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Production and Operations Management Powered by Artificial Intelligence and Data Analytics

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5371

Special Issue Editors

Dr. Hongbo Li

E-Mail Website
Guest Editor
School of Management, Shanghai University, Shanghai 200444, China
Interests: artificial intelligence; data analytics; project management
Dr. Wenchao Wei

E-Mail Website
Guest Editor
School of Economics and Management, Beijing Jiaotong University, Beijing 100084, China
Interests: supply chain management; scheduling and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Hongli Zhu

E-Mail Website
Guest Editor
School of Business Administration, Shandong Technology and Business University, Yantai 264005, China
Interests: production scheduling; logistics management
Dr. Fang Xie

E-Mail Website
Guest Editor
School of Economics and Management, Yantai University, Yantai 264005, China
Interests: project management and scheduling

Special Issue Information

Dear Colleagues,

In today’s highly competitive and globalized market environment, in order to maintain a long-term competitive advantage, it is vital for companies to adopt sustainable and effective production and operations management methods. Therefore, both the industry and academia devote resources to developing various approaches based on computer, decision, and mathematical sciences to deal with complex decision problems that arise in production and operations management.

Recent years have witnessed significant advances in artificial intelligence and data analytics. There has been a growing research effort that attempts to develop and apply artificial intelligence and data analytics methods that are suitable for production and operations management problems. Artificial intelligence and data analytics provide promising data-driven opportunities for improving the delivery of products and services by better using limited resources.

This Special Issue seeks to champion the integration of artificial intelligence and data analytics into production and operations management. Original research articles and reviews are welcomed in this Special Issue, encompassing a broad spectrum of research areas with a focus on promoting sustainability. Potential research domains include, but are not confined to, the following:

  • Sustainable production and operations management;
  • Production and operations management based on artificial intelligence (machine learning, data mining, metaverse, etc.);
  • Production and operations management based on data analytics;
  • Data-driven production and operations management;
  • Production planning and scheduling;
  • Project management and scheduling;
  • Logistics and supply chain management;
  • Information systems and operations management;
  • Product innovation and technology management;
  • Optimization models and exact/meta-heuristic algorithms in production and operations management.

We look forward to receiving your contributions.

Dr. Hongbo Li
Prof. Dr. Wenchao Wei
Dr. Hongli Zhu
Dr. Fang Xie
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

  • production and operations management
  • artificial intelligence
  • data analytics
  • sustainability
  • optimization

Published Papers (4 papers)

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Research

25 pages, 5328 KiB  
Article
Storage Location Assignment for Improving Human–Robot Collaborative Order-Picking Efficiency in Robotic Mobile Fulfillment Systems
by Yue Chen and Yisong Li
Sustainability 2024, 16(5), 1742; https://doi.org/10.3390/su16051742 - 20 Feb 2024
Viewed by 687
Abstract
The robotic mobile fulfillment (RMF) system is a parts-to-picker warehousing system and a sustainable technology used in human–robot collaborative order picking. Storage location assignment (SLA) tactically benefits order-picking efficiency. Most studies focus on the retrieval efficiency of robots to solve SLA problems. To [...] Read more.
The robotic mobile fulfillment (RMF) system is a parts-to-picker warehousing system and a sustainable technology used in human–robot collaborative order picking. Storage location assignment (SLA) tactically benefits order-picking efficiency. Most studies focus on the retrieval efficiency of robots to solve SLA problems. To further consider the crucial role played by human pickers in RMF systems, especially in the context that the sustainable performance of human workers should be paid attention to in human–robot collaboration, we solve the SLA problem by aiming to improve human–robot collaborative order-picking efficiency. This study specifically makes decisions on assigning multiple items of various products to the slots of pods in the RMF system, in which human behavioral factors are taken into account. To obtain the solution in one mathematical model, we propose the heuristic algorithm under a two-stage optimization method. The results show that assigning correlated products to pods improves the retrieval efficiency of robots compared to class-based assignment. We also find that assigning items of each product to slots of pods, considering behavioral factors, benefits the operation efficiency of human pickers compared to random assignment. Improving human–robot collaborative order-picking efficiency and increasing the capacity usage of pods benefits sustainable warehousing management. Full article
(This article belongs to the Special Issue Production and Operations Management Powered by Artificial Intelligence and Data Analytics)
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26 pages, 4003 KiB  
Article
Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting
by Xue Zhou, Yajian Ke, Jianhui Zhu and Weiwei Cui
Sustainability 2024, 16(1), 333; https://doi.org/10.3390/su16010333 - 29 Dec 2023
Cited by 1 | Viewed by 1222
Abstract
Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good [...] Read more.
Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future. Full article
(This article belongs to the Special Issue Production and Operations Management Powered by Artificial Intelligence and Data Analytics)
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16 pages, 2475 KiB  
Article
Integrated Location Selection and Scheduling Problems for Inland Container Transportation
by Wenchao Wei, Zining Dong and Jinkui Fan
Sustainability 2023, 15(22), 15992; https://doi.org/10.3390/su152215992 - 16 Nov 2023
Viewed by 744
Abstract
Well-organized network configuration is the key to the success of inland container transportation systems. In this study, we firstly propose an integrated framework for the location selection of inland container depots (ICDs) and the scheduling of containers and trucks. The objective is to [...] Read more.
Well-organized network configuration is the key to the success of inland container transportation systems. In this study, we firstly propose an integrated framework for the location selection of inland container depots (ICDs) and the scheduling of containers and trucks. The objective is to minimize the total cost of setting up the ICDs and transportation cost associated with trucks and containers. A mixed-integer linear programming (MILP) model is developed to solve the proposed problem. The computational studies show that the proposed decision approach is effective and can reduce the total operating costs of ICDs and transportation costs of containers. Sensitivity analysis on the impact of customer distributions and the number of ICDs on the total cost are conducted to reveal the characteristics of the problem. The utilization of ICDs can significantly improve the efficiency of the transportation network, i.e., the total cost is reduced by at least 27% for the proposed instances, and the transportation distance of empty containers is reduced by at least 4%. Finally, managerial insights and future research directions are provided. Full article
(This article belongs to the Special Issue Production and Operations Management Powered by Artificial Intelligence and Data Analytics)
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13 pages, 897 KiB  
Article
Cold Chain Logistics Network Design for Fresh Agricultural Products with Government Subsidy
by Hongli Zhu, Congcong Liu, Guanghua Wu and Yanjun Gao
Sustainability 2023, 15(13), 10021; https://doi.org/10.3390/su151310021 - 25 Jun 2023
Viewed by 2176
Abstract
This paper investigates the cold chain logistics network design in the first mile for fresh agricultural products with government subsidy, involving the capacity, location of cold storage facilities, and transportation from production areas to cold storage facilities after harvest. A bi-level programming model [...] Read more.
This paper investigates the cold chain logistics network design in the first mile for fresh agricultural products with government subsidy, involving the capacity, location of cold storage facilities, and transportation from production areas to cold storage facilities after harvest. A bi-level programming model is formulated considering the quality degradation of fresh agricultural products. Based on the proposition and KKT conditions, a solution method is designed for reformulating the bi-level model into a single-level programming model. Numerical experiments are conducted to verify the proposed model. Experimental results show that the solution method efficiently solves the problem of the cold chain logistics network design for fresh agricultural products with subsidy, and sensitivity analysis provides managerial insights for decision-makers. Full article
(This article belongs to the Special Issue Production and Operations Management Powered by Artificial Intelligence and Data Analytics)
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