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Mathematics
Special Issues
Theory and Applications of Scheduling and Optimization
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Theory and Applications of Scheduling and Optimization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 643

Special Issue Editors

Prof. Dr. Bertrand M.T. Lin

E-Mail Website
Guest Editor
Institute of Information Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: scheduling theory; discrete optimization
Prof. Dr. Shyong Jian Shyu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Ming Chuan University, Taipei 111, Taiwan
Interests: design and analysis of algorithms; visual cryptography and secret sharing; computational intelligence; parallel processing
Prof. Dr. Peng-Yeng Yin

E-Mail Website
Guest Editor
Department of Information Technology and Management, Ming Chuan University, Taipei 111, Taiwan
Interests: machine learning; meta-heuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Scheduling refers to allocating limited resources, like machines, personnel, and transport vehicles, to economic activities over a period of time to achieve managerial excellence. The practical significance of scheduling theory can be seen in various manufacturing and service industries as well as in public sectors. This Special Issue will focus on recent theoretical analysis and algorithm designs used to solve optimization problems in scheduling. Case studies of real applications and reviews are welcome. Topics include, but are not limited to, the following:

  • Discrete and stochastic optimization models;
  • Design and analysis optimization algorithms;
  • Performance appraisal of models and algorithms;
  • Metaheuristics;
  • Machine learning techniques;
  • Reviews or surveys;
  • Case studies.

Prof. Dr. Bertrand M.T. Lin
Prof. Dr. Shyong Jian Shyu
Prof. Dr. Peng-Yeng Yin
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

  • scheduling
  • design and analysis of algorithms
  • meta-heuristics
  • machine learning

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Published Papers (1 paper)

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Research

24 pages, 2850 KiB  
Article
Solving Three-Stage Operating Room Scheduling Problems with Uncertain Surgery Durations
by Yang-Kuei Lin and Chin Soon Chong
Mathematics 2025, 13(12), 1973; https://doi.org/10.3390/math13121973 - 15 Jun 2025
Viewed by 458
Abstract
Operating room (OR) scheduling problems are often addressed using deterministic models that assume surgery durations are known in advance. However, such assumptions fail to reflect the uncertainty that often occurs in real surgical environments, especially during the surgery and recovery stages. This study [...] Read more.
Operating room (OR) scheduling problems are often addressed using deterministic models that assume surgery durations are known in advance. However, such assumptions fail to reflect the uncertainty that often occurs in real surgical environments, especially during the surgery and recovery stages. This study focuses on a robust scheduling problem involving a three-stage surgical process that includes pre-surgery, surgery, and post-surgery stages. The scheduling needs to coordinate multiple resources—pre-operative holding unit (PHU) beds, ORs, and post-anesthesia care unit (PACU) beds—while following a strict no-wait rule to keep patient flow continuous without delays between stages. The main goal is to minimize the makespan and improve schedule robustness when surgery and post-surgery durations are uncertain. To solve this problem, we propose a Genetic Algorithm for Robust Scheduling (GARS), which evaluates solutions using a scenario-based robustness criterion derived from multiple sampled instances. GARS is compared with four other algorithms: a deterministic GA (GAD), a random search (BRS), a greedy randomized insertion and swap heuristic (GRIS), and an improved version of GARS with simulated annealing (GARS_SA). The results from different problem sizes and uncertainty levels show that GARS and GARS_SA consistently perform better than the other algorithms. In large-scale tests with moderate uncertainty (30 surgeries, α = 0.5), GARS achieves an average makespan of 633.85, a standard deviation of 40.81, and a worst-case performance ratio (WPR) of 1.00, while GAD reaches 673.75, 54.21, and 1.11, respectively. GARS can achieve robust performance without using any extra techniques to strengthen the search process. Its structure remains simple and easy to use, making it a practical and effective approach for creating reliable and efficient surgical schedules under uncertainty. Full article
(This article belongs to the Special Issue Theory and Applications of Scheduling and Optimization)
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