Symmetry in Computing Algorithms and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1003

Special Issue Editors

Dr. Lei Yue
E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510000, China
Interests: intelligent manufacturing system; digital modeling and simulation; digital twin in manufacturing operations; production line balancing; smart optimization algorithms
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China
Interests: mathematical modeling; adaptive robust control; vehicle dynamics

Special Issue Information

Dear Colleagues,

Symmetry plays a crucial role in computing algorithms and their applications, contributing to efficiency, aesthetics, and problem-solving in various domains. In algorithm design, symmetry often leads to elegant solutions that simplify complex problems, since it can help us find patterns and regularities in data and improve the efficiency and accuracy of algorithms. For example, sorting algorithms like Quicksort and Mergesort exploit symmetry to divide and conquer, reducing time complexity. Symmetric data structures, such as binary trees and balanced search trees, ensure efficient data retrieval and insertion. In addition to sorting algorithms, symmetry also appears in other types of algorithms. For example, some machine learning algorithms use symmetry to find patterns in data and classify or cluster similar objects. Symmetry can also help us find regularities in regular expressions and improve the efficiency of pattern-matching algorithms. Moreover, symmetry finds applications in artificial intelligence and machine learning, where it assists in feature extraction, dimensionality reduction, and data preprocessing. Recognizing symmetries in datasets can improve classification accuracy and reduce computational complexity. In summary, symmetry is a powerful concept in computing, with widespread applications across algorithms and various fields. In this Special Issue, we invite submissions focusing on novel models that can predict, detect, and adapt to symmetric solutions in complex mathematic programming. We particularly encourage research that explores the practical applications of these models and algorithms in solving industrial problems.

The focus of this Special Issue is to continue to advance research on topics relating to the Symmetry in Computing Algorithms and Applications. Topics that are invited for submission include (but are not limited to):

  • Symmetries of complex manufacturing systems;
  • Artificial intelligence (AI) in production scheduling;
  • Fuzzy computing;
  • Data analytics for manufacturing;
  • Cloud computing for manufacturing;
  • Predictive maintenance;
  • Real-time monitoring and control;
  • Supply chain optimization;
  • Computing applications;
  • Smart decision support for industry.

Dr. Lei Yue
Dr. Jinhua Zhang
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. Symmetry 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

  • symmetries of complex manufacturing systems
  • Artificial Intelligence (AI) in production scheduling
  • fuzzy computing
  • data analytics for manufacturing
  • cloud computing for manufacturing
  • predictive maintenance
  • real-time monitoring and control
  • supply chain optimization
  • computing applications
  • smart decision support for industry

Published Papers (1 paper)

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Research

21 pages, 2144 KiB  
Article
Scheduling Optimization of Compound Operations in Autonomous Vehicle Storage and Retrieval System
Symmetry 2024, 16(2), 168; https://doi.org/10.3390/sym16020168 - 31 Jan 2024
Viewed by 503
Abstract
The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The [...] Read more.
The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The system mainly completes operations through elevators and multiple rail-guided vehicles (RGVs). This paper focuses on the scheduling optimization of compound operations in the AVS/RS to improve system performance. Compound operations involve the coordinated execution of both single-command and double-command operations. A mathematical model with compound operations was proposed and effectively decomposed into a horizontal component for RGVs and a vertical counterpart for the elevator, which can represent the operations of one elevator cooperating with multiple RGVs. The goal of this model was to minimize the makespan for compound operations and to determine the optimal operation sequence and path for RGVs. An improved discrete particle swarm optimization (DPSO) algorithm called AGDPSO was proposed to solve the model. The algorithm combines DPSO and a genetic algorithm in an adaptive manner to prevent the algorithm from falling into local optima and relying solely on the initial solution. Through rigorous optimization, optimal parameters for the algorithm were identified. When assessing the performance of our improved algorithm against various counterparts, considering different task durations and racking configurations, our results showed that AGDPSO outperformed the alternatives, proving its effectiveness in enhancing system efficiency for the model. The findings of this study not only contribute to the optimization of AVS/RS but also offer valuable insights for designing more efficient warehouses. By streamlining scheduling, improving operations, and leveraging advanced optimization techniques, we can create a more robust and effective storage and retrieval system. Full article
(This article belongs to the Special Issue Symmetry in Computing Algorithms and Applications)
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