Special Issue "Advanced Methodologies for Lean and Green Production"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 1466

Special Issue Editor

Mechanical Engineering Department, The University of West Attica, Egaleo, Attica, Greece
Interests: product development; product design and development; design engineering; mechanical processes; creativity and innovation; sustainability; optimization; production; production engineering; operations management
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Special Issue Information

Dear Colleagues,

Lean-and-green production principles and their deployment tactics constitute a vital part of modern improvement initiatives in several areas of manufacturing. For this Special Issue of Applied Sciences, entitled ‘Advanced Methodologies for Lean and Green Production’, we solicit articles that focus on novel methods that can assist us toward attaining greener operations and leaner processes.

Therefore, the scope of this Special Issue is broad enough to include new improvement methodologies that provide decision-making support to engineers/professionals who seek to design and develop leaner and greener processes. Novel mathematical techniques or computational models that provide faster, robust, and convenient predictions to complex production problems are desirable as long as they are demonstrated on real data-centric case studies. The empirical modeling effort should implement modern statistical, evolutionary/quantum computing, and artificial intelligence tools so as to promote rapid and practical results in reducing wastes, curtailing lead times, and lowering costs. Contributions on state-of-the-art lean-and-green re-engineering approaches are also welcome. However, they should be accompanied by a detailed case study that places emphasis on the critical innovation opportunities and their respective improvement accomplishments. Technological advances that involve modern operations in the Industry 4.0 era consolidate new knowledge and, therefore, are particularly relevant to this Special Issue.

Dr. George J. Besseris
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. Applied Sciences 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 2300 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.


  • lean manufacturing
  • green production
  • lean optimization techniques
  • green improvement methods
  • novel green estimators
  • novel lean estimators
  • data-driven lean engineering studies
  • concurrent lean and green statistical engineering
  • data-driven life-cycle improvement
  • green process improvement
  • lean performance optimization
  • lean-and-green improvement in additive manufacturing
  • lean in big data
  • greener processes in industry 4.0

Published Papers (1 paper)

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Lean Screening for Greener Energy Consumption in Retrofitting a Residential Apartment Unit
Appl. Sci. 2022, 12(13), 6631; https://doi.org/10.3390/app12136631 - 30 Jun 2022
Cited by 4 | Viewed by 1082
Buildings consume a large portion of the global primary energy. They are also key contributors to CO2 emissions. Greener residential buildings are part of the ‘Renovation Wave’ in the European Green Deal. The purpose of this study was to explore the usefulness [...] Read more.
Buildings consume a large portion of the global primary energy. They are also key contributors to CO2 emissions. Greener residential buildings are part of the ‘Renovation Wave’ in the European Green Deal. The purpose of this study was to explore the usefulness of energy consumption screening as a part of seeking retrofitting opportunities in the older residential building stock. The objective was to manage the screening of the electromechanical energy systems for an existing apartment unit. The parametrization was drawn upon inspection items in a comprehensive electronic checklist—part of an official software—in order to incur the energy certification status of a residential building. The extensive empirical parametrization intends to discover retrofitting options while offering a glimpse of the influence of the intervention costs on the final screening outcome. A supersaturated trial planner was implemented to drastically reduce the time and volume of the experiments. Matrix data analysis chart-based sectioning and general linear model regression seamlessly integrate into a simple lean-and-agile solver engine that coordinates the polyfactorial profiling of the joint multiple characteristics. The showcased study employed a 14-run 24-factor supersaturated scheme to organize the data collection of the performance of the energy consumption along with the intervention costs. It was found that the effects that influence the energy consumption may be slightly differentiated if intervention costs are also simultaneously considered. The four strong factors that influenced the energy consumption were the automation type for hot water, the types of heating and cooling systems, and the power of the cooling systems. An energy certification category rating of ‘B’ was achieved; thus, the original status (‘C’) was upgraded. The renovation profiling practically reduced the energy consumption by 47%. The concurrent screening of energy consumption and intervention costs detected five influential effects—the automation type for water heating, the automation control category, the heating systems type, the location of the heating system distribution network, and the efficiency of the water heating distribution network. The overall approach was shown to be simpler and even more accurate than other potentially competitive methods. The originality of this work lies in its rareness, worldwide criticality, and impact since it directly deals with the energy modernization of older residential units while promoting greener energy performance. Full article
(This article belongs to the Special Issue Advanced Methodologies for Lean and Green Production)
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