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Статті в журналах з теми "Bid Optimization"
Balakrishnan, Raju, and Rushi P. Bhatt. "Real-Time Bid Optimization for Group-Buying Ads." ACM Transactions on Intelligent Systems and Technology 5, no. 4 (January 23, 2015): 1–21. http://dx.doi.org/10.1145/2532441.
Повний текст джерелаMilano, Michela, and Alessio Guerri. "Bid evaluation in combinatorial auctions: optimization and learning." Software: Practice and Experience 39, no. 13 (September 10, 2009): 1127–55. http://dx.doi.org/10.1002/spe.930.
Повний текст джерелаTandale, Akshaykumar, Chaitanya Shirsath, Bharat Vigne, Yash Dane, and 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, no. 5 (May 31, 2023): 951–54. http://dx.doi.org/10.22214/ijraset.2023.51635.
Повний текст джерелаArya, A., SPS Mathur, and 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.
Повний текст джерелаZhu, Zhong Rong, Xin Zhe Li, and Zheng Song Wu. "Analysis on Optimization of Dividing Construction Bid-Section Based on Safety Risks." Advanced Materials Research 912-914 (April 2014): 1571–75. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1571.
Повний текст джерелаXinyi, Yang, Chen Han, Chen Liu, Xie Ying, and Chen Bing. "Research on Intelligent Verification Technology of Bid Evaluation Results." BCP Business & Management 45 (April 27, 2023): 402–6. http://dx.doi.org/10.54691/bcpbm.v45i.4961.
Повний текст джерелаNuara, Alessandro, Francesco Trovò, Nicola Gatti, and Marcello Restelli. "Online joint bid/daily budget optimization of Internet advertising campaigns." Artificial Intelligence 305 (April 2022): 103663. http://dx.doi.org/10.1016/j.artint.2022.103663.
Повний текст джерелаYan, Fang, Yanfang Ma, Manjing Xu, and 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.
Повний текст джерелаIslam, Md Mainul, and Sherif Mohamed. "Bid-Winning Potential Optimization for Concession Schemes with Imprecise Investment Parameters." Journal of Construction Engineering and Management 135, no. 8 (August 2009): 690–700. http://dx.doi.org/10.1061/(asce)co.1943-7862.0000032.
Повний текст джерелаKuyzu, Gültekin, Çağla Gül Akyol, Özlem Ergun, and Martin Savelsbergh. "Bid price optimization for truckload carriers in simultaneous transportation procurement auctions." Transportation Research Part B: Methodological 73 (March 2015): 34–58. http://dx.doi.org/10.1016/j.trb.2014.11.012.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаWang, 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.
Повний текст джерелаIncludes 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.
Повний текст джерелаI 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.
Повний текст джерелаBalkan, 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.
Повний текст джерелаinflation 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.
Повний текст джерелаTruckload 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.
Повний текст джерелаTaylor, Kendra C. "Sequential Auction Design and Participant Behavior." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7250.
Повний текст джерелаMüller, Sibylle D. "Bio-inspired optimization algorithms for engineering applications /." Zürich, 2002. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14719.
Повний текст джерелаZuniga, Virgilio. "Bio-inspired optimization algorithms for smart antennas." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5766.
Повний текст джерелаКниги з теми "Bid Optimization"
Pardalos, Panos, Mario Pavone, Giovanni Maria Farinella, and 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.
Повний текст джерелаNicosia, Giuseppe, Panos Pardalos, Giovanni Giuffrida, and 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.
Повний текст джерелаPardalos, Panos M., Piero Conca, Giovanni Giuffrida, and 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.
Повний текст джерелаEmrouznejad, 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.
Повний текст джерелаNijdam, Jelle Luutzen. Behaviour and optimization of packed bed regenerators. Eindhoven: Eindhoven University of Technology, 1995.
Знайти повний текст джерелаWang, John. Encyclopedia of business analytics and optimization. Hershey, PA: Business Science Reference, 2014.
Знайти повний текст джерелаZhan, Jianfeng, Rui Han, and 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.
Повний текст джерелаZhan, Jianfeng, Rui Han, and 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.
Повний текст джерелаChoi, Tsan-Ming, Jianjun Gao, James H. Lambert, Chi-Kong Ng, and 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.
Повний текст джерела1946-, Elshishini S. S., ed. Modelling, simulation, and optimization of industrial fixed bed catalytic reactors. Yverdon, Switzerland: Gordon and Breach Science Publishers, 1993.
Знайти повний текст джерелаЧастини книг з теми "Bid Optimization"
Peng, Kun, Colin Boyd, and Ed Dawson. "Optimization of Electronic First-Bid Sealed-Bid Auction Based on Homomorphic Secret Sharing." In Progress in Cryptology – Mycrypt 2005, 84–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554868_7.
Повний текст джерелаAsadpour, Arash, Mohammad Hossein Bateni, Kshipra Bhawalkar, and Vahab Mirrokni. "Concise Bid Optimization Strategies with Multiple Budget Constraints." In Web and Internet Economics, 263–76. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13129-0_21.
Повний текст джерелаRoy, Pritam. "A Memetic Evolutionary Algorithm-Based Optimization for Competitive Bid Data Analysis." In Evolutionary Computing and Mobile Sustainable Networks, 917–25. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5258-8_84.
Повний текст джерелаZhu, Xiaobo, Qian Yu, and Xianjia Wang. "Strategic Learning in the Sealed-Bid Bargaining Mechanism by Particle Swarm Optimization Algorithm." In 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.
Повний текст джерелаDörpinghaus, Jens, Vera Weil, Sebastian Schaaf, and Alexander Apke. "Optimization." In Studies in Big Data, 361–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08411-9_13.
Повний текст джерелаTomlin, W. Craig. "Conclusion: The Big Picture." In UX Optimization, 177–93. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3867-7_10.
Повний текст джерелаFrench, Mark. "Optimization: The Big Idea." In Fundamentals of Optimization, 1–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76192-3_1.
Повний текст джерелаNazareth, John Lawrence. "Optimization: The Big Picture." In An Optimization Primer, 93–98. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4684-9388-7_10.
Повний текст джерелаCastillo, Oscar, and Patricia Melin. "Bio-Inspired Optimization Methods." In 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.
Повний текст джерелаDing, Yongsheng, Lei Chen, and Kuangrong Hao. "Bio-Inspired Optimization Algorithms." In Studies in Systems, Decision and Control, 317–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6689-4_8.
Повний текст джерелаТези доповідей конференцій з теми "Bid Optimization"
Schuyler, John R. "Bid Optimization With Monte Carlo Simulation." In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/130141-ms.
Повний текст джерелаEven Dar, Eyal, Vahab S. Mirrokni, S. Muthukrishnan, Yishay Mansour, and Uri Nadav. "Bid optimization for broad match ad auctions." In the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526741.
Повний текст джерелаYu, Linfei, Kun She, and Changyuan Yu. "A Primal Dual Approach for Dynamic Bid Optimization." In 2010 IEEE 16th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2010. http://dx.doi.org/10.1109/icpads.2010.75.
Повний текст джерелаKong, Deguang, Konstantin Shmakov, and Jian Yang. "An Inflection Point Approach for Advertising Bid Optimization." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3186944.
Повний текст джерелаYang, Xun, Yasong Li, Hao Wang, Di Wu, Qing Tan, Jian Xu, and Kun Gai. "Bid Optimization by Multivariable Control in Display Advertising." In 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.
Повний текст джерелаBalakrishnan, Raju, and Rushi P. Bhatt. "Real-time bid optimization for group-buying ads." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398502.
Повний текст джерелаBorgs, Christian, Jennifer Chayes, Nicole Immorlica, Kamal Jain, Omid Etesami, and Mohammad Mahdian. "Dynamics of bid optimization in online advertisement auctions." In the 16th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1242572.1242644.
Повний текст джерелаFan, Rui, and Erick Delage. "Risk-Aware Bid Optimization for Online Display Advertisement." In 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.
Повний текст джерелаWenjun Chen and Shuang Yang. "Optimization model of bid evaluation on ELECTRE-III method." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5689992.
Повний текст джерелаKarlsson, Niklas, and Qian Sang. "Adaptive Bid Shading Optimization of First-Price Ad Inventory." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9482665.
Повний текст джерелаЗвіти організацій з теми "Bid Optimization"
Schmidt, C. A., M. J. Brower, J. J. Coogan, and R. A. Tennant. Optimization of a packed bed reactor for liquid waste treatment. Office of Scientific and Technical Information (OSTI), November 1993. http://dx.doi.org/10.2172/10193814.
Повний текст джерелаGarcia, Alfredo. Bio-Inspired Schemes for Global Optimization and Online Distributed Search. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada567710.
Повний текст джерелаSmith, J. C. Enhanced Cutting Plane Techniques for Bi-Level Optimization Algorithms. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada481838.
Повний текст джерелаHoussainy, Sammy, Khanh Nguyen Cu, and Ramin Faramarzi. Final Optimization Report: Empowering Energy Efficiency in Existing Big-Box Retail/Grocery Stores. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1665839.
Повний текст джерелаKnotek-Smith, Heather, and Catherine Thomas. Microbial dynamics of a fluidized bed bioreactor treating perchlorate in groundwater. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45403.
Повний текст джерелаMartin, A. Laser Powder Bed Fusion Additive Manufacturing In-Process Monitoring and Optimization Using Thermionic Emission Detection. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1647152.
Повний текст джерелаGabelmann, Jeffrey, and Eduardo Gildin. A Machine Learning-Based Geothermal Drilling Optimization System Using EM Short-Hop Bit Dynamics Measurements. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1842454.
Повний текст джерелаLiu, Xiaoyue, Yirong Zhou, Ran Wei, Aaron Golub, and 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.
Повний текст джерелаKing, Wayne. Process Control for Defect Mitigation in Laser Powder Bed Fusion Additive Manufacturing. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, May 2023. http://dx.doi.org/10.4271/epr2023011.
Повний текст джерелаCorum, Zachary, Ethan Cheng, Stanford Gibson, and 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.), April 2022. http://dx.doi.org/10.21079/11681/43887.
Повний текст джерела