportation, which accounts for a substantial portion of transportation industry, where shippers procure transportation services from carriers. Transportation service procurement is often realized by auction. Through designing effective auction mechanisms and efficient methods for solving related bid generation problems, shippers and carriers can save costs and increase profits respectively. This thesis studies three problems raised in the procurement of truckload transportation services realized by combinatorial auctions. Firstly, two two-phase combinatorial auction mechanisms are designed with supplementary bundles of requests offered for bid generated by the auctioneer and the carriers respectively in the second phase. Secondly, a column generation algorithm is proposed to solve the bid generation problem appeared in the combinatorial auction. Finally, the bid generation problem is extended to one that considers both multiple periods and uncertainty in truckload transportation service procurement. This stochastic optimization problem is formulated through scenario optimization and deterministic equivalence. To solve this model, a Benders decomposition approach is proposed
Mubark, Athmar. "Computer Science Optimization Of Reverse auction : Reverse Auction". Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-68140.
Texto completoTaylor, Kendra C. "Sequential Auction Design and Participant Behavior". Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7250.
Texto completoMüller, Sibylle D. "Bio-inspired optimization algorithms for engineering applications /". Zürich, 2002. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14719.
Texto completoZuniga, Virgilio. "Bio-inspired optimization algorithms for smart antennas". Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5766.
Texto completoLibros sobre el tema "Bid Optimization"
Pardalos, Panos, Mario Pavone, Giovanni Maria Farinella y Vincenzo Cutello, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27926-8.
Texto completoNicosia, Giuseppe, Panos Pardalos, Giovanni Giuffrida y Renato Umeton, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72926-8.
Texto completoPardalos, Panos M., Piero Conca, Giovanni Giuffrida y Giuseppe Nicosia, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51469-7.
Texto completoEmrouznejad, Ali, ed. Big Data Optimization: Recent Developments and Challenges. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30265-2.
Texto completoNijdam, Jelle Luutzen. Behaviour and optimization of packed bed regenerators. Eindhoven: Eindhoven University of Technology, 1995.
Buscar texto completoWang, John. Encyclopedia of business analytics and optimization. Hershey, PA: Business Science Reference, 2014.
Buscar texto completoZhan, Jianfeng, Rui Han y Chuliang Weng, eds. Big Data Benchmarks, Performance Optimization, and Emerging Hardware. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13021-7.
Texto completoZhan, Jianfeng, Rui Han y Roberto V. Zicari, eds. Big Data Benchmarks, Performance Optimization, and Emerging Hardware. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29006-5.
Texto completoChoi, Tsan-Ming, Jianjun Gao, James H. Lambert, Chi-Kong Ng y Jun Wang, eds. Optimization and Control for Systems in the Big-Data Era. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53518-0.
Texto completo1946-, Elshishini S. S., ed. Modelling, simulation, and optimization of industrial fixed bed catalytic reactors. Yverdon, Switzerland: Gordon and Breach Science Publishers, 1993.
Buscar texto completoCapítulos de libros sobre el tema "Bid Optimization"
Peng, Kun, Colin Boyd y Ed Dawson. "Optimization of Electronic First-Bid Sealed-Bid Auction Based on Homomorphic Secret Sharing". En Progress in Cryptology – Mycrypt 2005, 84–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554868_7.
Texto completoAsadpour, Arash, Mohammad Hossein Bateni, Kshipra Bhawalkar y Vahab Mirrokni. "Concise Bid Optimization Strategies with Multiple Budget Constraints". En Web and Internet Economics, 263–76. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13129-0_21.
Texto completoRoy, Pritam. "A Memetic Evolutionary Algorithm-Based Optimization for Competitive Bid Data Analysis". En Evolutionary Computing and Mobile Sustainable Networks, 917–25. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5258-8_84.
Texto completoZhu, Xiaobo, Qian Yu y Xianjia Wang. "Strategic Learning in the Sealed-Bid Bargaining Mechanism by Particle Swarm Optimization Algorithm". En Lecture Notes in Computer Science, 524–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_64.
Texto completoDörpinghaus, Jens, Vera Weil, Sebastian Schaaf y Alexander Apke. "Optimization". En Studies in Big Data, 361–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08411-9_13.
Texto completoTomlin, W. Craig. "Conclusion: The Big Picture". En UX Optimization, 177–93. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3867-7_10.
Texto completoFrench, Mark. "Optimization: The Big Idea". En Fundamentals of Optimization, 1–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76192-3_1.
Texto completoNazareth, John Lawrence. "Optimization: The Big Picture". En An Optimization Primer, 93–98. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4684-9388-7_10.
Texto completoCastillo, Oscar y Patricia Melin. "Bio-Inspired Optimization Methods". En Recent Advances in Interval Type-2 Fuzzy Systems, 13–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28956-9_3.
Texto completoDing, Yongsheng, Lei Chen y Kuangrong Hao. "Bio-Inspired Optimization Algorithms". En Studies in Systems, Decision and Control, 317–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6689-4_8.
Texto completoActas de conferencias sobre el tema "Bid Optimization"
Schuyler, John R. "Bid Optimization With Monte Carlo Simulation". En SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/130141-ms.
Texto completoEven Dar, Eyal, Vahab S. Mirrokni, S. Muthukrishnan, Yishay Mansour y Uri Nadav. "Bid optimization for broad match ad auctions". En the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526741.
Texto completoYu, Linfei, Kun She y Changyuan Yu. "A Primal Dual Approach for Dynamic Bid Optimization". En 2010 IEEE 16th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2010. http://dx.doi.org/10.1109/icpads.2010.75.
Texto completoKong, Deguang, Konstantin Shmakov y Jian Yang. "An Inflection Point Approach for Advertising Bid Optimization". En Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3186944.
Texto completoYang, Xun, Yasong Li, Hao Wang, Di Wu, Qing Tan, Jian Xu y Kun Gai. "Bid Optimization by Multivariable Control in Display Advertising". En KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330681.
Texto completoBalakrishnan, Raju y Rushi P. Bhatt. "Real-time bid optimization for group-buying ads". En the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398502.
Texto completoBorgs, Christian, Jennifer Chayes, Nicole Immorlica, Kamal Jain, Omid Etesami y Mohammad Mahdian. "Dynamics of bid optimization in online advertisement auctions". En the 16th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1242572.1242644.
Texto completoFan, Rui y Erick Delage. "Risk-Aware Bid Optimization for Online Display Advertisement". En CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3511808.3557436.
Texto completoWenjun Chen y Shuang Yang. "Optimization model of bid evaluation on ELECTRE-III method". En 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5689992.
Texto completoKarlsson, Niklas y Qian Sang. "Adaptive Bid Shading Optimization of First-Price Ad Inventory". En 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9482665.
Texto completoInformes sobre el tema "Bid Optimization"
Schmidt, C. A., M. J. Brower, J. J. Coogan y R. A. Tennant. Optimization of a packed bed reactor for liquid waste treatment. Office of Scientific and Technical Information (OSTI), noviembre de 1993. http://dx.doi.org/10.2172/10193814.
Texto completoGarcia, Alfredo. Bio-Inspired Schemes for Global Optimization and Online Distributed Search. Fort Belvoir, VA: Defense Technical Information Center, abril de 2012. http://dx.doi.org/10.21236/ada567710.
Texto completoSmith, J. C. Enhanced Cutting Plane Techniques for Bi-Level Optimization Algorithms. Fort Belvoir, VA: Defense Technical Information Center, abril de 2008. http://dx.doi.org/10.21236/ada481838.
Texto completoHoussainy, Sammy, Khanh Nguyen Cu y Ramin Faramarzi. Final Optimization Report: Empowering Energy Efficiency in Existing Big-Box Retail/Grocery Stores. Office of Scientific and Technical Information (OSTI), septiembre de 2020. http://dx.doi.org/10.2172/1665839.
Texto completoKnotek-Smith, Heather y Catherine Thomas. Microbial dynamics of a fluidized bed bioreactor treating perchlorate in groundwater. Engineer Research and Development Center (U.S.), septiembre de 2022. http://dx.doi.org/10.21079/11681/45403.
Texto completoMartin, A. Laser Powder Bed Fusion Additive Manufacturing In-Process Monitoring and Optimization Using Thermionic Emission Detection. Office of Scientific and Technical Information (OSTI), julio de 2020. http://dx.doi.org/10.2172/1647152.
Texto completoGabelmann, Jeffrey y Eduardo Gildin. A Machine Learning-Based Geothermal Drilling Optimization System Using EM Short-Hop Bit Dynamics Measurements. Office of Scientific and Technical Information (OSTI), abril de 2020. http://dx.doi.org/10.2172/1842454.
Texto completoLiu, Xiaoyue, Yirong Zhou, Ran Wei, Aaron Golub y Devin Macarthur. Bi-objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity. Transportation Research and Education Center (TREC), 2021. http://dx.doi.org/10.15760/trec.256.
Texto completoKing, Wayne. Process Control for Defect Mitigation in Laser Powder Bed Fusion Additive Manufacturing. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, mayo de 2023. http://dx.doi.org/10.4271/epr2023011.
Texto completoCorum, Zachary, Ethan Cheng, Stanford Gibson y Travis Dahl. Optimization of reach-scale gravel nourishment on the Green River below Howard Hanson Dam, King County, Washington. Engineer Research and Development Center (U.S.), abril de 2022. http://dx.doi.org/10.21079/11681/43887.
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