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Innovations in Optimization and Operations Research
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Innovations in Optimization and Operations Research

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 9130

Special Issue Editors

Prof. Dr. Igor Litvinchev

E-Mail Website
Guest Editor
Graduate Program in Systems Engineering, Nuevo Leon State University (UANL), Av. Universidad s/n, Col. Ciudad Universitaria, San Nicolas de los Garza 66455, Nuevo Leon, Mexico
Interests: modeling, optimization and control of large scale systems; optimization; operations research
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tetyana Romanova

E-Mail Website
Guest Editor
1. Leeds University Business School, University of Leeds, Leeds, West Yorkshire, UK
2. A. Pidhornyi Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine, Kharkiv, Ukraine
Interests: mathematical modeling; optimization; packing; operations research
Prof. Dr. Socorro Rangel

E-Mail Website
Guest Editor
Departamento de Matemática, IBILCE, UNESP—Universidade Estadual Paulista, SP, São José do Rio Preto 15054-000, Brazil
Interests: optimization; mathematical models; mixed integer linear optimization; cutting and packing; multiobjective optimization

Special Issue Information

Dear Colleagues,

The Special Issue "Innovations in Optimization and Operations Research" aims to bring together cutting-edge research and novel advancements in the fields of optimization and operations research. This Special Issue seeks to provide a platform for researchers, practitioners, and experts from diverse disciplines to share their innovative ideas, methodologies, and applications in tackling complex problems related to optimization, decision-making, and resource allocation.

The scope of this Special Issue encompasses, but is not limited to, the following topics: 

  • Metaheuristic algorithms and optimization techniques;
  • Multi-objective optimization and Pareto frontiers;
  • Operations research models and applications;
  • Combinatorial optimization and network optimization;
  • Stochastic optimization and uncertainty modeling;
  • Heuristics and algorithms for complex optimization problems;
  • Optimization in supply chain management and logistics;
  • Robust optimization and sensitivity analysis;
  • Game theory and its applications in optimization;
  • Evolutionary algorithms and swarm intelligence;
  • Optimization in finance, economics, and engineering;
  • Decision support systems and analytics;
  • Data-driven optimization and machine learning in operations research;
  • Applications of optimization in real-world problems;
  • Multidisciplinary applications of optimization and operations research;
  • Large scale optimization.

Prof. Dr. Igor Litvinchev
Prof. Dr. Tetyana Romanova
Prof. Dr. Socorro Rangel
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

  • optimization
  • operations research
  • metaheuristics
  • multi-objective optimization
  • combinatorial optimization
  • stochastic optimization
  • heuristics
  • supply chain management
  • logistics
  • robust optimization
  • game theory
  • evolutionary algorithms
  • swarm intelligence
  • finance
  • economics
  • engineering
  • decision support systems
  • data-driven optimization
  • machine learning
  • real-world applications
  • multidisciplinary applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

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18 pages, 1914 KiB  
Article
A Genetic Algorithm for Site-Specific Management Zone Delineation
by Francisco Huguet, Lluís M. Plà-Aragonés, Víctor M. Albornoz and Mauricio Pohl
Mathematics 2025, 13(7), 1064; https://doi.org/10.3390/math13071064 - 25 Mar 2025
Viewed by 375
Abstract
This paper presents a genetic algorithm-based methodology to address the Site-Specific Management Zone (SSMZ) delineation problem. A SSMZ is a subregion of a field that is homogeneous with respect to a soil or crop property, enabling farmers to apply customized management strategies for [...] Read more.
This paper presents a genetic algorithm-based methodology to address the Site-Specific Management Zone (SSMZ) delineation problem. A SSMZ is a subregion of a field that is homogeneous with respect to a soil or crop property, enabling farmers to apply customized management strategies for optimizing resource use. The algorithm generates optimized field partitions using rectangular zones, applicable to both regular and irregularly shaped fields. To the best of our knowledge, the Genetic Algorithm for Zone Delineation (GAZD) is the first approach to handle the rectangular SSMZ delineation problem in irregular-shaped lands without introducing non-real data. The algorithm’s performance is compared with an exact solution based on integer linear programming. Experimental tests conducted on real-field and generated irregular-shaped instances show that while the GAZD requires longer execution times than the exact approach, it proves to be functional and robust in solving the SSMZ problem. Furthermore, the GAZD offers a set of “good enough” solutions that can be evaluated for feasibility and practical convenience, making it a valuable tool for decision-making processes. Moreover, strategies such as implementation in a compiled language and parallel processing can be used to improve the execution time performance of the algorithm. Full article
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
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11 pages, 236 KiB  
Article
Decomposition Algorithm for a Nonlinear Three-Index Transportation Problem
by Vladimir Lobantsov, Alexander Tizik, Vladimir Tsurkov and Ivan Matveev
Mathematics 2025, 13(6), 944; https://doi.org/10.3390/math13060944 - 12 Mar 2025
Viewed by 348
Abstract
A three-index transportation problem is considered, where the indices stand for supplier, consumer, and type of product. A quite broad statement is allowed, including additional points of production, consumption, and nonlinear transportation costs therein. The solution method is a further development of a [...] Read more.
A three-index transportation problem is considered, where the indices stand for supplier, consumer, and type of product. A quite broad statement is allowed, including additional points of production, consumption, and nonlinear transportation costs therein. The solution method is a further development of a universal approach proposed earlier by the authors, which is based on the decomposition of the original problem into a sequence of two-dimensional problems with the recalculation of the objective function coefficients. This paper demonstrates the customization of the method to a nonlinear three-index problem. Full article
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
21 pages, 2624 KiB  
Article
A Model-Based Heuristic for Packing Soft Rotated Rectangles in an Optimized Convex Container with Prohibited Zones
by Oksana Melashenko, Tetyana Romanova, Igor Litvinchev, Carlos Gustavo Martínez Gomez, Rui Yang and Bingtao Sun
Mathematics 2025, 13(3), 493; https://doi.org/10.3390/math13030493 - 31 Jan 2025
Viewed by 659
Abstract
Packing soft rectangular objects in an optimized convex container is considered. Each soft rectangle can be freely translated and rotated, has a fixed area, and its dimensions can vary in certain limits. The convex container may have prohibited zones where allocation of the [...] Read more.
Packing soft rectangular objects in an optimized convex container is considered. Each soft rectangle can be freely translated and rotated, has a fixed area, and its dimensions can vary in certain limits. The convex container may have prohibited zones where allocation of the objects is not allowed. The soft rectangles must be arranged completely inside the container; mutual intersection and overlapping with prohibited zones is not allowed. The objective is to minimize a certain metric characteristic of the container. The corresponding nonlinear mathematical problem is formulated using the phi-function technique to present non-overlapping and containment conditions. A model-based heuristic is proposed to find reasonable solutions to the problem. Numerical results are provided for triangular, circular, and scaled polygonal containers to validate the model and demonstrate the proposed approach’s efficiency. Full article
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
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29 pages, 4178 KiB  
Article
Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks
by Umar Draz, Tariq Ali, Sana Yasin, Muhammad Hasanain Chaudary, Muhammad Ayaz, El-Hadi M. Aggoune and Isha Yasin
Mathematics 2024, 12(22), 3447; https://doi.org/10.3390/math12223447 - 5 Nov 2024
Cited by 1 | Viewed by 1162
Abstract
This research introduces a hybrid approach combining bio- and nature-inspired metaheuristic algorithms to enhance scheduling efficiency and minimize energy consumption in Underwater Acoustic Sensor Networks (UASNs). Five hybridized algorithms are designed to efficiently schedule nodes, reducing energy costs compared to existing methods, and [...] Read more.
This research introduces a hybrid approach combining bio- and nature-inspired metaheuristic algorithms to enhance scheduling efficiency and minimize energy consumption in Underwater Acoustic Sensor Networks (UASNs). Five hybridized algorithms are designed to efficiently schedule nodes, reducing energy costs compared to existing methods, and addressing the challenge of unscheduled nodes within the communication network. The hybridization techniques such as Elephant Herding Optimization (EHO) with Genetic Algorithm (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), and Binary Particle Swarm Optimization (BPSO) are used for optimization. To implement these optimization techniques, the Scheduled Routing Algorithm for Localization (SRAL) is introduced, aiming to enhance node scheduling and localization. This framework is crucial for improving data delivery, optimizing Route REQuest (RREQ) and Routing Overhead (RO), while minimizing Average End-to-End (AE2E) delays and localization errors. The challenges of node localization, RREQ reconstruction at the beacon level, and increased RO, along with End-to-End delays and unreliable data forwarding, have a significant impact on overall communication in underwater environments. The proposed framework, along with the hybridized metaheuristic algorithms, show great potential in improving node localization, optimizing scheduling, reducing energy costs, and enhancing reliable data delivery in the Internet of Underwater Things (IoUT)-based network. Full article
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
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61 pages, 12951 KiB  
Article
WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems
by Zoubida Benmamoun, Khaoula Khlie, Mohammad Dehghani and Youness Gherabi
Mathematics 2024, 12(7), 1059; https://doi.org/10.3390/math12071059 - 1 Apr 2024
Cited by 19 | Viewed by 3058
Abstract
Supply Chain (SC) Optimization is a key activity in today’s industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper [...] Read more.
Supply Chain (SC) Optimization is a key activity in today’s industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors. Full article
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
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24 pages, 3437 KiB  
Article
The Impacts of Payment Schemes and Carbon Emission Policies on Replenishment and Pricing Decisions for Perishable Products in a Supply Chain
by Chun-Tao Chang and Yao-Ting Tseng
Mathematics 2024, 12(7), 1033; https://doi.org/10.3390/math12071033 - 29 Mar 2024
Cited by 1 | Viewed by 1462
Abstract
In the supplier–retailer–consumer system, the retailer’s replenishment and pricing strategies impact the entire transaction process, forming a comprehensive trading market. Suppliers offer advance-cash-credit payments to retailers, while retailers provide customers with cash-credit payment options. In the current health-conscious consumer market, purchasing decisions are [...] Read more.
In the supplier–retailer–consumer system, the retailer’s replenishment and pricing strategies impact the entire transaction process, forming a comprehensive trading market. Suppliers offer advance-cash-credit payments to retailers, while retailers provide customers with cash-credit payment options. In the current health-conscious consumer market, purchasing decisions are influenced not only by commodity prices but also by the freshness of products, particularly perishable goods. Growing awareness of climate change and the advent of carbon emission policies have raised concerns about the environmental costs of business transactions. This study focuses on perishable products whose demand is influenced by both price and freshness. It explores the adoption of various payment methods by suppliers and retailers, as well as the impact of carbon emission cap-and-trade policies or carbon tax policies on management and pricing strategies. Suitable inventory models are established to determine the optimal replenishment and pricing strategies for maximizing the current value of total profit. We illustrate that the current value of total profit demonstrates joint concavity concerning both the selling price and the replenishment time. Finally, we verify the proposed models using numerical examples and present the findings of sensitivity analyses. The findings of this study yield several valuable insights for inventory management of perishable goods. Full article
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
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Review

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26 pages, 1990 KiB  
Review
One-Rank Linear Transformations and Fejer-Type Methods: An Overview
by Volodymyr Semenov, Petro Stetsyuk, Viktor Stovba and José Manuel Velarde Cantú
Mathematics 2024, 12(10), 1527; https://doi.org/10.3390/math12101527 - 14 May 2024
Viewed by 802
Abstract
Subgradient methods are frequently used for optimization problems. However, subgradient techniques are characterized by slow convergence for minimizing ravine convex functions. To accelerate subgradient methods, special linear non-orthogonal transformations of the original space are used. This paper provides an overview of these transformations [...] Read more.
Subgradient methods are frequently used for optimization problems. However, subgradient techniques are characterized by slow convergence for minimizing ravine convex functions. To accelerate subgradient methods, special linear non-orthogonal transformations of the original space are used. This paper provides an overview of these transformations based on Shor’s original idea. Two one-rank linear transformations of Euclidean space are considered. These simple transformations form the basis of variable metric methods for convex minimization that have a natural geometric interpretation in the transformed space. Along with the space transformation, a search direction and a corresponding step size must be defined. Subgradient Fejer-type methods are analyzed to minimize convex functions, and Polyak step size is used for problems with a known optimal objective value. Convergence theorems are provided together with the results of numerical experiments. Directions for future research are discussed. Full article
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
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