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Mathematics
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Mathematical Modeling and Intelligent Algorithms in Operations Research
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Mathematical Modeling and Intelligent Algorithms in Operations Research

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 August 2025 | Viewed by 743

Special Issue Editors

Prof. Dr. Álvaro García-Sánchez

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Guest Editor
Department of Organization Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, Jose Abascal 2, 28006 Madrid, Spain
Interests: operations research; simulation; supply chain management; operations management
Prof. Dr. Angel A. Juan

E-Mail Website
Guest Editor
Research Center on Production Management and Engineering, Universitat Politècnica de València, Plaza Ferrandiz-Carbonell, 03801 Alcoy, Spain
Interests: operations research; artificial intelligence; transportation; simulation optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit a manuscript to this Special Issue of Mathematics, which is dedicated to novel mathematical programming models, intelligent algorithms, and their application in complex Operations Research (OR) problems. This Special Issue aims to compile innovative research that leverages advanced mathematical techniques and intelligent algorithms to address real-world challenges across diverse industries and sectors.

We welcome the submission of papers that cover a broad range of fields, including (but not limited to) transportation, logistics, supply chain management, smart cities, finance, healthcare, production systems, manufacturing, and telecommunication systems.

Submissions should focus on methodologies such as exact optimization methods, metaheuristics, reinforcement learning, matheuristics, simheuristics (simulation-optimization), and learnheuristics (integration of heuristics with machine learning techniques), among others. We particularly welcome articles that present original methodological contributions, alongside practical OR applications grounded in real-world case studies.

This Special Issue aims to highlight research that not only promotes innovation in mathematical modelling and the design of intelligent algorithms, but also exhibits a significant practical impact regarding the solution of challenging and high-stakes problems in operations research.

Prof. Dr. Álvaro García-Sánchez
Prof. Dr. Angel A. Juan
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

  • mathematical programming
  • hybrid intelligent algorithms
  • simulation optimization
  • artificial intelligence
  • operations research
  • machine learning
  • reinforcement learning

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Published Papers (1 paper)

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Research

37 pages, 6596 KiB  
Article
Optimizing Route Planning via the Weighted Sum Method and Multi-Criteria Decision-Making
by Guanquan Zhu, Minyi Ye, Xinqi Yu, Junhao Liu, Mingju Wang, Zihang Luo, Haomin Liang and Yubin Zhong
Mathematics 2025, 13(11), 1704; https://doi.org/10.3390/math13111704 - 22 May 2025
Viewed by 386
Abstract
Choosing the optimal path in planning is a complex task due to the numerous options and constraints; this is known as the trip design problem (TTDP). This study aims to achieve path optimization through the weighted sum method and multi-criteria decision analysis. Firstly, [...] Read more.
Choosing the optimal path in planning is a complex task due to the numerous options and constraints; this is known as the trip design problem (TTDP). This study aims to achieve path optimization through the weighted sum method and multi-criteria decision analysis. Firstly, this paper proposes a weighted sum optimization method using a comprehensive evaluation model to address TTDP, a complex multi-objective optimization problem. The goal of the research is to balance experience, cost, and efficiency by using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to assign subjective and objective weights to indicators such as ratings, duration, and costs. These weights are optimized using the Lagrange multiplier method and integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. Additionally, a weighted sum optimization method within the Traveling Salesman Problem (TSP) framework is used to maximize ratings while minimizing costs and distances. Secondly, this study compares seven heuristic algorithms—the genetic algorithm (GA), particle swarm optimization (PSO), the tabu search (TS), genetic-particle swarm optimization (GA-PSO), the gray wolf optimizer (GWO), and ant colony optimization (ACO)—to solve the TOPSIS model, with GA-PSO performing the best. The study then introduces the Lagrange multiplier method to the algorithms, improving the solution quality of all seven heuristic algorithms, with an average solution quality improvement of 112.5% (from 0.16 to 0.34). The PSO algorithm achieves the best solution quality. Based on this, the study introduces a new variant of PSO, namely PSO with Laplace disturbance (PSO-LD), which incorporates a dynamic adaptive Laplace perturbation term to enhance global search capabilities, improving stability and convergence speed. The experimental results show that PSO-LD outperforms the baseline PSO and other algorithms, achieving higher solution quality and faster convergence speed. The Wilcoxon signed-rank test confirms significant statistical differences among the algorithms. This study provides an effective method for experience-oriented path optimization and offers insights into algorithm selection for complex TTDP problems. Full article
(This article belongs to the Special Issue Mathematical Modeling and Intelligent Algorithms in Operations Research)
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