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Axioms
Special Issues
Computational Optimization and Applications
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Computational Optimization and Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 7125

Special Issue Editors

Dr. Yao-Huei Huang

E-Mail Website
Guest Editor
Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Interests: linear programming; mixed-integer programming
Dr. Feng-Jang Hwang

E-Mail Website
Guest Editor
School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: scheduling; linear programming; integer programming; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Hao-Chun Lu

E-Mail Website
Guest Editor
College of Management, Graduate Institute of Business and Management, Chang Gung University, Taoyuan 333, Taiwan
Interests: linear programming; integer programming; machine learning

Special Issue Information

Dear Colleagues,

We have the intention of launching a Special Issue of Axioms. The central topic in the Special Issue will be “Computational Optimization and Applications”. We would like to provide an opportunity to showcase recent developments in the many methods of both theoretical and practical studies in Mathematics, which are related to mixed integer linear programming theory and/or its extensions and generalizations. Among the topics that this Special Issue will address, we may consider deterministic method, robust optimization, stochastic optimization, theoretical modelling, designed algorithm, etc.

Needless to say, this Special Issue is open to receiving further ideas, apart from the aforementioned topics.

In the hopes that this initiative is of interest, we encourage you to submit your current research to be included in this Special Issue.

Dr. Yao-Huei Huang
Dr. Feng-Jang Hwang
Dr. Hao-Chun Lu
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

  • linear programming
  • mixed-integer programming
  • mixed-integer non-linear programming
  • scheduling
  • stochastic optimization
  • mathematical modelling
  • robust optimization
  • deterministic method
  • designed algorithm

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

Published Papers (3 papers)

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Research

26 pages, 8742 KiB  
Article
Multi-Objective Golden Flower Optimization Algorithm for Sustainable Reconfiguration of Power Distribution Network with Decentralized Generation
by Dhivya Swaminathan and Arul Rajagopalan
Axioms 2023, 12(1), 70; https://doi.org/10.3390/axioms12010070 - 9 Jan 2023
Cited by 5 | Viewed by 2218
Abstract
This paper provides a meta-heuristic hybridized version called multi-objective golden flower pollination algorithm (MOGFPA) as the best method for choosing the optimal reconfiguration for distribution networks (DNs) in order to reduce power losses (PLs). Aside from PLs, another parameter is considered: the load [...] Read more.
This paper provides a meta-heuristic hybridized version called multi-objective golden flower pollination algorithm (MOGFPA) as the best method for choosing the optimal reconfiguration for distribution networks (DNs) in order to reduce power losses (PLs). Aside from PLs, another parameter is considered: the load balance index (LBI). The expression for the LBI is stated using real and reactive indices. It makes the optimal distributed generation (DG) placement and DN routing of the multi-objective (MO) problem have PLs and the LBI as the main parameters that need to be optimized. For that purpose, the MOGFPA is proposed in this paper. The MOGFPA consists of a golden search (GS) and tangent flight with Pareto distribution that only needs a few tuning parameters. Therefore, it is simple to alter these parameters to reach the best values compared to other existing methodologies. Its performance is predicted using different case studies on multiple test bus systems, namely the IEEE systems such as 33, 69, 119, and Indian 52 bus. Through simulation outcomes, the MOGFPA computes the optimum distribution of DG units and reconfigures the DNs with the aim of minimal PLs and LBI. Furthermore, another state-of-the-art technology and comparing convergence charts provide optimal outputs in less time, with minimum iterations. Full article
(This article belongs to the Special Issue Computational Optimization and Applications)
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23 pages, 3103 KiB  
Article
An Improved Elephant Herding Optimization for Energy-Saving Assembly Job Shop Scheduling Problem with Transportation Times
by Tianhua Jiang, Lu Liu, Huiqi Zhu and Yaping Li
Axioms 2022, 11(10), 561; https://doi.org/10.3390/axioms11100561 - 16 Oct 2022
Cited by 1 | Viewed by 1910
Abstract
The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) [...] Read more.
The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) adds the additional consideration of hierarchical precedence constraints between different jobs of each final product. This paper focuses on developing a methodology for an energy-saving assembly job shop scheduling problem with job transportation times. Firstly, a mathematical model is constructed with the objective of minimizing total energy consumption. Secondly, an improved elephant herding optimization (IEHO) is proposed by considering the problem’s characteristics. Finally, thirty-two different instances are designed to verify the performance of the proposed algorithm. Computational results and statistical data demonstrate that the IEHO has advantages over other algorithms in terms of the solving accuracy for the considered problem. Full article
(This article belongs to the Special Issue Computational Optimization and Applications)
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80 pages, 674 KiB  
Article
Extended Form of Robust Solutions for Uncertain Continuous-Time Linear Programming Problems with Time-Dependent Matrices
by Hsien-Chung Wu
Axioms 2022, 11(5), 211; https://doi.org/10.3390/axioms11050211 - 1 May 2022
Viewed by 1814
Abstract
An extended form of robust continuous-time linear programming problem with time-dependent matrices is formulated in this paper. This complicated problem is studied theoretically in this paper. We also design a computational procedure to solve this problem numerically. The desired data that appeared in [...] Read more.
An extended form of robust continuous-time linear programming problem with time-dependent matrices is formulated in this paper. This complicated problem is studied theoretically in this paper. We also design a computational procedure to solve this problem numerically. The desired data that appeared in the problem are considered to be uncertain quantities, which are treated according to the concept of robust optimization. A discretization problem of the robust counterpart is formulated and solved to obtain the ϵ-optimal solutions. Full article
(This article belongs to the Special Issue Computational Optimization and Applications)
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