Transportation Planning, Management and Optimization
Deadline for manuscript submissions: 20 April 2024 | Viewed by 13567
Interests: cognitive radio, multimedia transmission, and machine learning
Special Issues, Collections and Topics in MDPI journals
Transportation, along with manufacturing and warehousing, is one of the main components in supply chain processes. It comprises a lot of activities—from delivery planning to carrier management to reverse logistics for recycling—that have to be properly handled. In recent years, the new realities of the logistics environment have made transportation optimization more important than ever before. For example, the recycling process in reverse logistics is known as the process for allowing different materials in products to be reused in future manufacturing processes, which is essential for sustainable industrial manufacturing. However, the current process may produce a certain loss of materials and result in environmental pollution due to the lack of recycling efficiency.
Hence, research on intelligent transport planning, management and optimization has recently attracted more attention from academia and industry. Transportation planning and management is the process of looking at the current state of transportation in the region, designing for future transportation needs, and combining all of that with the requirement of commercial, political, and other objectives, e.g., study of more efficient and environmentally friendly reverse logistics technologies in recycling. On the other hand, artificial intelligence has been widely deployed for improving the efficiency of manufacturing, transportation, recycling of energy and materials, etc., while the design of intelligent transportation technologies relies on a great amount of high-quality data.
In this Special Issue, recent efforts and advances made for intelligent transport planning, management, and optimization will be discussed. The topics of interest include but are not limited to the following research areas:
- AI for sustainable transportation and manufacturing;
- Deep learning for recyclable material transportation;
- Plan and forecast network needs;
- Smart transportation route optimization;
- Machine learning for transportation efficiency improvement;
- Intelligent reverse logistic technology;
- Integration of manufacturing, transportation, and recycling;
- Related value assessment and pricing strategy;
- IoT for smart transportation, manufacturing, and warehousing.
Prof. Dr. Xinlin Huang
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 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.
- intelligent transportation system
- transportation planning
- transportation management
- reverse logistic efficiency
- pricing strategy
- route optimization
- machine learning
- neuron network