Recent Advances in Planning and Scheduling for Supply Chain Optimization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 443

Special Issue Editors


E-Mail Website
Guest Editor
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
Interests: production scheduling; combination optimization; evolutionary computation
Special Issues, Collections and Topics in MDPI journals
School of International Economics and Business, Nanjing University of Finance & Economics, Nanjing 210023, China
Interests: machine scheduling; approximation algorithm; process optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intellectualization and greenization of supply chains are driving transformative innovations in optimization theories and operation techniques, enabling enterprises to achieve cost reduction and efficiency enhancement. Recent advancements in smart technologies (e.g., big data, cloud computing, and AI) have intensified academic and industrial focus on the optimization of planning and scheduling—the decision-making cores of supply chain management. Numerous successful applications have been presented in supply chain domains, including order processing, product manufacture, equipment assembly, warehousing and transportation, etc.

This Special Issue aims to collect up-to-date and high-quality studies incorporating novel methods on planning and scheduling in the area of supply chain optimization, as well as to promote developments and applications of operations research theory and methods in relevant fields. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Scheduling in advanced manufacturing;
  • Logistics scheduling and optimization;
  • AI-based planning and scheduling;
  • Routing optimization in distribution;
  • Data-driven production scheduling;
  • Optimization for facility location.

We look forward to receiving your contributions.

Prof. Dr. Danyu Bai
Dr. Dehua Xu
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. Mathematics 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 2600 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

  • planning and scheduling
  • production scheduling
  • logistics optimization
  • facility location
  • combination optimization
  • evolutionary computation
  • machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 2268 KiB  
Article
Improved Fuel Consumption Estimation for Sailing Speed Optimization: Eliminating Log Transformation Bias
by Qi Hong, Xuecheng Tian, Yong Jin, Zhiyuan Liu and Shuaian Wang
Mathematics 2025, 13(12), 1987; https://doi.org/10.3390/math13121987 - 16 Jun 2025
Viewed by 216
Abstract
Sailing Speed Optimization (SSO) is a crucial problem in shipping operations management, aiming to reduce both operating costs and carbon dioxide emissions. The ship’s sailing speed directly impacts fuel consumption, where fuel consumption is generally assumed to follow a power function with respect [...] Read more.
Sailing Speed Optimization (SSO) is a crucial problem in shipping operations management, aiming to reduce both operating costs and carbon dioxide emissions. The ship’s sailing speed directly impacts fuel consumption, where fuel consumption is generally assumed to follow a power function with respect to sailing speed. Previous studies have used transformation-based fitting methods, such as logarithmic transformations, to investigate the relationship between sailing speed and fuel consumption using historical data. However, these methods introduce estimation bias and heteroskedasticity, violating the core assumptions of Ordinary Least Squares (OLS) used for general linear regression. To address these issues, we propose two novel fitting methods that directly optimize the original nonlinear model without relying on transformations. By analyzing the characteristics of the objective function, we derive parameter constraints and integrate them into a discrete optimization framework, resulting in improved fitting accuracy. Our methods are validated through extensive case studies, demonstrating their effectiveness in enhancing the reliability of SSO decisions. These methods offer a practical approach to optimizing fuel consumption in real-world maritime operations. Full article
Show Figures

Figure 1

Back to TopTop