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Axioms
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Applied Mathematics, Intelligence and Operations Research
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Applied Mathematics, Intelligence and Operations Research

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 15951

Special Issue Editors

Dr. Omar Rojas

E-Mail Website
Guest Editor
Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Interests: operations research; applied maths; financial time series; adaptive systems; risk
Special Issues, Collections and Topics in MDPI journals
Dr. Guillermo Sosa

E-Mail Website
Guest Editor
Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico
Interests: applied math; entropy; heuristics; operations research; security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Axioms is now welcoming submissions to the Special Issue “Applied Mathematics, Intelligence and Operations Research”. This Special Issue aims to publish recent developments in the fields of applied mathematics, intelligence and operations research. Both empirical and theoretical contributions are welcome. Submissions should aim to enrich the state of the art and understanding of current research problems, especially those derived from real-world applications.

Papers presenting new research ideas that might improve the understanding of important scientific questions are also welcome. 

Research topics include, but are not limited to:

  • 90-XX: operations research: mathematical programming;
  • 49-XX: calculus of variations and optimal control; optimization;
  • 69-XX: general applied mathematics.

We look forward to receiving your submissions.

Dr. Omar Rojas
Dr. Guillermo Sosa
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. Axioms is an international peer-reviewed open access monthly 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 2400 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

  • applied mathematics
  • intelligence systems
  • operations research
  • heuristics
  • optimization
  • decision-making processes
  • industrial mathematics

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

Published Papers (4 papers)

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Research

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22 pages, 334 KiB  
Article
Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines Environment
by Marko Đurasević and Domagoj Jakobović
Axioms 2024, 13(1), 37; https://doi.org/10.3390/axioms13010037 - 5 Jan 2024
Viewed by 1339
Abstract
Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). [...] Read more.
Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). Various approaches have been used to improve the results of automatically generated DRs, with ensemble learning being one of the best-known. The goal of ensemble learning is to create sets of automatically designed DRs that perform better together. One of the main problems in ensemble learning is the selection of DRs to form the ensemble. To this end, various ensemble construction methods have been proposed over the years. However, these methods are quite computationally intensive and require a lot of computation time to obtain good ensembles. Therefore, in this study, we propose several simple heuristic ensemble construction methods that can be used to construct ensembles quite efficiently and without the need to evaluate their performance. The proposed methods construct the ensembles solely based on certain properties of the individual DRs used for their construction. The experimental study shows that some of the proposed heuristic construction methods perform better than more complex state-of-the-art approaches for constructing ensembles. Full article
(This article belongs to the Special Issue Applied Mathematics, Intelligence and Operations Research)
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16 pages, 331 KiB  
Article
Improved Bound of Four Moment Theorem and Its Application to Orthogonal Polynomials Associated with Laws
by Yoon-Tae Kim and Hyun-Suk Park
Axioms 2023, 12(12), 1092; https://doi.org/10.3390/axioms12121092 - 29 Nov 2023
Viewed by 1036
Abstract
In the case where the square of an eigenfunction F with respect to an eigenvalue of Markov generator L can be expressed as a sum of eigenfunctions, we find the largest number excluding zero among the eigenvalues in the terms of the sum. [...] Read more.
In the case where the square of an eigenfunction F with respect to an eigenvalue of Markov generator L can be expressed as a sum of eigenfunctions, we find the largest number excluding zero among the eigenvalues in the terms of the sum. Using this number, we obtain an improved bound of the fourth moment theorem for Markov diffusion generators. To see how this number depends on an improved bound, we give some examples of eigenfunctions of the diffusion generators L such as Ornstein–Uhlenbeck, Jacobi, and Romanovski–Routh. Full article
(This article belongs to the Special Issue Applied Mathematics, Intelligence and Operations Research)
28 pages, 2751 KiB  
Article
Digital Coupon Promotion and Inventory Strategies of Omnichannel Brands
by Yue Zhang and Xiaojian Hu
Axioms 2023, 12(1), 29; https://doi.org/10.3390/axioms12010029 - 26 Dec 2022
Cited by 1 | Viewed by 2069
Abstract
This paper investigates when an omnichannel brand should offer digital coupons in the online and buy-online-and-pick-up-in-store (BOPS) channels and, if so, the joint decision of coupon face value and store inventory. The impact of a digital coupon promotion on store inventory is also [...] Read more.
This paper investigates when an omnichannel brand should offer digital coupons in the online and buy-online-and-pick-up-in-store (BOPS) channels and, if so, the joint decision of coupon face value and store inventory. The impact of a digital coupon promotion on store inventory is also explored. Two scenarios are considered, one where consumers’ costs in the online and store channels are homogeneous and another in which they are heterogeneous, and two newsvendor models, with and without a coupon promotion, are constructed under each scenario. The results show that the issuance of coupons improves the omnichannel brand’s profit when the price is high and the coefficient of the difference in valuation between two types of consumers is low in the homogeneous scenario. In the heterogeneous scenario, the brand prefers the coupon promotion when the price is high or moderate and the coefficient of the difference in valuation between two types of consumers is high. In addition, offering a coupon promotion yields a higher store inventory in most cases. However, store inventory is decreased in some special cases in the homogeneous scenario. Moreover, an increased hassle cost in the BOPS channel significantly lowers the offline demand and profit increase from a digital coupon promotion. Furthermore, a coupon promotion is more likely to benefit both the brand and consumers as the cross-selling revenue increases. These results provide guidance for omnichannel brands to implement coupon promotions and adjust store inventory with stochastic demand. Full article
(This article belongs to the Special Issue Applied Mathematics, Intelligence and Operations Research)
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Review

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30 pages, 8329 KiB  
Review
A Review of Optimization Studies for System Appointment Scheduling
by Tiantian Niu, Bingyin Lei, Li Guo, Shu Fang, Qihang Li, Bingrui Gao, Li Yang and Kaiye Gao
Axioms 2024, 13(1), 16; https://doi.org/10.3390/axioms13010016 - 25 Dec 2023
Cited by 3 | Viewed by 9880
Abstract
In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation [...] Read more.
In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research. Full article
(This article belongs to the Special Issue Applied Mathematics, Intelligence and Operations Research)
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