Advances in Mathematical Analytics and Operations Research

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1484

Special Issue Editor


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Guest Editor
Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla 72453, Mexico
Interests: operations research; inventory management; supply chain management; facility location problem

Special Issue Information

Dear Colleagues,

Advances in Mathematical Analytics and Operations Research encompass a broad spectrum of enhancements and innovations in the methodologies, algorithms, and applications of mathematics for data analysis, complex problem solving, and deriving insights across various disciplines. This field integrates traditional mathematical techniques with contemporary computational tools to manage large-scale data, optimize processes, and facilitate informed decision making. Furthermore, it involves the development of novel methodologies, algorithms, and applications to optimize decision-making processes in intricate systems. These advancements significantly improve the capacity to model, analyze, and solve problems across diverse domains such as logistics, manufacturing, finance, healthcare, and more.

We seek papers that contribute to these areas, presenting original research, case studies, and reviews that reflect the latest developments and applications in mathematical analytics and operations research.

Dr. Amir Hossein Nobil
Guest Editor

Manuscript Submission Information

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Keywords

  • mathematical analytics
  • operations research
  • optimization techniques
  • supply chain optimization
  • decision analysis
  • numerical methods
  • algorithm development
  • process optimization

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Published Papers (2 papers)

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Research

25 pages, 4420 KiB  
Article
Optimizing Dynamic Evacuation Using Mixed-Integer Linear Programming
by Hamoud Bin Obaid, Theodore B. Trafalis, Mastoor M. Abushaega, Abdulhadi Altherwi and Ahmed Hamzi
Mathematics 2025, 13(1), 12; https://doi.org/10.3390/math13010012 - 24 Dec 2024
Viewed by 532
Abstract
This study presents a new approach to optimize the dynamic evacuation process through a dynamic traffic assignment model formulated using mixed-integer linear programming (MILP). The model approximates the travel time for evacuee groups with a piecewise linear function that accounts for variations in [...] Read more.
This study presents a new approach to optimize the dynamic evacuation process through a dynamic traffic assignment model formulated using mixed-integer linear programming (MILP). The model approximates the travel time for evacuee groups with a piecewise linear function that accounts for variations in travel time due to load-dependent factors. Significant delays are transferred to subsequent groups to simulate delay propagation. The primary objective is to minimize the network clearance time—the total time required for the last group of evacuees to reach safety from the start of the evacuation. Given the model’s computational intensity, a simplified version is introduced for comparison. Both the original and simplified models are tested on small networks and benchmarked against the Cell Transmission Model, a well-regarded method in dynamic traffic assignment literature. Additional objectives, including average travel time and average evacuation time, are explored. A sensitivity analysis is conducted to assess how varying the number of evacuee groups impacts model outcomes. Full article
(This article belongs to the Special Issue Advances in Mathematical Analytics and Operations Research)
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11 pages, 861 KiB  
Article
A Numerical Stability Analysis in the Inclusion of an Inverse Term in the Design of Experiments for Mixtures
by Javier Cruz-Salgado, Sergio Alonso-Romero, Edgar Augusto Ruelas-Santoyo, Israel Miguel-Andrés, Roxana Zaricell Bautista-López and Amir Hossein Nobil
Mathematics 2024, 12(22), 3587; https://doi.org/10.3390/math12223587 - 16 Nov 2024
Viewed by 652
Abstract
A mixture experiment is one where the response depends only on the relative proportions of the ingredients present in the mixture. Different regression models are used to analyze mixture experiments, such as the Scheffé model, the Slack Variable model, and models with inverse [...] Read more.
A mixture experiment is one where the response depends only on the relative proportions of the ingredients present in the mixture. Different regression models are used to analyze mixture experiments, such as the Scheffé model, the Slack Variable model, and models with inverse terms. Models with inverse terms are worthy of consideration in certain applications. These models have been analyzed considering their fit quality, but not their numerical stability. This article analyzes the numerical stability of the model with inverse terms and the use of pseudo components. Likewise, a criterion is defined for the selection of the regression model with inverse terms, based on the quality of fit and numerical stability. Full article
(This article belongs to the Special Issue Advances in Mathematical Analytics and Operations Research)
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