Gotowa bibliografia na temat „Bid Optimization”
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Artykuły w czasopismach na temat "Bid Optimization"
Balakrishnan, Raju, i Rushi P. Bhatt. "Real-Time Bid Optimization for Group-Buying Ads". ACM Transactions on Intelligent Systems and Technology 5, nr 4 (23.01.2015): 1–21. http://dx.doi.org/10.1145/2532441.
Pełny tekst źródłaMilano, Michela, i Alessio Guerri. "Bid evaluation in combinatorial auctions: optimization and learning". Software: Practice and Experience 39, nr 13 (10.09.2009): 1127–55. http://dx.doi.org/10.1002/spe.930.
Pełny tekst źródłaTandale, Akshaykumar, Chaitanya Shirsath, Bharat Vigne, Yash Dane i 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, nr 5 (31.05.2023): 951–54. http://dx.doi.org/10.22214/ijraset.2023.51635.
Pełny tekst źródłaArya, A., SPS Mathur i 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.
Pełny tekst źródłaZhu, Zhong Rong, Xin Zhe Li i Zheng Song Wu. "Analysis on Optimization of Dividing Construction Bid-Section Based on Safety Risks". Advanced Materials Research 912-914 (kwiecień 2014): 1571–75. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1571.
Pełny tekst źródłaXinyi, Yang, Chen Han, Chen Liu, Xie Ying i Chen Bing. "Research on Intelligent Verification Technology of Bid Evaluation Results". BCP Business & Management 45 (27.04.2023): 402–6. http://dx.doi.org/10.54691/bcpbm.v45i.4961.
Pełny tekst źródłaNuara, Alessandro, Francesco Trovò, Nicola Gatti i Marcello Restelli. "Online joint bid/daily budget optimization of Internet advertising campaigns". Artificial Intelligence 305 (kwiecień 2022): 103663. http://dx.doi.org/10.1016/j.artint.2022.103663.
Pełny tekst źródłaYan, Fang, Yanfang Ma, Manjing Xu i 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.
Pełny tekst źródłaIslam, Md Mainul, i Sherif Mohamed. "Bid-Winning Potential Optimization for Concession Schemes with Imprecise Investment Parameters". Journal of Construction Engineering and Management 135, nr 8 (sierpień 2009): 690–700. http://dx.doi.org/10.1061/(asce)co.1943-7862.0000032.
Pełny tekst źródłaKuyzu, Gültekin, Çağla Gül Akyol, Özlem Ergun i Martin Savelsbergh. "Bid price optimization for truckload carriers in simultaneous transportation procurement auctions". Transportation Research Part B: Methodological 73 (marzec 2015): 34–58. http://dx.doi.org/10.1016/j.trb.2014.11.012.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaWang, 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.
Pełny tekst źródłaIncludes 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.
Pełny tekst źródłaI 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.
Pełny tekst źródłaBalkan, 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.
Pełny tekst źródłainflation 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.
Pełny tekst źródłaTruckload 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.
Pełny tekst źródłaTaylor, Kendra C. "Sequential Auction Design and Participant Behavior". Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7250.
Pełny tekst źródłaMüller, Sibylle D. "Bio-inspired optimization algorithms for engineering applications /". Zürich, 2002. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14719.
Pełny tekst źródłaZuniga, Virgilio. "Bio-inspired optimization algorithms for smart antennas". Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5766.
Pełny tekst źródłaKsiążki na temat "Bid Optimization"
Pardalos, Panos, Mario Pavone, Giovanni Maria Farinella i Vincenzo Cutello, red. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27926-8.
Pełny tekst źródłaNicosia, Giuseppe, Panos Pardalos, Giovanni Giuffrida i Renato Umeton, red. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72926-8.
Pełny tekst źródłaPardalos, Panos M., Piero Conca, Giovanni Giuffrida i Giuseppe Nicosia, red. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51469-7.
Pełny tekst źródłaEmrouznejad, Ali, red. Big Data Optimization: Recent Developments and Challenges. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30265-2.
Pełny tekst źródłaNijdam, Jelle Luutzen. Behaviour and optimization of packed bed regenerators. Eindhoven: Eindhoven University of Technology, 1995.
Znajdź pełny tekst źródłaWang, John. Encyclopedia of business analytics and optimization. Hershey, PA: Business Science Reference, 2014.
Znajdź pełny tekst źródłaZhan, Jianfeng, Rui Han i Chuliang Weng, red. Big Data Benchmarks, Performance Optimization, and Emerging Hardware. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13021-7.
Pełny tekst źródłaZhan, Jianfeng, Rui Han i Roberto V. Zicari, red. Big Data Benchmarks, Performance Optimization, and Emerging Hardware. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29006-5.
Pełny tekst źródłaChoi, Tsan-Ming, Jianjun Gao, James H. Lambert, Chi-Kong Ng i Jun Wang, red. 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.
Pełny tekst źródła1946-, Elshishini S. S., red. Modelling, simulation, and optimization of industrial fixed bed catalytic reactors. Yverdon, Switzerland: Gordon and Breach Science Publishers, 1993.
Znajdź pełny tekst źródłaCzęści książek na temat "Bid Optimization"
Peng, Kun, Colin Boyd i Ed Dawson. "Optimization of Electronic First-Bid Sealed-Bid Auction Based on Homomorphic Secret Sharing". W Progress in Cryptology – Mycrypt 2005, 84–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554868_7.
Pełny tekst źródłaAsadpour, Arash, Mohammad Hossein Bateni, Kshipra Bhawalkar i Vahab Mirrokni. "Concise Bid Optimization Strategies with Multiple Budget Constraints". W Web and Internet Economics, 263–76. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13129-0_21.
Pełny tekst źródłaRoy, Pritam. "A Memetic Evolutionary Algorithm-Based Optimization for Competitive Bid Data Analysis". W Evolutionary Computing and Mobile Sustainable Networks, 917–25. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5258-8_84.
Pełny tekst źródłaZhu, Xiaobo, Qian Yu i Xianjia Wang. "Strategic Learning in the Sealed-Bid Bargaining Mechanism by Particle Swarm Optimization Algorithm". W 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.
Pełny tekst źródłaDörpinghaus, Jens, Vera Weil, Sebastian Schaaf i Alexander Apke. "Optimization". W Studies in Big Data, 361–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08411-9_13.
Pełny tekst źródłaTomlin, W. Craig. "Conclusion: The Big Picture". W UX Optimization, 177–93. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3867-7_10.
Pełny tekst źródłaFrench, Mark. "Optimization: The Big Idea". W Fundamentals of Optimization, 1–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76192-3_1.
Pełny tekst źródłaNazareth, John Lawrence. "Optimization: The Big Picture". W An Optimization Primer, 93–98. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4684-9388-7_10.
Pełny tekst źródłaCastillo, Oscar, i Patricia Melin. "Bio-Inspired Optimization Methods". W 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.
Pełny tekst źródłaDing, Yongsheng, Lei Chen i Kuangrong Hao. "Bio-Inspired Optimization Algorithms". W Studies in Systems, Decision and Control, 317–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6689-4_8.
Pełny tekst źródłaStreszczenia konferencji na temat "Bid Optimization"
Schuyler, John R. "Bid Optimization With Monte Carlo Simulation". W SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/130141-ms.
Pełny tekst źródłaEven Dar, Eyal, Vahab S. Mirrokni, S. Muthukrishnan, Yishay Mansour i Uri Nadav. "Bid optimization for broad match ad auctions". W the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526741.
Pełny tekst źródłaYu, Linfei, Kun She i Changyuan Yu. "A Primal Dual Approach for Dynamic Bid Optimization". W 2010 IEEE 16th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2010. http://dx.doi.org/10.1109/icpads.2010.75.
Pełny tekst źródłaKong, Deguang, Konstantin Shmakov i Jian Yang. "An Inflection Point Approach for Advertising Bid Optimization". W Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3186944.
Pełny tekst źródłaYang, Xun, Yasong Li, Hao Wang, Di Wu, Qing Tan, Jian Xu i Kun Gai. "Bid Optimization by Multivariable Control in Display Advertising". W 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.
Pełny tekst źródłaBalakrishnan, Raju, i Rushi P. Bhatt. "Real-time bid optimization for group-buying ads". W the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398502.
Pełny tekst źródłaBorgs, Christian, Jennifer Chayes, Nicole Immorlica, Kamal Jain, Omid Etesami i Mohammad Mahdian. "Dynamics of bid optimization in online advertisement auctions". W the 16th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1242572.1242644.
Pełny tekst źródłaFan, Rui, i Erick Delage. "Risk-Aware Bid Optimization for Online Display Advertisement". W 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.
Pełny tekst źródłaWenjun Chen i Shuang Yang. "Optimization model of bid evaluation on ELECTRE-III method". W 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5689992.
Pełny tekst źródłaKarlsson, Niklas, i Qian Sang. "Adaptive Bid Shading Optimization of First-Price Ad Inventory". W 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9482665.
Pełny tekst źródłaRaporty organizacyjne na temat "Bid Optimization"
Schmidt, C. A., M. J. Brower, J. J. Coogan i R. A. Tennant. Optimization of a packed bed reactor for liquid waste treatment. Office of Scientific and Technical Information (OSTI), listopad 1993. http://dx.doi.org/10.2172/10193814.
Pełny tekst źródłaGarcia, Alfredo. Bio-Inspired Schemes for Global Optimization and Online Distributed Search. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 2012. http://dx.doi.org/10.21236/ada567710.
Pełny tekst źródłaSmith, J. C. Enhanced Cutting Plane Techniques for Bi-Level Optimization Algorithms. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 2008. http://dx.doi.org/10.21236/ada481838.
Pełny tekst źródłaHoussainy, Sammy, Khanh Nguyen Cu i Ramin Faramarzi. Final Optimization Report: Empowering Energy Efficiency in Existing Big-Box Retail/Grocery Stores. Office of Scientific and Technical Information (OSTI), wrzesień 2020. http://dx.doi.org/10.2172/1665839.
Pełny tekst źródłaKnotek-Smith, Heather, i Catherine Thomas. Microbial dynamics of a fluidized bed bioreactor treating perchlorate in groundwater. Engineer Research and Development Center (U.S.), wrzesień 2022. http://dx.doi.org/10.21079/11681/45403.
Pełny tekst źródłaMartin, A. Laser Powder Bed Fusion Additive Manufacturing In-Process Monitoring and Optimization Using Thermionic Emission Detection. Office of Scientific and Technical Information (OSTI), lipiec 2020. http://dx.doi.org/10.2172/1647152.
Pełny tekst źródłaGabelmann, Jeffrey, i Eduardo Gildin. A Machine Learning-Based Geothermal Drilling Optimization System Using EM Short-Hop Bit Dynamics Measurements. Office of Scientific and Technical Information (OSTI), kwiecień 2020. http://dx.doi.org/10.2172/1842454.
Pełny tekst źródłaLiu, Xiaoyue, Yirong Zhou, Ran Wei, Aaron Golub i 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.
Pełny tekst źródłaKing, Wayne. Process Control for Defect Mitigation in Laser Powder Bed Fusion Additive Manufacturing. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, maj 2023. http://dx.doi.org/10.4271/epr2023011.
Pełny tekst źródłaCorum, Zachary, Ethan Cheng, Stanford Gibson i 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.), kwiecień 2022. http://dx.doi.org/10.21079/11681/43887.
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