Raja Masadeh, Nesreen Alsharman, Ahmad Sharieh, Basel A. Mahafzah and Arafat Abdulrahman
Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons…
Abstract
Purpose
Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.
Design/methodology/approach
Cloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.
Findings
The performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.
Originality/value
To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.
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Shadi Habis Abualoush, Abdallah Mishael Obeidat, Ali Tarhini, Ra’ed Masa’deh and Ali Al-Badi
The purpose of this paper is to investigate the interrelationships among knowledge management (KM), information systems (IS) and employees’ empowerment (EE) on employees’…
Abstract
Purpose
The purpose of this paper is to investigate the interrelationships among knowledge management (KM), information systems (IS) and employees’ empowerment (EE) on employees’ performance (EP).
Design/methodology/approach
Accordingly, a structural model is developed that delineates the interactions among these constructs and explores the mediating effect of EE on the relationship between KM, IS and EP. A questionnaire-based survey was designed to test the aforementioned model based on dataset of 287 employees’ pharmaceutical industries in Jordan. The model and posited hypotheses were tested using structural equation modeling analysis.
Findings
The results indicated that KM and IS positively and significantly affect EE, in which the latter impact EP as well. However, neither KM nor IS proved to be positively related to EP. Additionally, EE positively and significantly mediated the relationship between KM and EP, besides the relationship between IS and EP.
Originality/value
This is one of the few studies which investigate the interrelationships among KM, IS and EE on EP, and the first to test the model on companies in the pharmaceutical industries in Jordan.
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Keywords
This paper aims to conduct an inclusive bibliometric review of the International Journal of Lean Six Sigma (IJLSS) to elucidate the scholarly landscape, growth trends, impact…
Abstract
Purpose
This paper aims to conduct an inclusive bibliometric review of the International Journal of Lean Six Sigma (IJLSS) to elucidate the scholarly landscape, growth trends, impact, mappings, couplings, networking and thematic evolution within the field of Lean Six Sigma (LSS) research.
Design/methodology/approach
Using advanced bibliometric techniques, including network analysis and clustering, this study examines the publication output of IJLSS since its inception in 2010. The analysis focuses on identifying key contributors, mapping collaborative networks, tracing thematic evolution and exploring emerging research trends. The study is executed as per the proposed easy-to-understand methodology, containing well-structured nine segments hitting various critical-bibliometrics of IJLSS along with their respective implications.
Findings
The review reveals substantial growth in the publication output of IJLSS, with India emerging as a prominent contributor. Keywords such as “Lean”, “Six Sigma”, “Quality Management”, “Operational Excellence”, “Supply Chain Management”, “Industry 4.0” and “Sustainability” emerge as central themes, reflecting the journal’s focus on process improvement methodologies along with corresponding case studies. Collaborative networks among authors and countries are robust, indicating the global reach of LSS scholarship. Emerging research trends highlight areas of potential future exploration within the field.
Research limitations/implications
Limitations of this study include the reliance on bibliometric data and the exclusion of nonindexed sources. However, the findings offer valuable insights into the scholarly landscape of IJLSS, providing researchers and practitioners with a comprehensive overview of LSS research inclinations and developments.
Originality/value
This paper contributes to the literature by providing a detailed analysis of LSS research published in IJLSS through a unique approach and future directions. The study adds to existing knowledge by mapping collaborative networks, tracing thematic couplings and identifying emerging research clusters within the ever evolving domain of LSS.