Dynamic Pricing for Perishable Products Using Reinforcement Learning PDF Download
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Author: Rupal Rana
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Rupal Rana
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Bernabé Dorronsoro
Publisher: Springer Nature
ISBN: 3030856720
Category : Computers
Languages : en
Pages : 377
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Book Description
This volume constitutes the refereed proceedings of the 4th International Conference on Optimization and Learning, OLA 2021, held in Catania, Italy, in June 2021. Due to the COVID-19 pandemic the conference was held online. The 27 full papers were carefully reviewed and selected from 62 submissions. The papers presented in the volume are organized in topical sections on synergies between optimization and learning; learning for optimization; machine learning and deep learning; transportation and logistics; optimization; applications of learning and optimization methods.
Author: Michael Schwind
Publisher: Springer Science & Business Media
ISBN: 3540680039
Category : Mathematics
Languages : en
Pages : 305
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Book Description
This book develops allocation mechanisms that aim to ensure an efficient resource allocation in modern IT-services. Recent methods of artificial intelligence, such as neural networks and reinforcement learning, and nature-oriented optimization methods, such as genetic algorithms and simulated annealing, are advanced and applied to allocation processes in distributed IT-infrastructures, or grid systems.
Author: Deepak Gupta
Publisher: Springer Nature
ISBN: 9811662851
Category : Technology & Engineering
Languages : en
Pages : 850
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Book Description
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2021), held at Jan Wyzykowski University, Poland, during June 2021. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students.
Author: Kohei Arai
Publisher: Springer
ISBN: 3030010546
Category : Technology & Engineering
Languages : en
Pages : 1441
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Book Description
Gathering the Proceedings of the 2018 Intelligent Systems Conference (IntelliSys 2018), this book offers a remarkable collection of chapters covering a wide range of topics in intelligent systems and computing, and their real-world applications. The Conference attracted a total of 568 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer review process, after which 194 (including 13 poster papers) were selected to be included in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle many problems more effectively. This branching out of computational intelligence in several directions, and the use of intelligent systems in everyday applications, have created the need for such an international conference, which serves as a venue for reporting on cutting-edge innovations and developments. This book collects both theory and application-based chapters on all aspects of artificial intelligence, from classical to intelligent scope. Readers are sure to find the book both interesting and valuable, as it presents state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision of future research directions.
Author: Marcin Hernes
Publisher: Springer Nature
ISBN: 3030631192
Category : Computers
Languages : en
Pages : 829
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Book Description
This book constitutes refereed proceedings of the 12th International Conference on International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November – December 2020. Due to the the COVID-19 pandemic the conference was held online. The 68 papers were thoroughly reviewed and selected from 314 submissions. The papers are organized according to the following topical sections: data mining and machine learning; deep learning and applications for industry 4.0; recommender systems; computer vision techniques; decision support and control systems; intelligent management information systems; innovations in intelligent systems; intelligent modeling and simulation approaches for games and real world systems; experience enhanced intelligence to IoT; data driven IoT for smart society; applications of collective intelligence; natural language processing; low resource languages processing; computational collective intelligence and natural language processing.
Author: 蔡文真
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Martin Kunc
Publisher: Springer
ISBN: 1137535512
Category : Business & Economics
Languages : en
Pages : 412
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Book Description
Behavioral research is making a significant impact on many academic disciplines. Its status as the source of some of the most profound research in the social sciences is unparalleled. Therefore, it is not surprising that interest in Behavior and Operational Research (OR) is burgeoning, even though understanding the relationship between knowledge, behavior and action has been an academic preoccupation in OR since the beginning of the discipline. This book introduces the idea of Behavioral OR, where the theoretical and empirical developments in the behavioral field are making an impression on OR academics and practitioners alike. The book provides a much needed overview that connects together theory, methodology and practice and offers the “state of the art” on Behavioral Operational Research theory and practice. The book not only includes chapters by leading academics, but also includes rich and insightful real-life case studies by practitioners.
Author: Goh Chun Sheng
Publisher: ISEAS-Yusof Ishak Institute
ISBN: 9815011650
Category : Social Science
Languages : en
Pages : 380
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Book Description
“There is an energizing boldness in this synthesis: the right big-picture questions aligning all the way down to the right complexities on the ground, and across the diverse territories that comprise contemporary Borneo. A manifesto for the kinds of cross-sectoral and applied research that can make the difference to the future of Borneo.” Cynthia Ong, Chief Executive Facilitator, Forever Sabah “A surgical and timely compendium on the transformation of Borneo’s forests and land use with clear regional implications. If you care about the future of conservation in this part of the world, you will find all the key ingredients here for its salvation.” Gopalasamy Reuben Clements, Professor at Sunway University, Co-founder of Nature-Based Solutions “A perspective about balancing the future amidst the need for economic and social development while providing a better and more sustainable Borneo. It is something that you will need to help drive home change and make a sustainable impact for people and planet without compromising profit.” Timothy Ong, Head of Circular Bio-economy Unit, Malaysian Investment Development Authority (MIDA)
Author: Gah-Yi Ban
Publisher:
ISBN:
Category :
Languages : en
Pages : 53
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Book Description
We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers' characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand, but learns this through sales observations over a selling horizon of T periods. We prove that the seller's expected regret, i.e., the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s √T under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order s √Tlog T. We extend this policy to a more realistic setting where the seller does not know the true demand predictors, and show that this policy has an expected regret of order s √T(log d+logT), which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then- optimize policies. Furthermore, our policy improves upon the loan company's historical pricing decisions by 47% in expected revenue over a six-month period.
