Advances in Planning and Scheduling Theory
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 205
Editors
Interests: scheduling, in particular development of exact and approximate algorithms; stability investigations is discrete optimization; scheduling with interval processing times; complexity investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation and applications
Interests: production planning; manufacturing engineering; supply chain management; mathematical programming optimizers
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We invite you to submit your latest research in the area of the development of algorithms for scheduling and planning problems to this Special Issue. We are looking for new and innovative approaches for solving such problems exactly or approximately. High-quality papers are solicited to address both theoretical and practical issues of scheduling and planning algorithms. Submissions are welcome both for traditional scheduling and planning problems, as well as new applications.
Potential theoretical topics include, but are not limited to, sequencing in single- and multi-stage systems with additional constraints, single- or multi-criteria objectives as well as joint solving planning and scheduling problems.
Potential application topics to be addressed in this issue include, but also are not limited to, exact and approximate models and methods for sustainable manufacturing, the consideration of energy constraints in scheduling and planning of production systems; scheduling approaches in Industry 4.0 and 5.0 for minimizing waste or carbon emissions, as well as a broad spectrum of scheduling problems in emerging applications, such as sports, healthcare, energy management, cloud computing or internet of things. A focus will also be on coupling operations research techniques with artificial intelligence algorithms and data driven approaches in real life applications.
Prof. Dr. Frank Werner
Prof. Dr. Alexandre Dolgui
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly 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 1800 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
- scheduling and planning in manufacturing systems
- scheduling for segments of supply chains
- scheduling approaches in logistics, transport, and energy management
- scheduling for cloud computing and IOT
- resource-constrained project scheduling problems
- assembly line balancing and scheduling
- multi-criteria scheduling
- just-in-time and real-time scheduling
- enumerative, approximate and heuristic algorithms
- artificial intelligence tools for scheduling and planning
- coupling machine-learning with operations research techniques
- application of neural networks to scheduling and planning problems
- AI-driven scheduling and planning in different applications
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