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Relevant bibliographies by topics / Bid Optimization

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Academic literature on the topic 'Bid Optimization'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Bid Optimization"

1

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.

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Milano, 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.

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Tandale, 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.

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Abstract: This analysis and optimization study aims to improve the tedious tendering process in the construction industry. The study identifies the major challenges faced by stakeholders in the tendering process, including high administrative costs, lack of transparency, lengthy bidding periods, and inconsistencies in bid evaluation criteria. Various optimization techniques are proposed, including the use of electronic bidding platforms, standardization of bid evaluation criteria, and the implementation of a two-stage bidding process. The study concludes that implementing these optimization techniques can significantly improve the efficiency and effectiveness of the tendering process, resulting in lower costs, faster bidding periods, and increased transparency.

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Arya, 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.

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As a major Green House Gases (GHG) producer, CO2 in particular, the electricity industry’s emissions have turned in to a matter of immense concern in many countries, especially in India. India’s economy and fast economic development has attracts the attention of the world. Emission trading schemes (ETS) and renewable energy support schemes (RESS) are implemented by the various developed countries to alleviate the affect of GHG emissions. In this paper, an optimization based market simulation approach is proposed with the consideration of emission trading schemes and renewable support schemes. To simulate the bidding strategy and for profit maximization, a particle swarm optimization (PSO) algorithm is used. As above problem is a multi-objective optimization problem, Where, in the first level each Genco submit the bid to the independent system operator and in the next level a optimization method is used for the determination of optimal bidding with the implementation of emission trading schemes and renewable support schemes. It is assumed that each generator should submit bid as a price taker’s in sealed auction based on pay-as-bid market clearing price mechanism. The practicability of proposed optimization method is checked by an IEEE-30 bus test system consists of six suppliers.

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Zhu, 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.

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The geological disaster prevention project is to bring the unstable and unsafe disaster bodies under control, so construction safety risk prevention of this kind of projects is of great importance. Project bid-section division is closely related to the degree of project dispersion, the frequency of monitoring visits and the project duration, and it will also have influence on project construction safety risk. According to the characteristics of geological disaster prevention projects, the paper makes a quantitative analysis of the factors that influence the construction safety. The proposed bid-section division programs are calculated and analyzed by using fuzzy entropy weight model of group decision-making, so as to determine the optimal bid-section division program based on safety risk prevention.

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Xinyi, 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.

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Bidding is an important procurement method. By giving full play to the advantages of bidding enterprises and carrying out open and fair competition, it has realized the effective optimization of the market economic environment and significantly improved the benefits of engineering projects. The bidding of power engineering project is to realize the fair and transparent competition among bidding units. In order to improve the standardization and fairness of bid evaluation results and solve the final control problem of power bidding, it is particularly important to do a good job in the intelligent verification of bid evaluation results. This paper mainly studies the intelligent verification technology of bidding and bid evaluation results. By studying the bidding and bid evaluation process and standards, and using information intelligent technology, this project summarizes some standard laws and models for verifying the bid evaluation results, and develops the intelligent verification tool of bid evaluation results.

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Nuara, 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.

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Yan, 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.

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This paper presents mixed integer programming for a transportation service procurement bid construction problem from a less than full truckload perspective, in which the bidders (carriers) generate their best bid (package) using a bundled price to maximize their utility and increase the chance of winning the business. The models are developed from both the carriers and shippers perspectives to establish a relationship between the quoted price and the likelihood of winning to assist the carriers in balancing the potential benefits and the possibility of winning the bid. An intelligent algorithm based on Particle Swarm Optimization is then designed to solve the proposed model and hypothetical data sets are used to test the effectiveness and efficiency of the proposed model and algorithm.

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Islam, 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.

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Kuyzu, 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.

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Dissertations / Theses on the topic "Bid Optimization"

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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.

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Wang, 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.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2007.
Includes 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.

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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.

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In digital advertising, major search engines allow advertisers to set bid adjustments on their ad campaigns in order to capture the valuation differences that are a function of query dimensions. In this thesis, a model that uses bid adjustments is developed in order to increase the number of conversions and decrease the cost per conversion. A statistical model is used to select campaigns and dimensions that need bid adjustments along with several techniques to determine their values since they can be between -90% and 900%. In addition, an evaluation procedure is developed that uses campaign historical data in order to evaluate the calculation methods as well as to validate different approaches. We study the problem of interactions between different adjustments and a solution is formulated. Real-time experiments showed that our bid adjustments model improved the performance of online advertising campaigns with statistical significance. It increased the number of conversions by 9%, and decreased the cost per conversion by 10%.
I 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%.

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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.

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Balkan, 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.

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In this study, we address the long-term project pricing problem for a company that operates in the defense industry. The pricing problem is a bid project pricing problem which includes various technical and financial uncertainties, such as estimations of workhour content of the project and exchange &
inflation 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.

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Lyu, Ke. "Studies on Auction Mechanism and Bid Generation in the Procurement of Truckload Transportation Services." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0032.

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Le transport par camions entiers est un mode courant de transport de marchandises, qui représente une part importante de l’industrie de transport, où les expéditeurs achètent des services de transport auprès des transporteurs. L'achat de services de transport est souvent réalisé par des enchères. Par concevoir des mécanismes d'enchères efficaces et des méthodes efficaces pour résoudre les problèmes de génération d'enchères associés, les expéditeurs et les transporteurs peuvent réduire leurs coûts et augmenter leurs bénéfices respectivement. Cette thèse étudie trois problèmes soulevés dans l'achat de services de transport par camions entiers réalisé par une enchère combinatoire. Premièrement, deux mécanismes d'enchères combinatoires à deux phases sont conçus avec des paquets supplémentaires de demandes offerts à l’enchère générés respectivement par le commissaire-priseur et les transporteurs dans la deuxième phase. Deuxièmement, un algorithme de génération de colonnes est proposé pour résoudre le problème de génération d'enchères apparu dans l'enchère combinatoire. Enfin, le problème de génération d’enchères est étendu à un problème qui tient compte à la fois plusieurs périodes et l'incertitude dans l'achat de services de transport par camions entières. Ce problème d'optimisation stochastique est formulé par l’optimisation de scénario et l’équivalence déterministe. Pour résoudre ce modèle, une approche de décomposition de Benders est proposée
Truckload 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

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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.

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Many people still confused and misunderstand the differences between auction types: In fact, we have only two major types of auctions which are the forward auction and Reverse auction[22]. In a forward auction a single seller offers an item for sale with many competitive buyers driving the price upward: In a Reverse Auction, a single buyer wants to purchase a service or an item from many sellers, they drive the price downward: There are many differences between these type of auction: Including the progress of the auctions; winner selection criterion and other factors: The Reverse Auction nowadays is one of the most preferred types of online auctions: It gains popularity rapidly because of representing the buyers' side and helps him to drive prices down in contrary with the forward auction or traditional auction. The aim of this study is to identify the most common types of the Reverse auctions and compare them to one another to determine when should be used by a buyer and propose the most efficient implementation model for some types: The results of this study are: achieve a written report and a small demonstrator model on how to implement English Auction and Second-Sealed bid Auction.

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Taylor, Kendra C. "Sequential Auction Design and Participant Behavior." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7250.

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This thesis studies the impact of sequential auction design on participant behavior from both a theoretical and an empirical viewpoint. In the first of the two analyses, three sequential auction designs are characterized and compared based on expected profitability to the participants. The optimal bid strategy is derived as well. One of the designs, the alternating design, is a new auction design and is a blend of the other two. It assumes that the ability to bid in or initiate an auction is given to each side of the market in an alternating fashion to simulate seasonal markets. The conditions for an equilibrium auction design are derived and characteristics of the equilibrium are outlined. The primary result is that the alternating auction is a viable compromise auction design when buyers and suppliers disagree on whether to hold a sequence of forward or reverse auctions. We also found the value of information on future private value for a strategic supplier in a two-period case of the alternating and reverse auction designs. The empirical work studies the cause of low aggregation of timber supply in reverse auctions of an online timber exchange. Unlike previous research results regarding timber auctions, which focus on offline public auctions held by the U.S. Forest Service, we study online private auctions between logging companies and mills. A limited survey of the online auction data revealed that the auctions were successful less than 50% of the time. Regression analysis is used to determine which internal and external factors to the auction affect the aggregation of timber in an effort to determine the reason that so few auctions succeeded. The analysis revealed that the number of bidders, the description of the good and the volume demanded had a significant influence on the amount of timber supplied through the online auction exchange. A plausible explanation for the low aggregation is that the exchange was better suited to check the availability for custom cuts of timber and to transact standard timber.

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Müller, Sibylle D. "Bio-inspired optimization algorithms for engineering applications /." Zürich, 2002. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14719.

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Zuniga, Virgilio. "Bio-inspired optimization algorithms for smart antennas." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5766.

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This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.

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Books on the topic "Bid Optimization"

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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.

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Nicosia, 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.

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Pardalos, 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.

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Emrouznejad, 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.

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Nijdam, Jelle Luutzen. Behaviour and optimization of packed bed regenerators. Eindhoven: Eindhoven University of Technology, 1995.

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Wang, John. Encyclopedia of business analytics and optimization. Hershey, PA: Business Science Reference, 2014.

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Zhan, 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.

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Zhan, 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.

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Choi, 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.

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1946-, Elshishini S. S., ed. Modelling, simulation, and optimization of industrial fixed bed catalytic reactors. Yverdon, Switzerland: Gordon and Breach Science Publishers, 1993.

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Book chapters on the topic "Bid Optimization"

1

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.

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Asadpour, 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.

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Roy, 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.

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Zhu, 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.

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Dörpinghaus, 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.

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Tomlin, 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.

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French, 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.

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Nazareth, 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.

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Castillo, 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.

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Ding, 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.

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Conference papers on the topic "Bid Optimization"

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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.

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Even 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.

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Yu, 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.

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Kong, 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.

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Yang, 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.

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Balakrishnan, 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.

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Borgs, 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.

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Fan, 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.

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Wenjun 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.

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Karlsson, 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.

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Reports on the topic "Bid Optimization"

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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.

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Garcia, 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.

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Smith, 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.

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Houssainy, 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.

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Knotek-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.

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Optimization of operation and performance of the groundwater treatment system regarding perchlorate removal at Longhorn Army Ammunition Plant (LHAAP) is dependent on specific conditions within the reactor and the larger groundwater treatment process. This study evaluated the microbial community compositions within the plant during periods of adequate perchlorate degradation, sub-adequate perchlorate degradation, and non-operating conditions. Factors affecting the performance of the LHAAP ground water treatment system (GWTS) perchlorate de-grading fluidized bed reactor (FBR) are identified and discussed. Isolation of the FBR from naturally occurring microbial populations in the groundwater was the most significant factor reducing system effectiveness. The microbial population within the FBR is highly susceptible to system upsets, which leads to declining diversity within the reactor. As designed, the system operates for extended periods without the desired perchlorate removal without intervention such as a seed inoculant. A range of modifications and the operation of the system are identified to increase the effectiveness of perchlorate removal at LHAAP.

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Martin, 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.

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Gabelmann, 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.

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Liu, 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.

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King, 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.

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<div class="section abstract"><div class="htmlview paragraph">Success in metal additive manufacturing (AM) relies on the optimization of a large set of process parameters to achieve materials whose properties and performance meet design and safety requirements. Despite continuous improvements in the process over the years, the quality of AM parts remains a major concern for manufacturers. Today, researchers are starting to move from discrete geometry-dependent build parameters to continuously variable or dynamically changing parameters that are geometry- and scan-path aware. This approach has become known as “feedforward control.”</div><div class="htmlview paragraph"><b>Process Control for Defect Mitigation in Laser Powder Bed Fusion Additive Manufacturing</b> discusses the origins of feedforward control, its early implementations in AM, the current state of the art, and a path forward to its broader adoption.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

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Corum, 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.

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The US Army Corps of Engineers, Seattle District, nourishes gravel downstream of Howard Hanson Dam (HHD) on the Green River in Washington State. The study team developed numerical models to support the ongoing salmonid habitat improvement mission downstream of HHD. Recent advancements in computing and numerical modeling software make long-term simulations in steep, gravel, cobble, and boulder river environments cost effective. The team calibrated mobile-bed, sediment-transport models for the pre-dam and post-dam periods. The modeling explored geomorphic responses to flow and sediment regime changes associated with HHD construction and operation. The team found that pre-dam conditions were significantly more dynamic than post-dam conditions and may have had lower spawning habitat quality in the project vicinity. The team applied the Bank Stability and Toe Erosion Model to the site and then calibrated to the post-dam gravel augmentation period. The team implemented a new hiding routine in HEC-RAS that improved the simulated grain size trends but underestimated coarse sediment transport. Models without the hiding function overestimated grain size but matched bed elevations and mass flux very well. Decade-long simulations of four future gravel nourishment conditions showed continued sediment storage in the reach. The storage rate was sensitive to nourishment mass and grain size.

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