Literature DB >> 32638315

Optimization of hyperconnected mobile modular production toward environmental and economic sustainability.

Charifa Fergani1, Adiba El Bouzekri El Idrissi2, Suzanne Marcotte3, Abdelowahed Hajjaji2.   

Abstract

Logistics sustainability is increasingly becoming a central focus of businesses, when most societies are aware of the influence of industry on both the environment and human health. To address the drawbacks of the way logistics systems have been designed, a new logistics system called Physical Internet has been proposed. This system relies on the creation of hyperconnected logistics systems. It aims to improve in an order of magnitude the way physical objects are transported, handled, stored, supplied, realized, and used to be more sustainable and efficient economically, environmentally, and socially. This paper focuses on the product realization using a hyperconnected mobile production mode in the context of Physical Internet, an open global logistics system. It addresses its dynamic deployment of production modules and resource allocation and sharing. It then proposes a make-to-order bi-objective optimization model which minimizes costs and greenhouse gases (GHGs) related to the product realization of a manufacturer to serve its customers given the availability of the open fabs. Experimental results are presented to identify the computational performance of the established model, as well as the economic and environmental benefits of using the facilities enabled by the PI. Finally, it concludes and provides directions for future research.

Entities:  

Keywords:  Dynamic deployment of production modules; Environmental sustainability; Hyperconnected mobile modular production; Logistics drawbacks; Physical Internet

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Year:  2020        PMID: 32638315     DOI: 10.1007/s11356-020-09966-9

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  Intelligent monitoring method of tridimensional storage system based on deep learning.

Authors:  Mingzhou Liu; Xin Xu; Xiaoqiao Wang; Qiannan Jiang; Conghu Liu
Journal:  Environ Sci Pollut Res Int       Date:  2022-05-19       Impact factor: 5.190

  1 in total

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