IJPE 超连接物理互联网的数字孪生与数据驱动优化

Call for Papers

Digital Twin and Data-driven Optimization for Hyperconnected Physical Internet

for the International Journal of Production Economics

On Singles' Day 2019, Alibaba seals $38.4 billion new sales record (TechCrunch 2019). Millions of parcels were handled and delivered within a short time. It is a critical issue to improve by an order of magnitude the economical, environmental and societal efficiency and sustainability of the way physical freight are operated (Qiu, Luo et al. 2015). By analogizing to Internet, Physical Internet (PI) is defined as a hyperconnected global logistics system enabling seamless open asset sharing and flow consolidation through standardized encapsulation, modularization, protocols and interfaces (Mervis 2014; Ballot, Montreuil, and Meller 2014). Several key scientific topics of Physical Internet have already been studied in a growing body of literature (Sternberg and Norrman, 2017; Pan, Ballot, Huang and Montreuil, 2017). Whereas, most of the previous studies are still limited to conceptual model verification. Large-scale industry-wide PI applications and deployments are also very few. It still has many key research questions to discuss, such as what the economic value and feasibility of PI technologies for the large-scale deployment is; and how should we quantitatively evaluate PI platform innovations, with which methods (Joshi and Gupta 2019; Almohri, Chinnam, and Colosimo 2019).

Everything in the future physical world would be replicated in the digital space. Digital Twin (DT) is one of key driving forces for hyperconnected physical internet. DT can project physical assets or processes into the digital world to reflect the whole lifecycle process (e.g., design, production, operation and maintenance) of the corresponding counterpart (Tao and Qi, 2019). For example, based on DT technologies, the large amounts of data that result from online ecommerce and offline fulfilments can be mined, modelled and analysed for data-driven optimization (Ivanov, Dolgui, Das, and Sokolov 2019). We also consider DT technology as a combination of Cyber Physical System, Industry 4.0, the Internet of Things, Big Data analytics, Artificial Intelligence, Advanced tracking and tracing technologies, Wearables, Additive Manufacturing and etc (Kong, Luo, Huang and Yang, 2018).

The successful deployment of hyperconnected physical internet would rely on integrating the objectives of both technology and management. But much of the DT technology and data-driven literature has been largely disjointed without much emphasis on novel scientific contribution. Interdisciplinary researches are also needed to build up new theories that examines the interplay between digital twin and data driven. This special issue addresses this void by specifically encouraging research that provides insight into digital twin and data-driven interface for significant theoretical breakthroughs. The SI accepts scientific contribution based on data-driven methods and rigorous sound theory. We welcome a wide variety of topics spanning multiple industries. We are also looking for papers that will not only address contemporary PI challenges in novel ways, but will serve as exemplars for conducting research in future.

Potential topics include, but are not limited to:

•Digital supply chain twins and Physical Internet

•Economic valuation methods of PI/DT platform innovations

•DT/PI-enabled smart manufacturing

•DT/PI-enabled sustainable supply chain

•DT/PI-enabled hyperconnected and urban logistics

•DT/PI-enabled hyperconnected and omnichannel supply chains

•Economic and statistical contributions that are relevant to Physical Internet

•Data-driven optimization approaches to address practical challenges in Physical Internet

•Impact of DT/PI-enabled visibility, traceability and optimization on industrial economics

•Case study for large-scale industry-wide physical internet deployment

•Application of artificial intelligence, blockchain and big data analytics in Physical Internet

Manuscript preparation and submission

Before submission, authors should carefully read over the journal's "Instructions for Authors". The review process will follow the journal's practice. Papers submitted to the Special Issue will be subjected to normal thorough double-blind review process. Prospective authors should clarify on methodology used in the submitted papers, and submit an electronic copy of their complete manuscript via the EES according to the following timetable:

•Close submission of manuscript on 30 November 2020

•Completion of the first-round review on28 February 2021

•Completion of the second-round review on 31 May 2021

For any queries please contact the Guest Editors (alphabetical order)

Eric Ballot (Professor)

MINES ParisTech, PSL Research University, CGS – Centre de Gestion Scientifique, i3 UMR CNRS 9217, Paris, France. E-mail: eric.ballot@mines-paristech.fr

George Q. Huang (Professor, managing GE)

HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong. E-mail: gqhuang@hku.hk

Benoit Montreuil (Professor)

Physical Internet Center, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, USA. E-mail: benoit.montreuil@isye.gatech.edu

Hao Luo (Associate Professor)

Department of Transportation Economics and Logistics Management, College of Economics Shenzhen University, China. E-mail: luohao@szu.edu.cn

Xiang T.R. Kong (Assistant Professor)

Department of Transportation Economics and Logistics Management, College of Economics Shenzhen University, China. E-mail: kongxtr@szu.edu.cn

References

Almohri, H., Chinnam, R. B., & Colosimo, M. (2019). Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships. International Journal of Production Economics, 213, 69-80.

Ballot, E., B. Montreuil, and R. Meller. (2014). The Physical Internet: The Network of Logistics Networks. Paris: La documentation Française.

Ivanov, D., Dolgui, A., Das, A., & Sokolov, B. (2019). Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility. In Handbook of Ripple Effects in the Supply Chain, Springer, Cham, 309-332.

Joshi, A. D., & Gupta, S. M. (2019). Evaluation of design alternatives of End-Of-Life products using internet of things. International Journal of Production Economics, 208, 281-293.

Kong, X. T., Luo, H., Huang, G. Q., and Yang, X. (2018). Industrial wearable system: the human-centric empowering technology in Industry 4.0. Journal of Intelligent Manufacturing, 1-17.

Mervis, J. (2014). The Information Highway Gets Physical. Science, 344:1104-1107.

Pan, S., Ballot, E., Huang, G. Q., & Montreuil, B. (2017). Physical Internet and interconnected logistics services: research and applications. International Journal of Production Research, 55(9), 2603-2609.

Qiu, X., Luo, H., Xu, G., Zhong, R., & Huang, G. Q. (2015). Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP). International Journal of Production Economics, 159, 4-15.

Sternberg, H., & Norrman, A. (2017). The Physical Internet–review, analysis and future research agenda. International Journal of Physical Distribution & Logistics Management, 47(8), 736-762.

TechCrunch. (2019). Alibaba’s Singles’ Day sales top $38 billion, https://techcrunch.com/2019/11/11/alibaba-singles-day-record/

Tao F. and Qi Q.L. (2019). Make More Digital Twins. Nature, 573, 490-491.

征集论文

超连接物理互联网的数字孪生与数据驱动优化

国际生产经济学杂志

2019年光棍节，阿里巴巴创造了384亿美元的新销售纪录（TechCrunch 2019）。数百万件包裹在短时间内被处理和交付。如何一个数量级的经济、环境和社会效率以及实体运输运作方式的可持续性是一个关键问题（Qiu，Luo等 2015）。通过类比互联网，物理互联网（PI）被定义为一个超连接的全球物流系统，通过标准化封装、模块化、协议和接口实现无缝的开放式资产共享和流程整合（Mervis 2014；Ballot、Montreuil和Meller 2014）。已经有越来越多的文献研究了物理互联网的几个关键科学主题（Sternberg和Norrman，2017；Pan、Ballot、Huang和Montreuil，2017）。然而，以往的研究大多局限于概念模型的验证。大规模的行业范围的PI应用和部署也很少。它还有许多关键的研究问题需要讨论，例如PI技术在大规模部署中的经济价值和可行性是什么；我们应该如何定量评估PI平台创新，使用哪些方法（Joshi和Gupta 2019；Almohri、Chinnam和Colonimo 2019）。

未来物理世界中的一切都将在数字空间中复制。数字孪生（DT）是超连接物理互联网的关键驱动力之一。DT可以将物理资产或过程投影到数字世界中，以反映相应对应方的整个生命周期过程（例如，设计、生产、运行和维护）（Tao和Qi，2019）。例如，基于DT技术，在线电子商务和离线交付产生的大量数据可以被挖掘、建模和分析，以实现数据驱动的优化（Ivanov、Dolgui、Das和Sokolov 2019）。我们还将DT技术视为网络物理系统、工业4.0、物联网、大数据分析、人工智能、先进追踪跟踪技术、可穿戴设备、增材制造等的结合（Kong、Luo、Huang和Yang，2018）。

超连接物理互联网的成功部署将依赖于技术和管理目标的结合。但是，很多DT技术和数据驱动的文献在很大程度上是脱节的，没有太多地强调新的科学贡献。跨学科研究也需要建立新的理论来检验数字孪生和数据驱动之间的相互作用。本期特刊通过特别鼓励为重大的理论突破提供对数字孪生和数据驱动接口的洞察的研究来解决这一空白。特刊接受基于数据驱动方法和严格全面理论的科学贡献。我们欢迎涉及多个行业的各种主题。我们也在寻找不仅以新颖的方式解决当代PI挑战，而且将成为未来进行研究的范式的论文。

潜在主题包括但不限于：

•数字供应链孪生和物理互联网

•PI/DT平台创新的经济评估方法

•DT/PI赋能的智能制造

•DT/P赋能的可持续供应链

•DT/PI赋能的超连接和城市物流

•DT/PI赋能的超连接和全渠道供应链

•与物理互联网相关的经济和统计贡献

•数据驱动优化方法，解决物理互联网中的实际挑战

•DT/PI赋能的可视性、可追溯性和优化对工业经济的影响

•全行业大规模物理互联网部署案例研究

•人工智能、区块链和大数据分析在物理互联网中的应用

稿件准备和提交

投稿前，作者应仔细阅读期刊的“作者须知”。评审过程将遵循期刊的惯例。提交给特刊的论文将接受常规的、彻底的双盲审查程序。潜在作者应表明所提交论文中使用的方法，并根据以下时间表通过EES提交完整手稿的电子副本：

•2020年11月30日完成稿件提交

•2021年2月28日完成第一轮审查

•2021年5月31日完成第二轮审查

如有任何疑问，请联系特邀编辑（按字母顺序）