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Digital interoperability in logistics and supply chain management: state-of-the-art and research avenues towards Physical Internet
قابلیت همکاری دیجیتال در تدارکات و مدیریت زنجیره تأمین: پیشرفته ترین و روشهای تحقیقاتی به سمت اینترنت فیزیکی-2021 Interoperability is playing an increasing role for today’s logistics and supply chain management (LSCM)
because of the trends of cooperation or coopetition. Especially, digital interoperability concerning data
or information exchange becomes a key enabler for the next evolutions that will massively rely upon
digitalization, artificial intelligence, and autonomous systems. The notion of Physical Internet (PI) is one
such evolution, an innovative worldwide logistic paradigm aimed at interconnecting and coordinating
logistics networks for efficiency and sustainability. This paper investigates how digital interoperability
can help interconnect logistics and supply networks as well as the operational solutions for sustainable
development, and examines the new challenges and research opportunities for digital interoperability
under the PI paradigm. To this end, we study the most relevant technologies for digital interoperability in LSCM, via a bibliometric analysis based on 208 papers published during 2010−2020. The results
reveal that the present state-of-the-art solutions of digital interoperability are not fully aligned with PI
requirements and show new challenges, research gaps and opportunities that need further discussion.
Accordingly, several research avenues are suggested to advance research and applications in this area,
and to achieve interconnection in logistics and supply networks for sustainability. Keywords: Interoperability | Interconnection | Physical internet | Digitalization | Logistics | Supply Chain management | Bibliometric review | State-of-the-art | Research avenues |
مقاله انگلیسی |
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Machine-learning assisted coarse-grained model for epoxies over wide ranges of temperatures and cross-linking degrees
یادگیری ماشین به کمک مدل دانه درشت برای epoxies در طیف گسترده ای از درجه حرارت و درجه اتصال متقابل-2019 We present a practical computational framework for the coarse-graining of cross-linked epoxies by developing a machine-learning technique, which integrates molecular dynamics simulations with artificial neural network (ANN) assisted particle swarm optimization (PSO) algorithm. Key features of the framework include two as- pects: (1) determining the bonded interactions via the iterative Boltzmann inversion method to emulate the local structures of the epoxies and, (2) optimizing the nonbonded interaction potentials through the machine- learning approach to reproduce the mechanical properties. Such machine-learning based technique is computa- tionally efficient in searching for the optimal solution of nonbonded potential parameters and enables the CG model to become transferable within a wide range of cross-linking degrees. This is mainly attributed to the fact that ANN can give good predictions based on training database obtained from CG simulations and thus greatly accelerates the PSO algorithm in achieving the optimal solution. On the basis of the DOC-transferable CG model, the cohesive interaction strength is phenomenologically adjusted to preserve the temperature-dependent prop- erties. The CG model allows the mechanical properties of cross-linked epoxies to be predicted with reasonable accuracy over wide ranges of cross-linking degrees and temperature. The proposed framework will become highly beneficial to the design of high performance epoxy-matrix nanocomposites. Keywords: Machine-learning approach | Cross-linked epoxy | Coarse-grained model | Molecular dynamics |
مقاله انگلیسی |