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Process Innovation, Logistics Optimization and Sustainable Manufacturing

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 4666

Special Issue Editors


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Guest Editor
Department of Healthcare Industry Technology Development and Management, National Chin-Yi University of Technology, Taichung, Taiwan
Interests: statistical methods and applications; quality engineering and management; production and operation research
Special Issues, Collections and Topics in MDPI journals
Department of Industrial Education and Technology, National Changhua University of Education, Changhua, Taiwan
Interests: creativity and invention; case study; artificial intelligence; TRIZ and quality engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The industrial world is witnessing significant transformations driven by rapid technological advancements, sustainability imperatives, and collaborative innovation between academia and industry. This Special Issue delves into the interconnected fields of process innovation, logistics optimization, and sustainable manufacturing, highlighting their critical roles in shaping future industrial strategies.

Process innovation is key to improving efficiency and creating value, particularly through the integration of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). These technologies allow industries to automate and streamline production workflows, enhancing flexibility and waste reduction. Additionally, process innovation in design and education offers opportunities for academic and industrial collaboration. Submissions focusing on the role of academia–industry partnerships in developing innovative process designs, as well as new educational frameworks that train the next generation of engineers and designers, are particularly welcome.

Logistics optimization has become even more essential in today’s complex global supply chains, influenced by the rise of e-commerce and international trade. Leveraging data analytics, real-time monitoring, and AI-driven logistics, companies can create more responsive, efficient, and sustainable supply chains. This issue seeks research on how educational institutions can play a pivotal role in designing new logistics systems, fostering collaborations that enable the development of cutting-edge logistics tools, and equipping students with the skills necessary for managing future logistics challenges.

Sustainable manufacturing is at the forefront of the global push toward environmental responsibility. Companies are embracing renewable energy, circular economy principles, and resource efficiency to reduce their environmental footprints. Papers that highlight innovative solutions in green design, sustainable materials, and manufacturing education programs that promote sustainability are strongly encouraged. Special attention is given to industry–education collaborations that foster sustainable practices and innovative designs in manufacturing systems.

This Special Issue invites contributions from a wide range of disciplines, including industrial engineering, logistics, sustainability studies, and educational design. We aim to create a comprehensive platform that fosters academic and industry cooperation, ultimately driving innovation and sustainability in industrial systems.

Prof. Dr. Ching-Hsin Wang
Dr. Wei-Sho Ho
Guest Editors

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

  • process innovation
  • logistics optimization
  • sustainable manufacturing
  • artificial intelligence (AI)
  • Internet of Things (IoT)
  • industry–academia collaboration
  • educational design
  • green manufacturing
  • ESG
  • circular economy
  • big data analytics
  • supply chain management
  • resource efficiency

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Published Papers (1 paper)

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Research

22 pages, 8995 KiB  
Article
Reducing Food Waste in Campus Dining: A Data-Driven Approach to Demand Prediction and Sustainability
by Gul Fatma Turker
Sustainability 2025, 17(2), 379; https://doi.org/10.3390/su17020379 - 7 Jan 2025
Viewed by 4038
Abstract
Tracking density in universities is essential for planning services like food, transportation, and social activities on campus. However, food waste remains a critical challenge in campus dining operations, leading to significant environmental and economic consequences. Addressing this issue is crucial not only for [...] Read more.
Tracking density in universities is essential for planning services like food, transportation, and social activities on campus. However, food waste remains a critical challenge in campus dining operations, leading to significant environmental and economic consequences. Addressing this issue is crucial not only for minimizing environmental impact but also for achieving sustainable operational efficiency. Campus food services significantly influence students’ university choices; thus, forecasting meal consumption and preferences enables effective planning. This study tackles food waste by analyzing daily campus data with machine learning, revealing strategic insights related to food variety and sustainability. The algorithms Linear Regression, Extra Tree Regressor, Lasso, Decision Tree Regressor, XGBoost Regressor, and Gradient Boosting Regressor were used to predict food preferences and daily meal counts. Among these, the Lasso algorithm demonstrated the highest accuracy with an R2 metric value of 0.999, while the XGBRegressor also performed well with an R2 metric value of 0.882. The results underline that factors such as meal variety, counts, revenue, campus mobility, and temperature effectively influence food preferences. By balancing production with demand, this model significantly reduced food waste to 28%. This achievement highlights the potential for machine learning models to enhance sustainable dining services and operational efficiency on university campuses. Full article
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