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Sustainable Cyber-Physical Production and Manufacturing Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 2053

Special Issue Editor


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Guest Editor
Department of Economic Sciences, Spiru Haret University, 041916 Bucharest, Romania
Interests: neurobehavioral economics; smart manufacturing; smart cities; big data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is on the growing deployment of cyber-physical production networks throughout Industry-4.0-based manufacturing systems, sustainable smart manufacturing such as cyber-physical production systems, and real-time process monitoring and sustainable product lifecycle management in cyber-physical-system-based manufacturing. The scope of this Special Issue covers topics proving how the manufacturing cyber-physical system possesses progressively automated linkages and networking, leading to big-data-driven innovation in sustainable Industry 4.0; how smart manufacturing makes cyber-physical production networks more well organized and sustainable through the use of artificial intelligence-based decision-making algorithms; how by deploying groundbreaking sensors, input modeling, computing, and big data analytics technologies, cyber-physical production systems can become smart and sustainable; how cyber-physical smart manufacturing systems function in an automated, robust, and flexible manner, thus managing sustainability issues adequately; and how cyber-physical production networks operate automatically and smoothly with artificial-intelligence-based decision-making algorithms and Internet-of-Things-based real-time production logistics. The purpose of this Special Issue is to clarify how automated functions can contribute to increased productivity and decrease in work-in-process throughout deep-learning-assisted smart process planning and cyber-physical-system-based manufacturing by sensor network implementation; how automated production systems and industrial artificial intelligence enable the operations performed by cyber-physical-system-based smart factories through big-data-driven innovation and sustainable industrial value creation; and how industrial big data, cognitive decision-making algorithms, and Internet of Things sensing networks are instrumental in configuring sustainable organizational performance across cyber-physical smart manufacturing systems.

Prof. Dr. George Lăzăroiu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable industrial value creation
  • cyber-physical smart manufacturing system
  • industrial big data
  • deep-learning-assisted smart process planning
  • artificial-intelligence-based decision-making algorithms
  • Internet-of-Things-based real-time production logistics

Published Papers (1 paper)

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Research

15 pages, 662 KiB  
Article
A Multi-Stage Stochastic Programming Model for the Multi-Echelon Multi-Period Reverse Logistics Problem
by Vahid Azizi and Guiping Hu
Sustainability 2021, 13(24), 13596; https://doi.org/10.3390/su132413596 - 9 Dec 2021
Cited by 3 | Viewed by 1551
Abstract
Reverse logistics planning plays a crucial role in supply chain management. Stochasticity in different parameters along with time horizon can be a challenge in solving reverse logistics problems. This paper proposes a multi-stage, multi-period reverse logistics with lot sizing decisions under uncertainties. The [...] Read more.
Reverse logistics planning plays a crucial role in supply chain management. Stochasticity in different parameters along with time horizon can be a challenge in solving reverse logistics problems. This paper proposes a multi-stage, multi-period reverse logistics with lot sizing decisions under uncertainties. The main uncertain factors are return and demand quantities, and return quality. Moment matching method was adopted to generate a discrete set of scenarios to represent the original continuous distribution of stochastic parameters. Fast forward selection algorithm was employed to select the most representative scenarios and facilitate computational tractability. A case study was conducted and optimal solution of the recursive problem obtained by solving extensive form. Sensitivity analysis was implemented on different elements of stochastic solution. Results sow that solution of recursive problem (RP) outperforms the solution obtained from the problem with expected values of uncertain parameters (EEV). Full article
(This article belongs to the Special Issue Sustainable Cyber-Physical Production and Manufacturing Systems)
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