Applied Sciences

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25 pages, 5036 KiB

Open AccessArticle

Joint Optimization Method for Preventive Maintenance and Train Scheduling of Subway Vehicles Based on a Spatiotemporal Network Graph

by Chuanzhen Liu, Zhongwei Xu, Meng Mei and Wenqing Li

Appl. Sci. 2025, 15(8), 4138; https://doi.org/10.3390/app15084138 - 9 Apr 2025

Viewed by 176

Abstract

To address the challenges posed by the interdependence between subway vehicle scheduling and maintenance planning, which complicates joint optimization modeling and solution processes, this study proposes a spatiotemporal network-based joint optimization method for subway vehicle preventive maintenance and scheduling. First, based on spatiotemporal [...] Read more.

To address the challenges posed by the interdependence between subway vehicle scheduling and maintenance planning, which complicates joint optimization modeling and solution processes, this study proposes a spatiotemporal network-based joint optimization method for subway vehicle preventive maintenance and scheduling. First, based on spatiotemporal network theory, the transition process between train operation scheduling and preventive maintenance states is analyzed, and a spatiotemporal state network graph is constructed to represent the temporal and spatial transitions of subway vehicles throughout the planning period. The vehicle’s operational workflow is represented as a path within this network. Next, leveraging the generated spatiotemporal network path set, a joint optimization model for preventive maintenance and scheduling is formulated, integrating optimization objectives and constraints to achieve coordinated optimization. Finally, an improved genetic algorithm is employed to solve the model and determine the optimal scheduling and maintenance strategy. The experimental results demonstrate that the proposed method effectively addresses the challenges in modeling and solving the joint optimization problem, enabling efficient coordination between maintenance and scheduling while enhancing the overall operational efficiency in subway vehicle management. Full article

(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)

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16 pages, 1215 KiB

Open AccessArticle

Optimal Crew Scheduling in an Intensive Care Unit: A Case Study in a University Hospital

by Müfide Narlı and Onur Derse

Appl. Sci. 2025, 15(7), 3610; https://doi.org/10.3390/app15073610 - 25 Mar 2025

Viewed by 274

Abstract

Effective crew scheduling in hospitals with multiple personnel groups is essential for time efficiency and fair workload distribution. This study focuses on optimizing shift scheduling for a team of nurses, doctors, and caregivers working in the Pediatric Intensive Care Unit (PICU) of a [...] Read more.

Effective crew scheduling in hospitals with multiple personnel groups is essential for time efficiency and fair workload distribution. This study focuses on optimizing shift scheduling for a team of nurses, doctors, and caregivers working in the Pediatric Intensive Care Unit (PICU) of a university hospital. The model is implemented and solved using GAMS 23.5 software to minimize total staffing costs while ensuring balanced shift allocations. The scheduling process in PICUs is influenced by multiple factors, including staff skills, experience levels, personal preferences, contractual agreements, and hospital demands. Since these factors affect doctors, nurses, and caregivers differently, the model considers each personnel group separately while integrating them into a unified optimization framework. The proposed model successfully generates an annual optimal shift schedule for 10 doctors, 14 nurses, and 9 caregivers, ensuring equitable workload distribution and compliance with hospital regulations. By implementing this scheduling approach, employee satisfaction is enhanced, service quality is improved, and administrative workload is reduced. Additionally, the model ensures a well-balanced distribution of responsibilities, minimizes scheduling inefficiencies, and significantly reduces the time required for shift planning. Ultimately, this study provides a fast, fair, and cost-effective solution for hospital workforce management. Full article

(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)

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30 pages, 4768 KiB

Open AccessArticle

Dynamic Scheduling in Identical Parallel-Machine Environments: A Multi-Purpose Intelligent Utility Approach

by Mahmut İbrahim Ulucak and Hadi Gökçen

Appl. Sci. 2025, 15(5), 2483; https://doi.org/10.3390/app15052483 - 25 Feb 2025

Viewed by 563

Abstract

This paper presents a robust and adaptable framework for predictive–reactive rescheduling in identical parallel-machine environments. The proposed Multi-Purpose Intelligent Utility (MIU) methodology utilizes heuristic methods to efficiently address the computational challenges associated with NP-hard scheduling problems. By incorporating 13 diverse dispatching rules, the [...] Read more.

This paper presents a robust and adaptable framework for predictive–reactive rescheduling in identical parallel-machine environments. The proposed Multi-Purpose Intelligent Utility (MIU) methodology utilizes heuristic methods to efficiently address the computational challenges associated with NP-hard scheduling problems. By incorporating 13 diverse dispatching rules, the MIU framework provides a flexible and adaptive approach to balancing critical production objectives. It effectively minimizes total weighted tardiness and the number of tardy jobs while maintaining key performance metrics like stability, robustness, and nervousness. In dynamic manufacturing environments, schedule congestion and unforeseen disruptions often lead to inefficiencies and delays. Unlike traditional event-driven approaches, MIU adopts a periodic rescheduling strategy, enabling proactive adaptation to evolving production conditions. Comprehensive rescheduling ensures system-wide adjustments to disruptions, such as stochastic changes in processing times and rework requirements, without sacrificing overall performance. Empirical evaluations show that MIU significantly outperforms conventional methods, reducing total weighted tardiness by 50% and the number of tardy jobs by 27% on average across various scenarios. Furthermore, this study introduces novel quantifications for nervousness, expanding the scope of stability and robustness evaluations in scheduling research. This work contributes to the ongoing discourse on scheduling methodologies by bridging theoretical research with practical industrial applications, particularly in high-stakes production settings. By addressing the trade-offs between improving the objective function or improving the rescheduling performance, MIU provides a comprehensive solution framework that enhances operational performance and adaptability in complex manufacturing environments. Full article

(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)

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21 pages, 2521 KiB

Open AccessArticle

Crossover Operator Inspired by the Selection Operator for an Evolutionary Task Sequencing Algorithm

by Piotr Ciepliński and Sławomir Golak

Appl. Sci. 2024, 14(24), 11786; https://doi.org/10.3390/app142411786 - 17 Dec 2024

Viewed by 701

Abstract

This paper proposes a novel crossover operator for evolutionary algorithms in task sequencing and verifies its efficacy. Unlike the conventional blind and entirely stochastic selection of sequence fragments exchanged with the second individual, the proposed operator employs a method where the probability of [...] Read more.

This paper proposes a novel crossover operator for evolutionary algorithms in task sequencing and verifies its efficacy. Unlike the conventional blind and entirely stochastic selection of sequence fragments exchanged with the second individual, the proposed operator employs a method where the probability of fragment selection is influenced by the total cost of internal connections within the exchanged fragments. At the same time, the new operator retains its stochastic nature and is not a deterministic operator, which reduces the risk of the evolutionary algorithm getting stuck in a local minimum. The idea of the proposed crossover operator was based on the main mechanism of the evolutionary algorithm that determines the success of this type of algorithm selection. To assess its effectiveness, the new operator was compared against previously employed crossover operators using a traveling salesman problem (TSP) instance in a multidimensional space in order to map the problem of symmetric sequencing tasks described with multiparameters (e.g., a symmetric variant of production tasks sequencing). Full article

(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)

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23 pages, 2829 KiB

Open AccessArticle

Research on Continuous Casting–Hot Rolling Scheduling Model Involving Reheating Furnace Conversion Mode and Improved Bat Optimization Solution Algorithm

by Nan Zhang, Tianru Jiang and Zhimin Lv

Appl. Sci. 2024, 14(11), 4494; https://doi.org/10.3390/app14114494 - 24 May 2024

Cited by 3 | Viewed by 1564

Abstract

As the main energy-consuming equipment, the reheating furnace plays an important role in the scheduling of continuous casting and hot rolling for a steel mill. This paper studies the energy consumption of cold and hot slab conversion mode in a reheating furnace, forming [...] Read more.

As the main energy-consuming equipment, the reheating furnace plays an important role in the scheduling of continuous casting and hot rolling for a steel mill. This paper studies the energy consumption of cold and hot slab conversion mode in a reheating furnace, forming four conversion relationships, with the goal of minimizing the coupling function between the maximum completion time of the hot rolling and the energy waste of the reheating furnace, and modelling the scheduling of a continuous casting–hot rolling process. An improved ordered pair bat algorithm is used to solve for special process conditions of the proposed scheduling model. The effectiveness of the proposed model and algorithm is verified by solving the different slab charging sequence and conversion mode. Moreover, a comparative study of different types of batches is also conducted, and it is found that the model can save more than 5000 GJ per month for the steel mill, which can achieve the goal of effective energy saving and increasing enterprise profit. Full article

(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)

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24 pages, 4714 KiB

Open AccessArticle

A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers

by Inessa Ainbinder, Evgeni Temnikov and Miriam Allalouf

Appl. Sci. 2024, 14(9), 3799; https://doi.org/10.3390/app14093799 - 29 Apr 2024

Cited by 1 | Viewed by 1400

Abstract

A virtual marketplace or service-providing system must ensure minimal task response times. Varying working rates among the human workers in the system can lead to longer delays for certain tasks. The waiting time in the queue is crucially affected by the queueing architecture [...] Read more.

A virtual marketplace or service-providing system must ensure minimal task response times. Varying working rates among the human workers in the system can lead to longer delays for certain tasks. The waiting time in the queue is crucially affected by the queueing architecture used in the system, whether global or local. Studies generally favor global queue systems over local ones, assuming similar processing rates. However, system behavior changes when workers are heterogeneous. In this research, we used simulation to compare the waiting times of tasks assigned to three categories of processing rates in both architectures and with various routing policies in local queues. We found that when using random tie-breaking, there was a correlation between waiting time duration and the proportion of tie-breaking events. Performance is improved when controlling these events using scheduling awareness of the workers’ processing rates. The global queue outperforms local queues when the workers are homogeneous. However, the push mechanisms that control the assignment processes and heterogeneity-aware algorithms improve local queue system waiting times and load balance. It is better than global queues when tasks are assigned to medium and fast workers, but it also enables specific slow workers’ assignments. Full article

(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)

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19 pages, 1636 KiB

Open AccessArticle

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 - 9 Apr 2024

Cited by 8 | Viewed by 3789

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

Open AccessArticle

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

Cited by 2 | Viewed by 1136

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

Open AccessArticle

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

Cited by 1 | Viewed by 1413

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

Open AccessArticle

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

Cited by 2 | Viewed by 2090

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

Open AccessArticle

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

Cited by 7 | Viewed by 3019

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

Open AccessArticle

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 10 | Viewed by 8591

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

Open AccessArticle

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 - 4 Feb 2023

Cited by 7 | Viewed by 2268

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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