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Applied Sciences
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Technologies, Algorithms and Applications for Planning, Scheduling and Optimization
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Technologies, Algorithms and Applications for Planning, Scheduling and Optimization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 8928

Special Issue Editor

Prof. Dr. Richard C. Millham

E-Mail Website
Guest Editor
Faculty of Accounting and Informatics, Durban University of Technology, Durban 4000, South Africa
Interests: software and data evolution; cloud computing; big data; bio-inspired algorithms; IOT

Special Issue Information

Dear Colleagues,

Planning and scheduling play an integral role in our world in helping us to achieve our objectives with minimal cost in terms of resources and time. This includes planning delivery routes to maximize the number of deliveries within the shortest possible route and time period as well as scheduling manufacturing activities such that the correct material arrives at the precise moment it’s needed (just-in-time manufacturing). Just-in-time manufacturing involves a range of support technologies, such as Internet-of-Things (IOT) sensors to track the flow of assets, data streaming, real-time big data analytics, etc. Furthermore, a variety of solutions can be applied to obtain optimal or approximate solutions for a diverse range of problems in planning and scheduling processes, including conventional, meta-heuristic and other approaches. Research on such applications, or their supporting technologies, are welcome in this Special Issue.

We welcome you to submit your most recent work in the fields of scheduling and optimization to this Special Issue, "Technologies, Algorithms and Applications for Planning, Scheduling and Optimization", in Applied Sciences. Researchers from both industry and academia are warmly invited to submit either theoretical or practical research.

Prof. Dr. Richard C. Millham
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. Applied Sciences 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 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

  • logistical planning
  • just-in-time manufacturing
  • route optimization
  • IOT
  • data streaming
  • big data analytics
  • meta-heuristics

Published Papers (7 papers)

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Research

19 pages, 1636 KiB  
Article
An Efficiency Boost for Genetic Algorithms: Initializing the GA with the Iterative Approximate Method for Optimizing the Traveling Salesman Problem—Experimental Insights
by Esra’a Alkafaween, Ahmad Hassanat, Ehab Essa and Samir Elmougy
Appl. Sci. 2024, 14(8), 3151; https://doi.org/10.3390/app14083151 - 09 Apr 2024
Viewed by 341
Abstract
The genetic algorithm (GA) is a well-known metaheuristic approach for dealing with complex problems with a wide search space. In genetic algorithms (GAs), the quality of individuals in the initial population is important in determining the final optimal solution. The classic GA using [...] Read more.
The genetic algorithm (GA) is a well-known metaheuristic approach for dealing with complex problems with a wide search space. In genetic algorithms (GAs), the quality of individuals in the initial population is important in determining the final optimal solution. The classic GA using the random population seeding technique is effective and straightforward, but the generated population may contain individuals with low fitness, delaying convergence to the best solution. On the other side, heuristic population seeding strategies provide the advantages of producing individuals with high fitness and encouraging rapid convergence to the optimal solution. Using background information on the problem being solved, researchers have developed several population seeding approaches. In this paper, to enhance the genetic algorithm efficiency, we propose a new method for the initial population seeding based on a greedy approach. The proposed method starts by adding four extreme cities to the route, creating a loop, and then adding each city to the route through a greedy strategy that calculates the cost of adding every city to different locations along the route. This method identifies the best position to place the city as well as the best city to add. Employing local constant permutations improves the resultant route even more. Together with the suggested approach, we examine the GA’s effectiveness while employing conventional population seeding methods like nearest-neighbor, regression-based, and random seeding. Utilizing some of the well-known Traveling Salesman Problem (TSP) examples from the TSPLIB, the standard library for TSPs, tests were conducted. In terms of the error rate, average convergence, and time, the experimental results demonstrate that the GA that employs the suggested population seeding technique performs better than other GAs that use conventional population seeding strategies. Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
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18 pages, 929 KiB  
Article
Round-Based Mechanism and Job Packing with Model-Similarity-Based Policy for Scheduling DL Training in GPU Cluster
by Panissara Thanapol, Kittichai Lavangnananda, Franck Leprévost, Arnaud Glad, Julien Schleich and Pascal Bouvry
Appl. Sci. 2024, 14(6), 2349; https://doi.org/10.3390/app14062349 - 11 Mar 2024
Viewed by 420
Abstract
Graphics Processing Units (GPUs) are employed for their parallel processing capabilities, which are essential to train deep learning (DL) models with large datasets within a reasonable time. However, the diverse GPU architectures exhibit variability in training performance depending on DL models. Furthermore, factors [...] Read more.
Graphics Processing Units (GPUs) are employed for their parallel processing capabilities, which are essential to train deep learning (DL) models with large datasets within a reasonable time. However, the diverse GPU architectures exhibit variability in training performance depending on DL models. Furthermore, factors such as the number of GPUs for distributed training and batch size significantly impact training efficiency. Addressing the variability in training performance and accounting for these influential factors are critical for optimising resource usage. This paper presents a scheduling policy for DL training tasks in a heterogeneous GPU cluster. It builds upon a model-similarity-based scheduling policy by implementing a round-based mechanism and job packing. The round-based mechanism allows the scheduler to adjust its scheduling decisions periodically, whereas job packing optimises GPU utilisation by fitting additional jobs into a GPU that trains a small model. Results show that implementing a round-based mechanism reduces the makespan by approximately 29%, compared to the scenario without it. Additionally, integrating job packing further decreases the makespan by 5%. Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
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22 pages, 2023 KiB  
Article
Construction Schedule versus Various Constraints and Risks
by Paulina Kostrzewa-Demczuk
Appl. Sci. 2024, 14(1), 196; https://doi.org/10.3390/app14010196 - 25 Dec 2023
Viewed by 539
Abstract
The organization and planning of construction works are difficult issues due to the complexity, numerous limitations, uncertainty and risks associated with them. Construction planning is usually based on deterministic data. However, numerous studies and analyses of real cases show that a different computational [...] Read more.
The organization and planning of construction works are difficult issues due to the complexity, numerous limitations, uncertainty and risks associated with them. Construction planning is usually based on deterministic data. However, numerous studies and analyses of real cases show that a different computational approach is needed—one based on probabilistic data. The computational algorithms of the Time Coupling Method make it possible to introduce probabilistic data generated in the Multivariate Method of Statistical Models (MMSM) and via standard deviations. As a result, a new methodology was created, the Probabilistic Time Coupling Method (PTCM), through which it is possible to obtain a very good forecast of the investment implementation time compared to its real time. The paper presents theoretical considerations, computational schemes and validation exercises of this new method—known as the PTCM. The computational results of the PTCM (with a mapping accuracy prediction of 99%) confirm the effectiveness of the method. The computational algorithms of the PTCM enable the creation of a computational application based on a well-known program, e.g., Microsoft Excel, thanks to which the method can be quickly disseminated in the planning environment and widely used. Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
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23 pages, 2009 KiB  
Article
Application of Ant Colony Optimization Computing to a Recommended Travel Itinerary Planning System with Repeatedly Used Nodes
by Shuo-Tsung Chen, Tsung-Hsien Wu, Ren-Jie Ye, Liang-Ching Lee, Wen-Yu Huang, Yi-Hong Lin and Bo-Yao Wang
Appl. Sci. 2023, 13(24), 13221; https://doi.org/10.3390/app132413221 - 13 Dec 2023
Viewed by 762
Abstract
Recommended travel itinerary planning is an important issue in travel platforms or travel systems. Most research focuses on minimizing the time spent traveling between attractions or the cost of attractions. This study makes four contributions to recommended travel itinerary planning in travel platforms [...] Read more.
Recommended travel itinerary planning is an important issue in travel platforms or travel systems. Most research focuses on minimizing the time spent traveling between attractions or the cost of attractions. This study makes four contributions to recommended travel itinerary planning in travel platforms or travel systems. The first contribution is to consider recommended travel itinerary planning which can account for attractions, restaurants, and hotels at the same time. Due to the fact that restaurants and hotels can be repeated on the recommended itinerary, the second contribution is to propose an improved ant colony system (ACS) with repeatedly used nodes for the optimization of travel itinerary planning. In the third contribution, the proposed improved ACS allows repeated use of certain nodes without falling into a pattern of infinitely hovering within a certain interval or over certain nodes, through the interactive operation of a Watch List and a Tabu List. In the fourth contribution, the user satisfaction calculation for restaurants and hotels is also added to the travel itinerary planning in order to fully meet the needs of tourists. The experimental results verify the efficiency of the proposed improved ACS. Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
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26 pages, 2464 KiB  
Article
Solving the Distributed Permutation Flow-Shop Scheduling Problem Using Constrained Programming
by Christos Gogos
Appl. Sci. 2023, 13(23), 12562; https://doi.org/10.3390/app132312562 - 21 Nov 2023
Viewed by 878
Abstract
The permutation flow-shop scheduling problem is a classical problem in scheduling that aims at identifying the optimal sequence of jobs that should be processed in a number of machines in an effort to minimize makespan or some other performance criterion. The distributed permutation [...] Read more.
The permutation flow-shop scheduling problem is a classical problem in scheduling that aims at identifying the optimal sequence of jobs that should be processed in a number of machines in an effort to minimize makespan or some other performance criterion. The distributed permutation flow-shop scheduling problem adds multiple factories where copies of the machines exist and asks for minimizing the makespan on the longest-running location. In this paper, the problem is approached using Constraint Programming and its specialized scheduling features, such as interval variables and non-overlap constraints, while a novel heuristic is proposed for computing lower bounds. Two constraint programming models are proposed: one that solves the Distributed Permutation Flow-shop Scheduling problem, and another one that drops the constraint of processing jobs under the same order for all machines of each factory. The experiments use an extended public dataset of problem instances to validate the approach’s effectiveness. In the process, optimality is proved for many problem instances known in the literature but has yet to be proven optimal. Moreover, a high speed of reaching optimal solutions is achieved for many problems, even with moderate big sizes (e.g., seven factories, 20 machines, and 20 jobs). The critical role that the number of jobs plays in the complexity of the problem is identified and discussed. In conclusion, this paper demonstrates the great benefits of scheduling problems that stem from using state-of-the-art constraint programming solvers and models that capture the problem tightly. Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
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24 pages, 919 KiB  
Article
An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
by Fakhar Uddin, Naveed Riaz, Abdul Manan, Imran Mahmood, Oh-Young Song, Arif Jamal Malik and Aaqif Afzaal Abbasi
Appl. Sci. 2023, 13(12), 7339; https://doi.org/10.3390/app13127339 - 20 Jun 2023
Cited by 3 | Viewed by 4000
Abstract
The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation [...] Read more.
The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation not only improves the overall profitability of a logistic centre but also reduces greenhouse gas emissions by minimising the distance travelled. In this article, we propose a simple and improved heuristic algorithm named 2-Opt++, which solves symmetric TSP problems using an enhanced 2-Opt local search technique, to generate better results. As with 2-Opt, our proposed method can also be applied to the Vehicle Routing Problem (VRP), with minor modifications. We have compared our technique with six existing algorithms, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Furthermore, to allow for the complexity of larger TSP instances, we have used a graph compression/candidate list technique that helps in reducing the computational complexity and time. The comprehensive empirical evaluation carried out for this research work shows the efficacy of the 2-Opt++ algorithm as it outperforms the other well-known algorithms in terms of the error margin, execution time, and time of convergence. Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
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22 pages, 5578 KiB  
Article
Modelling and Optimization of Personalized Scenic Tourism Routes Based on Urgency
by Xiangrong Xu, Lei Wang, Shuo Zhang, Wei Li and Qiaoyong Jiang
Appl. Sci. 2023, 13(4), 2030; https://doi.org/10.3390/app13042030 - 04 Feb 2023
Cited by 3 | Viewed by 1090
Abstract
Traditional route planning methods usually plan the “fastest” or “lowest cost” travel route for users with the goal of finding the shortest path or the lowest cost, but this method cannot meet the needs of tourism users for personalized and multifunctional travel routes. [...] Read more.
Traditional route planning methods usually plan the “fastest” or “lowest cost” travel route for users with the goal of finding the shortest path or the lowest cost, but this method cannot meet the needs of tourism users for personalized and multifunctional travel routes. Given this phenomenon, this paper proposes a personalized route planning model based on urgency. First, the model uses the visitor’s historical tourism data and public road network data to extract their preferences, POI (point of interest) relationships, edge scenic values and other information. Then, the planned route function is determined according to the urgency value, which provides users with travel routes that accommodate their interest preferences and urgency. Finally, the improved genetic algorithm based on gene replacement and gene splicing operators is used to carry out numerical experiments on the Xi’an and Wuhan road network datasets. The experimental results show that the proposed algorithm is not only capable of planning routes with different functions for diverse users but also performs personalized route planning according to their preferences. Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
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