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Mathematics
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Recent Advances in Optimization Algorithms for Scheduling and Operations Research
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Recent Advances in Optimization Algorithms for Scheduling and Operations Research

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4199

Special Issue Editors

Prof. Dr. Julio Mar-Ortiz

E-Mail Website
Guest Editor
Faculty of Engineering, Universidad Autonoma de Tamaulipas, Tampico 89336, Mexico
Interests: port logistics and optimization; operations research; scheduling; metaheuristics
Dr. María D. Gracia

E-Mail Website
Guest Editor
Faculty of Engineering, Universidad Autonoma de Tamaulipas, Tampico 89336, Mexico
Interests: simulation modelling; port logistics; multicriteria decision models
Dr. Manuel Vargas

E-Mail Website
Guest Editor
Industrial Engineering Department, Universidad de Santiago de Chile, Santiago 9170124, Chile
Interests: logistics modeling; prospective manufacturing; intelligent systems

Special Issue Information

Dear Colleagues,

The development of advanced scheduling and optimization algorithms to solve practical problems in domains such as healthcare, engineering, logistics, finance, agriculture, the environment, and mining is revolutionizing industries. Advanced scheduling and optimization algorithms dynamically adjust routes, timetables, operations, and schedules based on real-time factors like traffic, available capacity, demand patterns, weather, and eventual disruptions. By continuously optimizing schedules, logistics companies can ensure timely deliveries, hospitals can improve the efficiency of medical treatment, agroindustry can establish efficient and sustainable irrigation schedules, and construction projects can meet time, quality, and costs goals.

This Special Issue focuses on state-of-the-art research related to the development and application of advanced scheduling and optimization algorithms in a wide range of industrial domains. Topics of interest include, but are not limited to:

  • The applications of novel scheduling models and algorithms in healthcare, logistics, manufacturing, project management, finance, engineering, or novel domains like drone and AGV scheduling.
  • The integration of artificial intelligence and machine learning methods to dynamically optimize task assignment and resource allocation.
  • The development of real-time schedules to quickly adapt to changing conditions.
  • The implementation of cloud-based scheduling solutions to provide flexibility and accessibility.
  • The integration of sustainability considerations to minimize energy consumption or reduce environmental impacts.
  • Case studies detailing the successful implementation and integration of novel scheduling models and algorithms with existing systems and processes.

Prof. Dr. Julio Mar-Ortiz
Dr. María D. Gracia
Dr. Manuel Vargas
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. Mathematics 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 2600 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

  • operations research
  • scheduling
  • logistics
  • manufacturing
  • artificial intelligence
  • metaheuristics algorithms
  • industrial applications

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Published Papers (6 papers)

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Research

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29 pages, 1833 KiB  
Article
An Improved Marriage in Honey-Bee Optimization Algorithm for Minimizing Earliness/Tardiness Penalties in Single-Machine Scheduling with a Restrictive Common Due Date
by Pedro Palominos, Mauricio Mazo, Guillermo Fuertes and Miguel Alfaro
Mathematics 2025, 13(3), 418; https://doi.org/10.3390/math13030418 - 27 Jan 2025
Viewed by 467
Abstract
This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. The goal is to find the optimal sequence for completing a set of [...] Read more.
This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. The goal is to find the optimal sequence for completing a set of tasks on a single machine, minimizing the total penalty incurred for tasks being completed too early or too late compared to their deadlines. To achieve this goal, the study adapts the MBO metaheuristic by introducing modifications to optimize the objective function and produce high-quality solutions within reasonable execution times. The novelty of this work lies in the application of MBO to the single-machine weighted earliness/tardiness problem, an approach previously unexplored in this context. MBO was evaluated using the test problem set from Biskup and Feldmann. It achieved an average improvement of 1.03% across 280 problems, surpassing upper bounds in 141 cases (50.35%) and matching or exceeding them in 193 cases (68.93%). In the most constrained problems (h = 0.2 and h = 0.4), the method achieved an average improvement of 3.77%, while for h = 0.6 and h = 0.8, the average error was 1.72%. Compared to other metaheuristics, MBO demonstrated competitiveness, with a maximum error of 1.12%. Overall, MBO exhibited strong competitiveness, delivering significant improvements and high efficiency in the problems studied. Full article
(This article belongs to the Special Issue Recent Advances in Optimization Algorithms for Scheduling and Operations Research)
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53 pages, 495 KiB  
Article
Pareto Approximation Empirical Results of Energy-Aware Optimization for Precedence-Constrained Task Scheduling Considering Switching Off Completely Idle Machines
by José Antonio Castán Rocha, Alejandro Santiago, Alejandro H. García-Ruiz, Jesús David Terán-Villanueva, Salvador Ibarra Martínez and Mayra Guadalupe Treviño Berrones
Mathematics 2024, 12(23), 3733; https://doi.org/10.3390/math12233733 - 27 Nov 2024
Viewed by 614
Abstract
Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the [...] Read more.
Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the HPC centers Frontier, Aurora, and Super Computer Fugaku report energy consumptions of 22,786 kW, 38,698 kW, and 29,899 kW, respectively. Currently, energy-aware scheduling is a topic of interest to many researchers. However, as far as we know, this work is the first approach considering the idle energy consumption by the HPC units and the possibility of turning off unused units entirely, driven by a quantitative objective function. We found that even when turning off unused machines, the objectives of makespan and energy consumption still conflict and, therefore, their multi-objective optimization nature. This work presents empirical results for AGEMOEA, AGEMOEA2, GWASFGA, MOCell, MOMBI, MOMBI2, NSGA2, and SMS-EMOA. The best-performing algorithm is MOCell for the 400 real scheduling problem tests. In contrast, the best-performing algorithm is GWASFGA for a small-instance synthetic testbed. Full article
(This article belongs to the Special Issue Recent Advances in Optimization Algorithms for Scheduling and Operations Research)
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14 pages, 725 KiB  
Article
A Simheuristic Approach to Scheduling Sustainable and Reliable Maintenance for Bridge Infrastructure
by Tommaso Pastore, Giulio Mariniello and Domenico Asprone
Mathematics 2024, 12(21), 3420; https://doi.org/10.3390/math12213420 - 31 Oct 2024
Cited by 1 | Viewed by 699
Abstract
Designing maintenance strategies for a vast portfolio of aging infrastructures requires decision-makers to ensure adequate safety levels while addressing the requirements on service interruptions, costs, and workforce availability. This study addresses the problem of scheduling maintenance interventions for a portfolio of bridges, aiming [...] Read more.
Designing maintenance strategies for a vast portfolio of aging infrastructures requires decision-makers to ensure adequate safety levels while addressing the requirements on service interruptions, costs, and workforce availability. This study addresses the problem of scheduling maintenance interventions for a portfolio of bridges, aiming to minimize CO2 emissions while meeting minimum reliability requirements and adhering to workforce and budget constraints. To achieve this, we present a Simheuristic algorithm that combines a metaheuristic core based on the Adaptive Large Neighborhood Search metaheuristic with a Monte Carlo simulation module. This integration allows for the evaluation of optimized scheduling solutions, accounting for the inherent randomness in the structural deterioration process. The proposed approach is tested in a comparative analysis against traditional time-based and condition-based scheduling methods. Results from diverse bridge portfolios demonstrate that the proposed algorithm offers improved performance in terms of both total costs and CO2 emissions. Full article
(This article belongs to the Special Issue Recent Advances in Optimization Algorithms for Scheduling and Operations Research)
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15 pages, 2792 KiB  
Article
A Radial Memetic Algorithm to Resolve the No-Wait Job-Shop Scheduling Problem
by Ricardo Pérez-Rodríguez
Mathematics 2024, 12(21), 3342; https://doi.org/10.3390/math12213342 - 25 Oct 2024
Viewed by 686
Abstract
A new radial memetic algorithm is proposed to resolve the no-wait job-shop scheduling problem. Basically, each sequencing solution is factorized as a distance-based ranking model, i.e., each solution is decomposed in n − 1 terms, where n is the number of jobs to [...] Read more.
A new radial memetic algorithm is proposed to resolve the no-wait job-shop scheduling problem. Basically, each sequencing solution is factorized as a distance-based ranking model, i.e., each solution is decomposed in n − 1 terms, where n is the number of jobs to be sequenced. After that, a cumulative radial distribution of hydrogen is considered to produce new factorizations using the offspring information (genes). Such radial distribution is applied in the local optimization procedure of the memetic algorithm. A benchmarking dataset is used to show the performance of this new experimental technique, as well as other current procedures. Statistical tests were implemented to confirm the performance of the proposed scheme. Full article
(This article belongs to the Special Issue Recent Advances in Optimization Algorithms for Scheduling and Operations Research)
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12 pages, 5940 KiB  
Article
The Propagation of Congestion on Transportation Networks Analyzed by the Percolation Process
by Jieming Chen and Yiwei Wu
Mathematics 2024, 12(20), 3247; https://doi.org/10.3390/math12203247 - 17 Oct 2024
Viewed by 821
Abstract
Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the [...] Read more.
Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures. Full article
(This article belongs to the Special Issue Recent Advances in Optimization Algorithms for Scheduling and Operations Research)
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Review

Jump to: Research

25 pages, 3806 KiB  
Review
Truck Appointment Scheduling: A Review of Models and Algorithms
by Maria D. Gracia, Julio Mar-Ortiz and Manuel Vargas
Mathematics 2025, 13(3), 503; https://doi.org/10.3390/math13030503 - 3 Feb 2025
Viewed by 112
Abstract
This paper provides a comprehensive review of truck appointment scheduling models and algorithms that support truck appointment systems (TASs) at container terminals. TASs have become essential tools for minimizing congestion, reducing wait times, and improving operational efficiency at the port and maritime industry. [...] Read more.
This paper provides a comprehensive review of truck appointment scheduling models and algorithms that support truck appointment systems (TASs) at container terminals. TASs have become essential tools for minimizing congestion, reducing wait times, and improving operational efficiency at the port and maritime industry. This review systematically categorizes and evaluates existing models and optimization algorithms, highlighting their strengths, limitations, and applicability in various operational contexts. We explore deterministic, stochastic, and hybrid models, as well as exact, heuristic, and metaheuristic algorithms. By synthesizing the latest advancements and identifying research gaps, this paper offers valuable insights for academics and practitioners aiming to enhance TAS efficiency and effectiveness. Future research directions and potential improvements in model formulation are also discussed. Full article
(This article belongs to the Special Issue Recent Advances in Optimization Algorithms for Scheduling and Operations Research)
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