Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.8
Journals
Algorithms
Special Issues
Scheduling: Algorithms and Real-World Applications
share announcement

Scheduling: Algorithms and Real-World Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 6324

Special Issue Editor

Dr. Mohammad Rohaninejad

E-Mail Website
Guest Editor
Czech Institute of Informatics Robotics and Cybernetics, Czech Technical University in Prague, 16636 Prague, Czech Republic
Interests: production planning and scheduling

Special Issue Information

Dear Colleagues,

We are thrilled to invite you to contribute to our upcoming Special Issue on "Scheduling: Algorithms and Real-World Applications" We actively seek the latest advancements in scheduling algorithms and their diverse applications, encouraging researchers, academicians, and professionals to share their expertise and insights, enriching our understanding of this evolving field. The Special Issue aims to bring together cutting-edge research, focusing on both theoretical and practical applications of scheduling algorithms. We welcome contributions exploring innovative scheduling methodologies to address the varied demands of modern industries and services, including novel scheduling algorithms, metaheuristic and optimization techniques, and machine learning approaches for scheduling. This Special Issue extends scope beyond traditional scheduling domains like manufacturing, energy markets, healthcare, transportation, and logistics,  to emerging technologies and realms such as cloud manufacturing, data center scheduling, adaptive scheduling, real-time scheduling for cyber-physical systems, digital twin-based scheduling, scheduling in the Internet of Things (IoT), quantum computing, and blockchain. Join us in shaping the discourse on "Scheduling: Algorithms and Real-World Applications" by submitting your groundbreaking research. Together, let us deepen our understanding of scheduling algorithms and their new practical applications. Your insights will play a crucial role in advancing the conversation in this field.

We look forward to receiving your submissions and appreciate your active involvement in shaping this Special Issue.

Dr. Mohammad Rohaninejad
Guest Editor

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. Algorithms 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 1600 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

  • metaheuristics algorithms
  • exact algorithms
  • heuristic methods for scheduling
  • machine learning in scheduling
  • hybrid optimization techniques for scheduling
  • multi-objective scheduling
  • agent-based scheduling
  • robust scheduling
  • data-driven scheduling
  • fuzzy logic in scheduling
  • dynamic scheduling algorithms
  • predictive and reactive scheduling
  • swarm intelligence in scheduling
  • decision support systems for scheduling
  • real-time scheduling
  • constraint-based scheduling
  • game theory for scheduling
  • network-based scheduling
  • scheduling problems in manufacturing, energy markets, healthcare
  • transportation, and logistics, supply chain, etc.
  • scheduling problems in emerging technologies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

20 pages, 3280 KiB  
Article
A Robust Heuristics for the Online Job Shop Scheduling Problem
by Hugo Zupan, Niko Herakovič and Janez Žerovnik
Algorithms 2024, 17(12), 568; https://doi.org/10.3390/a17120568 - 12 Dec 2024
Viewed by 895
Abstract
The job shop scheduling problem (JSSP) is a popular NP-hard problem in combinatorial optimization, due to its theoretical appeal and its importance in applications. In practical applications, the online version is much closer to the needs of smart manufacturing in Industry 4.0 and [...] Read more.
The job shop scheduling problem (JSSP) is a popular NP-hard problem in combinatorial optimization, due to its theoretical appeal and its importance in applications. In practical applications, the online version is much closer to the needs of smart manufacturing in Industry 4.0 and 5.0. Here, the online version of the job shop scheduling problem is solved by a heuristics that governs local queues at the machines. This enables a distributed implementation, i.e., a digital twin can be maintained by local processors which can result in high speed real time operation. The heuristics at the level of probabilistic rules for running the local queues is experimentally shown to provide the solutions of quality that is within acceptable approximation ratios to the best known solutions obtained by the best online algorithms. The probabilistic rule defines a model which is not unlike the spin glass models that are closely related to quantum computing. Major advances of the approach are the inherent parallelism and its robustness, promising natural and likely successful application to other variations of JSSP. Experimental results show that the heuristics, although designed for solving the online version, can provide near-optimal and often even optimal solutions for many benchmark instances of the offline version of JSSP. It is also demonstrated that the best solutions of the new heuristics clearly improve over the results obtained by heuristics based on standard dispatching rules. Of course, there is a trade-off between better computational time and the quality of the results in terms of makespan criteria. Full article
(This article belongs to the Special Issue Scheduling: Algorithms and Real-World Applications)
►▼ Show Figures

Figure 1

28 pages, 1897 KiB  
Article
Bi-Objective, Dynamic, Multiprocessor Open-Shop Scheduling: A Hybrid Scatter Search–Tabu Search Approach
by Tamer F. Abdelmaguid 
Algorithms 2024, 17(8), 371; https://doi.org/10.3390/a17080371 - 21 Aug 2024
Cited by 1 | Viewed by 963
Abstract
This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly [...] Read more.
This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines’ utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfill the requirements of real-life circumstances. Previous attempts have included the development of an exact algorithm and two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective gray wolf optimizer (MOGWO). The exact algorithm is limited to small-sized instances; meanwhile, NSGA-II was found to produce better results compared to MOGWO in both small- and large-sized test instances. The proposed MOSS in this paper attempts to provide more efficient non-dominated solutions for the studied Bi-DMOSP. This is achievable via its hybridization with a novel, bi-objective tabu search approach that utilizes a set of efficient neighborhood search functions. Parameter tuning experiments are conducted first using a subset of small-sized benchmark instances for which the optimal Pareto front solutions are known. Then, detailed computational experiments on small- and large-sized instances are conducted. Comparisons with the previously developed NSGA-II metaheuristic demonstrate the superiority of the proposed MOSS approach for small-sized instances. For large-sized instances, it proves its capability of producing competitive results for instances with low and medium density. Full article
(This article belongs to the Special Issue Scheduling: Algorithms and Real-World Applications)
►▼ Show Figures

Figure 1

25 pages, 3881 KiB  
Article
Logical Execution Time and Time-Division Multiple Access in Multicore Embedded Systems: A Case Study
by Carlos-Antonio Mosqueda-Arvizu, Julio-Alejandro Romero-González, Diana-Margarita Córdova-Esparza, Juan Terven, Ricardo Chaparro-Sánchez and Juvenal Rodríguez-Reséndiz
Algorithms 2024, 17(7), 294; https://doi.org/10.3390/a17070294 - 5 Jul 2024
Viewed by 1234
Abstract
The automotive industry has recently adopted multicore processors and microcontrollers to meet the requirements of new features, such as autonomous driving, and comply with the latest safety standards. However, inter-core communication poses a challenge in ensuring real-time requirements such as time determinism and [...] Read more.
The automotive industry has recently adopted multicore processors and microcontrollers to meet the requirements of new features, such as autonomous driving, and comply with the latest safety standards. However, inter-core communication poses a challenge in ensuring real-time requirements such as time determinism and low latencies. Concurrent access to shared buffers makes predicting the flow of data difficult, leading to decreased algorithm performance. This study explores the integration of Logical Execution Time (LET) and Time-Division Multiple Access (TDMA) models in multicore embedded systems to address the challenges in inter-core communication by synchronizing read/write operations across different cores, significantly reducing latency variability and improving system predictability and consistency. Experimental results demonstrate that this integrated approach eliminates data loss and maintains fixed operation rates, achieving a consistent latency of 11 ms. The LET-TDMA method reduces latency variability to approximately 1 ms, maintaining a maximum delay of 1.002 ms and a minimum delay of 1.001 ms, compared to the variability in the LET-only method, which ranged from 3.2846 ms to 8.9257 ms for different configurations. Full article
(This article belongs to the Special Issue Scheduling: Algorithms and Real-World Applications)
►▼ Show Figures

Figure 1

Review

Jump to: Research

47 pages, 2834 KiB  
Review
Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms
by Noor A. Rashed, Yossra H. Ali and Tarik A. Rashid
Algorithms 2024, 17(9), 416; https://doi.org/10.3390/a17090416 - 19 Sep 2024
Cited by 1 | Viewed by 2332
Abstract
The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative [...] Read more.
The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000–2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization. Full article
(This article belongs to the Special Issue Scheduling: Algorithms and Real-World Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop