Optimization Algorithms for Operations Research and Scheduling Problems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 653
Special Issue Editors
Interests: production planning; inventory; logistics; scheduling; inventory management; text mining; operations management; support vector machine; forecasting; econometrics
Special Issues, Collections and Topics in MDPI journals
Interests: business intelligence; big data mining; decision support systems; energy forecasting; financial management
Special Issues, Collections and Topics in MDPI journals
Interests: energy finance; energy economics and policy analysis; macroeconomic model; investment and financing decision and risk management; Bayesian statistics
Special Issues, Collections and Topics in MDPI journals
Interests: optimization; logistics; simulation; heuristics; modeling; scheduling; combinatorial optimization; discrete event simulation; discrete optimization; optimization modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increasing complexity of operations research and scheduling problems in various industries requires advanced optimization algorithms to enhance efficiency, reduce costs, and improve decision-making. This Special Issue aims to explore the latest advancements in optimization algorithms applied to operations research and scheduling, focusing on transportation, logistics, supply chain management, manufacturing, healthcare, and energy systems, amongst possible others.
We welcome high-quality original research and review articles that contribute to the theoretical and practical aspects of optimization algorithms for operations research and scheduling problems. Topics of interest include, but are not limited to, the following:
- Novel mathematical programming models for scheduling and resource allocation;
- Combinatorial optimization techniques for production planning and logistics;
- Metaheuristic and hybrid optimization approaches in large-scale scheduling problems;
- Stochastic and robust optimization in uncertain environments;
- Artificial intelligence and machine learning applications in scheduling;
- Multi-objective and multi-criteria decision-making models;
- Advances in vehicle routing, workforce scheduling, and project management;
- Sustainable and green logistics optimization;
- Real-world case studies and industrial applications of optimization algorithms.
We encourage submissions that introduce innovative methodologies, theoretical advancements, and real-world applications. We look forward to receiving your contributions to this Special Issue.
Prof. Dr. Kin Keung Lai
Prof. Dr. Lean Yu
Prof. Dr. Jian Chai
Prof. Dr. Kanchana Sethanan
Guest Editors
Manuscript Submission Information
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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
- mathematical programming
- combinatorial optimization
- metaheuristic algorithms
- multi-objective decision making
- stochastic and robust optimization
- AI and machine learning in optimization
- supply chain and logistics scheduling
- vehicle routing and workforce scheduling
- sustainable and green logistics
- project and resource management
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