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The impact of supply chain partnership and market driven strategy on consumer behavior in buying Vocaloid Hatsune Miku products

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The impact of supply chain partnership and market driven str...

2018 International Mechanical and Industrial engineering Conference, IMIEC 2018

作者： Damar, Vicio Rizky Hartono, Markus Postgraduate Program in Logistics and Supply Chain Engineering University of Surabaya Surabaya60293 Indonesia

Hatsune Miku is the first virtual singer who is currently achieving success, even up to the world level. Miku has a large number of fansbase, like a famous singer. Its popularity also helps Crypton Future Media, as the company maker of Miku, to collaborate, forming a supply chain partnership with several big companies, to bring Miku into real products, which fans can have. This literature review explores this literature study explores Miku's development ever since it was created, until it became famous, and gave rise to a unique supply chain partnership. Researcher using systematic literature review method, with the aim of understanding how big the impact of supply chain Partnership and Market Driven from the success of virtual singer Hatsune Miku to consumer behavior, both from the general public and fans. The impact of this unique supply chain partnership is to make consumers loyal and feel close. © The Authors, published by EDP Sciences, 2018.

关键词： supply chains

Dynamic pooled capacity deployment for Urban parcel logistics

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arXiv 2020年

作者： Faugère, Louis Klibi, Walid White, Chelsea Montreuil, Benoit Physical Internet Center School of Industrial & Systems Engineering Georgia Institute of Technology AtlantaGA United States KEDGE Business School Bordeaux France Supply Chain & Logistics Institute Georgia Institute of Technology AtlantaGA United States Schneider National Chair in Transportation and Logistics Coca-Cola Material Handling & Distribution Chair

Last-mile logistics is regarded as an essential yet highly expensive component of parcel logistics. In dense urban environments, this is partially caused by inherent inefficiencies due to traffic congestion and the disparity and accessibility of customer locations. In parcel logistics, access hubs are facilities supporting relay-based last-mile activities by offering temporary storage locations enabling the decoupling of last-mile activities from the rest of the urban distribution chain. This paper focuses on a novel tactical problem: the geographically dynamic deployment of pooled relocatable storage capacity modules in an urban parcel network operating under space-time uncertainty. In particular, it proposes a two-stage stochastic optimization model for the access hub dynamic pooled capacity deployment problem with synchronization of underlying operations through travel time estimates, and a solution approach based on a rolling horizon algorithm with lookahead and a benders decomposition able to solve large scale instances of a real-sized megacity. Numerical results, inspired by the case of a large parcel express carrier, are provided to evaluate the computational performance of the proposed approach and suggest up to 28% last-mile cost savings and 26% capacity savings compared to a static capacity deployment strategy. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic models

Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management

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arXiv 2021年

作者： Gammelli, Daniele Wang, Yihua Prak, Dennis Rodrigues, Filipe Minner, Stefan Pereira, Francisco Camara Department of Technology Management and Economics Technical University of Denmark Kgs. Lyngby2800 Denmark Logistics and Supply Chain Management School of Management Technical University of Munich Munich80333 Germany Department of Operations University of Groningen PO Box 800 Groningen9700 AV Netherlands Department Industrial Engineering and Business Information Systems University of Twente PO Box 217 Enschede7500 AE Netherlands Technical University of Munich Garching85748 Germany

Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility. The asymmetric nature of bike demand causes the need for rebalancing bike stations, which is typically done during nighttime. To determine the optimal starting inventory level of a station for a given day, a User Dissatisfaction Function (UDF) models user pickups and returns as non-homogeneous Poisson processes with piece-wise linear rates. In this paper, we devise a deep generative model directly applicable in the UDF by introducing a variational Poisson recurrent neural network model (VP-RNN) to forecast future pickup and return rates. We empirically evaluate our approach against both traditional and learning-based forecasting methods on real trip travel data from the city of New York, USA, and show how our model outperforms benchmarks in terms of system efficiency and demand satisfaction. By explicitly focusing on the combination of decision-making algorithms with learning-based forecasting methods, we highlight a number of shortcomings in literature. Crucially, we show how more accurate predictions do not necessarily translate into better inventory decisions. By providing insights into the interplay between forecasts, model assumptions, and decisions, we point out that forecasts and decision models should be carefully evaluated and harmonized to optimally control shared mobility systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

A Mathematical Model for Internal Task Scheduling in Cross Docking

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A Mathematical Model for Internal Task Scheduling in Cross D...

IEEE International Conference on Industrial engineering and engineering Management

作者： D. Buakum W. Wisittipanich Graduate Program in Industrial Engineering Faculty of Engineering Chiang Mai University Thailand Excellence Center in Logistics and Supply Chain Management Chiang Mai University Thailand

This study proposes a novel problem for solving task scheduling in cross docking. In particular, we formulate a mathematical model for the task scheduling between inbound and outbound dock doors in order to minimize total operational time based on limited resource. The goal is to determine the start time and finish time of each operation on the assigned working team and transfer equipment with respect to maximum resource utilization. The use of mathematical model is paving the way to Artificial Intelligence (AI) for smart decision in cross docking since AI is a key factor in Industries 4.0. Moreover, facility cost is analyzed based on trade-off features between cost and makespan so that a decision can be made based on a decision maker's preference.

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A Physical Emulation Model of Cellular Warehousing for E-commerce logistics

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Procedia CIRP 2019年 83卷 339-344页

作者： Xiang T.R. Kong George Q. Huang Meng L. Du Collaborative Research Centre for Supply Chain Innovation Department of Transportation Economy and Logistics Management College of Economics Shenzhen University Shenzhen China Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong China SF Technology Shenzhen China

With the development of E-commerce logistics industry, higher requirements towards logistics and warehousing emerge, including the shorter delivery time and higher operation accuracy when processing massive SKUs and orders. However, the existing full-automated solutions cannot meet the variety and variation of E-commerce warehousing demands. Inspired by cellular manufacturing, a flexible and reconfigurable warehousing system called cellular warehousing is put forward. Although several articles have illustrated this new concept, few systematic studies on cell formation, scenario design and physical emulation model exist. This study develops this conceptual model with three-fold objectives. First, the cell formation and operation flow of cellular warehousing are deciphered. Second, application scenarios are given including intra-cell and inter-cell operation, with the background of different types of order processing. At last, a physical emulation model is created as test-bed for further qualitative evaluation and insight abstraction.

关键词： E-commerce logistics and Warehousing Cellular Manufacturing Process Design and engineering Physical Emulation Model

Corrigendum to “Manufacturer’s selling mode choice in a platform-oriented dual channel supply chain” [Expert Syst. Appl. 198 (2022) 116842]

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Expert systems with Applications 2022年 207卷

作者： Tong-Yuan Wang Zhen-Song Chen Kannan Govindan Kwai-Sang Chin Institute of Modern Logistics Henan University Kaifeng 475004 China School of Business Henan University Kaifeng 475004 China School of Transportation and Logistics Southwest Jiaotong University Chengdu 611756 China Department of Engineering Management School of Civil Engineering Wuhan University Wuhan 430072 China School of Data Science City University of Hong Kong Hong Kong China Institute of FTZ Supply Chain Shanghai Maritime University Shanghai 201306 China Yonsei Frontier Lab Yonsei University Seoul South Korea Center for Sustainable Supply Chain Engineering Department of Technology and Innovation Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense M Denmark Department of Advanced Design and Systems Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China

Operations & supply chain Management: Principles and Practice

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arXiv 2025年

作者： Petropoulos, Fotios Akkermans, Henk Aksin, O. Zeynep Ali, Imran Babai, Mohamed Zied Barbosa-Povoa, Ana Battaïa, Olga Besiou, Maria Boysen, Nils Brammer, Stephen Brandon-Jones, Alistair Briskorn, Dirk Browning, Tyson R. Buijs, Paul Centobelli, Piera Chiarini, Andrea Cousins, Paul Cudney, Elizabeth A. Davies, Andrew Day, Steven J. de Koster, René Dekker, Rommert Denicol, Juliano Despeisse, Mélanie Disney, Stephen M. Dolgui, Alexandre Duong, Linh El-Qallali, Malek Fahimnia, Behnam Fakhredin, Fatemeh Gershwin, Stanley B. Ghamat, Salar Giannikas, Vaggelis Glock, Christoph H. Godsell, Janet Govindan, Kannan Hannibal, Claire Haug, Anders Hernaus, Tomislav Hsuan, Juliana Ivanov, Dmitry Jahre, Marianne Johansson, Björn Kalidoss, Madan Shankar Kanellopoulos, Argyris Kannan, Devika Karul, Elif Katsikopoulos, Konstantinos V. Kilic-Ararat, Ayse Begüm Kolisch, Rainer Koppenberg, Maximilian Kumar, Maneesh Kuo, Yong-Hong Kusiak, Andrew Lewis, Michael A. Lim, Stanley Frederick W.T. Limère, Veronique Liu, Jiyin Maghazei, Omid Marić, Matija Meissner, Joern Meuwissen, Miranda Micheli, Pietro Nanayakkara, Samudaya Özdemir, Bengü Nur Papadopoulos, Thanos Pavelin, Stephen Perera, Srinath Phillips, Wendy Prak, Dennis Pun, Hubert Qazi, Sharfah Ahmad Ramanathan, Usha Reiner, Gerald Reitsma, Ewout Roehrich, Jens K. Sanders, Nada R. Sarkis, Joseph Schmid, Nico André Schmidt, Christoph G. Schroeder, Andreas Selviaridis, Kostas Seuring, Stefan Shi, Chuan Son, Byung-Gak Spring, Martin Squire, Brian van der Valk, Wendy van Donk, Dirk Pieter van Houtum, Geert-Jan Wilhelm, Miriam Wynstra, Finn Zheng, Ting School of Management University of Bath Bath United Kingdom Department of Information Systems & Operations Management Tilburg School of Economics and Management Tilburg University Netherlands College of Administrative Sciences and Economics Koc University Turkey School of Business and Law Central Queensland University Australia Kedge Business School France Center for Management Studies Instituto Superior Técnico University of Lisbon Portugal Kühne Logistics University Hamburg Germany Friedrich-Schiller-Universität Jena Lehrstuhl für Operations Management Germany Bergische Universität Wuppertal Germany Neeley School of Business Texas Christian University Fort WorthTX United States Department of Operations University of Groningen Netherlands Department of Industrial Engineering University of Naples "Federico II" Italy Department of Management University of Verona Italy Management School University of Liverpool United Kingdom John E. Simon School of Business Maryville University of Saint Louis Missouri United States Science Policy Research Unit University of Sussex Business School Brighton United Kingdom School of Management Zhejiang University China Rotterdam School of Management Erasmus University Netherlands Erasmus School of Economics Erasmus University Netherlands Bartlett School of Sustainable Construction The Bartlett Faculty of the Built Environment University College London United Kingdom Department of Industrial and Material Sciences Chalmers University of Technology Sweden Center for Simulation Analytics and Modelling University of Exeter Business School United Kingdom IMT Atlantique LS2N-CNRS Nantes France College of Business and Law University of the West of England United Kingdom Rabdan Academy United Arab Emirates Institute of Transport and Logistics Studies The University of Sydney Business School Australia Kühne Logistics University Germany Department of Mechanical Engineering Massachusetts Institute of Technology Massachusetts United S

Operations and supply chain Management (OSCM) has continually evolved, incorporating a broad array of strategies, frameworks, and technologies to address complex challenges across industries. This encyclopedic article provides a comprehensive overview of contemporary strategies, tools, methods, principles, and best practices that define the field’s cutting-edge advancements. It also explores the diverse environments where OSCM principles have been effectively implemented. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. © 2025, CC BY.

关键词： supply chain management

A Consensus Privacy Metrics Framework for Synthetic Data

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

Operational Research: Methods and Applications

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arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making