Advanced discrete firefly algorithm with adaptive mutation‐based neighborhood search for scheduling unrelated parallel machines with sequence‐dependent setup times

Ezugwu, Absalom E.

doi:10.1002/int.22733

Cited by 8 publications

(25 citation statements)

References 43 publications

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“…Therefore, they are more suitable for solving small-sized problem instances. In this paper, we adopt and enhance the MIP formulation presented in previous studies for minimizing the makespan of the UPMSP (Anagnostopoulos and Rabadi, 2002;Helal, Rabadi, and Al-Salem, 2006;Ezugwu and Akutsah, 2018;Ezugwu, 2019;2022). We believe that our MIP model provides a clear theoretical foundation for the variant of the parallel machine problem addressed in this research.…”

Section: Problem Formulation and Mathematical Modelmentioning

confidence: 99%

“…These concepts were adapted from the work of Ezugwu and Akutsah (2018), Ezugwu (2019), and Ezugwu (2022), and discussed in more detail in this paper.…”

Section: Application Of Metaheuristics For Upmspsmentioning

confidence: 99%

“…The UPMSP is a challenging problem that is known to be NP-hard. However, metaheuristic optimization algorithms can be used to find high-quality solutions to UPMSP (Ezugwu, 2022). More over, by optimizing UPMSP, industries can enhance resource utilization, reduce energy consumption, minimize waste, and ultimately align their operations with sustainable development goals (SDGs) as highlighted in the studies presented by Palomares et al (2021) and Sartal et al (2020).…”

Section: Introductionmentioning

confidence: 99%

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Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Ezugwu

2024

IEEE Access

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Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The evaluation of the algorithms focuses on their ability to improve the optimization of job-to-machine assignments, enabling industries to efficiently minimize the overall makespan of scheduling tasks. This, in turn, leads to waste reduction and enhanced energy efficiency. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives. We assess the algorithms' performance in terms of solution quality, convergence speed, robustness, and scalability, while also examining their implications for sustainable resource allocation and environmental stewardship. The findings of this study provide insights into the efficacy of metaheuristic optimization algorithms for addressing UPMSP with a focus on sustainable development goals. By leveraging these algorithms, industries can optimize scheduling decisions to minimize waste and enhance energy efficiency. The practical implications of this research are valuable for decision-makers, production planners, and researchers seeking to achieve sustainable development goals in the context of unrelated parallel machine scheduling. By embracing metaheuristic optimization algorithms, businesses can optimize their scheduling processes while aligning with sustainable principles, leading to improved operational efficiency, cost savings, and a positive contribution to the global sustainability agenda.INDEX TERMS sustainable development goals, metaheuristic optimization algorithms, unrelated parallel machine scheduling, resource utilization, energy consumption, environmental impact.

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Section: Problem Formulation and Mathematical Modelmentioning

confidence: 99%

“…These concepts were adapted from the work of Ezugwu and Akutsah (2018), Ezugwu (2019), and Ezugwu (2022), and discussed in more detail in this paper.…”

Section: Application Of Metaheuristics For Upmspsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Ezugwu

2024

IEEE Access

Self Cite

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“…Companies would use these systems to know where to position product adverts or offers. Many online retailers can better manage inventory and optimize logistics, such as warehousing, using predictive modeling based on machine learning techniques [ 142 ]. Furthermore, machine learning techniques enable companies to maximize profits by creating packages and content tailored to their customer's needs, allowing them to maintain existing customers while attracting new ones.…”

Section: Machine Learning Researchmentioning

confidence: 99%

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Ezugwu

Oyelade

Ikotun

et al. 2023

Arch Computat Methods Eng

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The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa’s most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

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“…This section covers the answers to the designed research questions stated in Section 3.1, with each subsection distinctly handling answers to each research question. Meta-heuristics techniques are developed for providing optimal solutions to optimization problems through iterative exploration and exploitation of the entire search space [20]. A number of these algorithms have been integrated with the traditional K-means algorithm to improve the process of data clustering.…”

Section: Data Synthesis and Analysismentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

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Advanced discrete firefly algorithm with adaptive mutation‐based neighborhood search for scheduling unrelated parallel machines with sequence‐dependent setup times

Cited by 8 publications

References 43 publications

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

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