Special Issue "Mathematical Methods of Operational Research and Data Analytics in Operations Planning and Scheduling"
Deadline for manuscript submissions: 31 March 2024 | Viewed by 101
Interests: scheduling; manufacturing; healthcare; supply chain; combinatorial optimization; computational complexity; metaheuristics
We are pleased to present this Special Issue, which aims to explore the latest advances, methodologies, and applications of operational research and data analytics in the fields of planning and scheduling. The articles presented here underscore the importance of operational research and data analytics in addressing complex planning and scheduling challenges across various industries such as manufacturing, transportation, logistics, and healthcare, to name a few. The key topics covered in this Special Issue include, but are not limited to, the following:
- Innovative mathematical algorithms: These papers feature research on cutting-edge mathematical algorithms and optimization techniques, devised to tackle specific planning and scheduling challenges. This Special Issue also highlights hybrid approaches that combine various methods to enhance performance.
- Machine learning and artificial intelligence: These papers examine the integration of machine learning and artificial intelligence methods within operational research, showcasing their potential in augmenting decision-making processes and predictive capabilities in planning and scheduling tasks.
- Real-world applications and case studies: These papers delve into the practical implementation of mathematical operational research techniques across diverse sectors, emphasizing their impact on efficiency, cost reduction, and overall performance improvement.
- Theoretical advancements in production and operations management: These papers concentrate on the development of new mathematical models and frameworks, contributing to a deeper understanding of the principles of production and operations management.
This Special Issue offers a comprehensive perspective on the current state of operational research and data analytics applied to various problems in production and operations management. By showcasing groundbreaking research and real-world applications, we aim to further the development and refinement of operational research and data analytics techniques and strategies to address complex decision-making challenges in various industries.
Prof. Dr. Fazle Baki
Prof. Dr. Ahmed Azab
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.
- scheduling, models
- supply chain
- combinatorial optimization
- computational complexity
- design of algorithms
- special cases
- polynomial algorithms
- exact methods
- heuristic methods
- machine learning
- artificial intelligence