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Sustainability
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Sustainable and Collaborative Smart Manufacturing and Logistics
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Sustainable and Collaborative Smart Manufacturing and Logistics

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 9728

Special Issue Editors

Prof. Dr. Kwangyeol Ryu

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Guest Editor
Department of Industrial Engineering, Pusan National University, Busan 46241, Korea
Interests: smart factory; manufacturing data analytics; fractal manufacturing system (FrMS)
Prof. Dr. Dong-Ho Lee

E-Mail Website
Guest Editor
Department of Industrial Engineering, Hanyang University, Seoul 04763, Korea
Interests: design and operation of production/service systems; supply chain management; reverse logistics
Prof. Dr. Youngjae Jang

E-Mail Website
Guest Editor
Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
Interests: AI-based smart factory and supply chain

Special Issue Information

Dear Colleagues,

The word "SMART" is one of most popular keywords for representing recent research topics on manufacturing as well as logistics. With the advancement of various technologies, manufacturing and logistics are gradually developing, and in particular, the issue of environmental considerations, referred to as sustainability, is a topic that should be dealt with, as important as it is. In addition, for the efficient operation of production and logistics in a distributed environment, collaboration among companies (i.e., inter-collaboration), collaboration within companies (i.e., intra-collaboration), and human–machine or machine–machine collaboration are essential. In order to facilitate a sustainable and collaborative environment, the manufacturing industry must apply and utilize advanced technologies such as cyber–physical production systems (CPPSs), artificial intelligence (AI), internet-of-things/everything/behavior (IoT/IoE/IoB) applications, big data analytics, 3D printing, cloud/fog/edge computing, and reality technology (AR/VR/MR/XR), which have recently been actively researched. The aforementioned technologies can be applied not only to the manufacturing industry but also to the optimization and efficient operation of the logistics industry. This Special Issue will emphasize the impact of the development of innovative theories, models, systems, and applications of advanced technologies facilitating smart manufacturing and logistics. Sustainability, in this Special Issue, can be considered in the efficient monitoring and use of energy and resources during production and logistics, reduction of waste, elimination of environmental harmfulness, etc. Collaboration can also be considered among cooperating companies, business partners, human workers, robots, machines, and even products or parts.

This Special Issue will provide knowledge, experience, and applications in the recent achievements in the area of sustainable and collaborative manufacturing and logistics. It will address all aspects of smart manufacturing and logistics, such as planning, control, the management of the supply network, production, logistics, the development of smart manufacturing and logistics support systems, and the applications of advanced technologies, while focusing on issues of sustainability and collaboration.

We welcome papers on the following topics for the Special Issue:

  • Sustainability-enhanced methodologies and models for manufacturing and logistics;
  • Applications of advanced technologies for sustainable and collaborative smart production and logistics;
  • Innovative planning, control, and management of supply networks for sustainability and collaboration objectives;
  • Theory and methodology for collaborative manufacturing and logistics, and their applications, including topics on collaboration platforms or co-bots.

Prof. Dr. Kwangyeol Ryu
Prof. Dr. Dong-Ho Lee
Prof. Dr. Youngjae Jang
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

  • sustainability
  • smart manufacturing
  • smart logistics
  • collaboration
  • planning, control, and management
  • advanced technology (CPPS, AI, IoT/IoE/IoB, big data analytics, 3D printing, cloud/fog/edge computing, and AR/VR/MR/XR)

Published Papers (3 papers)

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Research

15 pages, 2892 KiB  
Article
Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly
by Yee Yeng Liau and Kwangyeol Ryu
Sustainability 2021, 13(21), 12044; https://0-doi-org.brum.beds.ac.uk/10.3390/su132112044 - 31 Oct 2021
Cited by 9 | Viewed by 2561
Abstract
Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC [...] Read more.
Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC systems, humans control the execution of robot tasks, and this causes delays in the operation. Therefore, we propose a status recognition system to enable the early execution of robot tasks without human control during the HRC mold assembly operation. First, we decompose the mold assembly operation into task and sub-tasks, and define the actions representing the status of sub-tasks. Second, we develop status recognition based on parts, tools, and actions using a pre-trained YOLOv5 model, a one-stage object detection model. We compared four YOLOv5 models with and without a freezing backbone. The YOLOv5l model without a freezing backbone gave the optimal performance with a mean average precision (mAP) value of 84.8% and an inference time of 0.271 s. Given the success of the status recognition, we simulated the mold assembly operations in the HRC environment and reduced the assembly time by 7.84%. This study improves the sustainability of the mold assembly from the point of view of human safety, with reductions in human workload and assembly time. Full article
(This article belongs to the Special Issue Sustainable and Collaborative Smart Manufacturing and Logistics)
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26 pages, 3962 KiB  
Article
Collaborative Optimization of Storage Location Assignment and Path Planning in Robotic Mobile Fulfillment Systems
by Jianming Cai, Xiaokang Li, Yue Liang and Shan Ouyang
Sustainability 2021, 13(10), 5644; https://0-doi-org.brum.beds.ac.uk/10.3390/su13105644 - 18 May 2021
Cited by 19 | Viewed by 4135
Abstract
The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage [...] Read more.
The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage location assignment and path planning into one optimization problem from the perspective of collaborative optimization. A sustainable mathematical model for the collaborative optimization of storage location assignment and path planning (COSLAPP) is established, which considers the relationship between the location assignment of goods and rack storage and path planning in an RMFS. On this basis, we propose a location assignment strategy for goods clustering and rack turnover, which utilizes reservation tables, sets AGV operation rules to resolve AGV running conflicts, and improves the A-star(A*) algorithm based on the node load to find the shortest path by which the AGV handling the racks can complete the order picking. Ultimately, simulation studies were performed to ascertain the effectiveness of COSLAPP in the RMFS; the results show that the new approach can significantly improve order picking efficiency, reduce energy consumption, and lessen the operating costs of the warehouse of a distribution center. Full article
(This article belongs to the Special Issue Sustainable and Collaborative Smart Manufacturing and Logistics)
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19 pages, 1395 KiB  
Article
Vehicle Routing Problem Considering Reconnaissance and Transportation
by Byungjun Ju, Minsu Kim and Ilkyeong Moon
Sustainability 2021, 13(6), 3188; https://0-doi-org.brum.beds.ac.uk/10.3390/su13063188 - 14 Mar 2021
Cited by 4 | Viewed by 2247
Abstract
Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This paper proposes a vehicle routing problem considering reconnaissance and transportation (VRPCRT) for [...] Read more.
Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This paper proposes a vehicle routing problem considering reconnaissance and transportation (VRPCRT) for wartime troop movements. The VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements and reconnaissance, and transportation vehicle routes were determined simultaneously in the VRPCRT. For this paper, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed, and computational experiments were conducted to compare the ACO algorithm’s performance and that of the mixed-integer programming model. The performance of the ACO algorithm was shown to yield excellent results even for the real-size problem. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed. Full article
(This article belongs to the Special Issue Sustainable and Collaborative Smart Manufacturing and Logistics)
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