This article presents a review of the Genetic Algorithm (GA), a prominent optimization technique inspired by natural selection and genetics. In the context of rapidly evolving computational methodologies, GA have gained considerable attention for their efficacy in solving complex optimization problems across various domains. The background highlights the growing significance of optimization techniques in addressing real-world challenges. However, the inherent complexity and diversity of problems necessitate versatile approaches like GA. The problem statement underscores the need to explore the underlying operations and applications of GA to provide a nuanced understanding of their capabilities and limitations. The objectives of this review encompass delving into the fundamental genetic operators, such as selection, crossover, and mutation, while examining their role in maintaining diversity and converging toward optimal solutions. Methodology-wise, a systematic analysis of existing literature is undertaken to distil key insights and trends in GA applications. The main findings show the adaptability of GA in tackling problems spanning engineering, economics, bioinformatics, and beyond. By facilitating the discovery of optimal or near-optimal solutions within large solution spaces, GA proves its mettle in scenarios where traditional methods fall short. The conclusion underscores the enduring relevance of GA in the optimization landscape, emphasizing their potential to remain a pivotal tool for addressing intricate real-world challenges, provided their parameters are fine-tuned judiciously to balance exploration and exploitation.

Industry 4.0 has the potential to cause both evolutionary (operational) and revolutionary (strategic) changes, but assessing the readiness of companies towards Industry 4.0 has largely been a challenge. Industry 4.0 readiness is also important as it is the first step for companies that want to adopt Industry 4.0 technologies. This paper pilot surveys 100 technology companies in Malaysia to understand their overall readiness towards Industry 4.0. In particular, this research paper contributes to the assessment of Industry 4.0 readiness in terms of seven key areas: (i) Market pressure, (ii) risk-taking, (iii) knowledge, (iv) management support, (v) competencies, (vi) motivation and (vii) freedom. These findings can act as stepping stones for managers and companies that are aiming towards the implementation of Industry 4.0 readiness.

Industry 4.0 revolution, with its cutting-edge technologies, is an enabler for businesses, particularly in reducing the cost and improving the productivity. However, a large number of organizations are still too in their infancy to leverage the true potential of Industry 4.0 and its technologies. This paper takes a quantitative approach to reveal key insights from the companies that have implemented Industry 4.0 technologies. For this purpose, 238 technology companies in Malaysia were studied through a survey questionnaire. As technology companies are usually the first in line to adopt new technologies, they can be studied better as leaders in adopting the latest technologies. The findings of this descriptive study surfaced an array of insights in terms of Industry 4.0 readiness, Industry 4.0 technologies, leadership, strategy, and innovation. This research paper contributes by providing 10 key empirical insights on Industry 4.0 that can be utilized by managers to pace up their efforts towards digital transformation, and can help the policymakers in drafting the right policy to drive the digital revolution.

Quality has become a fundamental issue for organizational success in today’s dynamic business environment particularly hotel industry as it affects the stakeholders which involve government, employers, employees as well as the end users. Total Quality Management (TQM) has seen as a revolution in management whereby it not only began to exert its vigorous effect on national business system but also referred as an comprehensive approach for total organizational performance improvement. TQM literature has been widely addressed and many researchers evaluated the gaps in TQM to achieve organizational performance. However, the literature does not seem to agree on a universal framework for the implementation of TQM philosophy in hotel settings. This paper emphasizes the importance level of inter-dependency among the TQM which has rarely been explored in the preceding studies. A novel hybrid Multi-Criteria Decision Making (MCDM) model is used to address the dependence relationships of factors with the aid of grey relational analysis, Analytical Network Process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL). The initial model of the study is designed by considering main criteria and sub-criteria of TQM using interview method. DEMATEL is employed to construct interrelations among criteria and sub-criteria in the integrated model. By using this approach, the inter-dependencies strength among the criteria and sub-criteria is tested. The ANP method is then applied in order to determine the relative importance of the TQM and used to identify how TQM are weighted and prioritized by the management representatives (MR) or hotel top management. The development of this MCDM model will assist Malaysia hotels in identifying the key elements and factors that need to be thoroughly considered and managed for achieving competitive advantage as well as be a guide for hotel entities to invent new quality management practices or improve existing management policies.

Slow-moving product is harmful to the business. The slow-moving products take up space and tie up the company’s capital and leave the company with fewer funds to invest in its business. Several factors can cause this issue. There are several methods ranging from statistics to heuristic methods for a company to identify slow-moving inventory but all of them rely on data. In this paper, a partitioning technique from the graph network is proposed to partition the inventories or products into a few clusters. It can help the company to identify what group does the product belongs to and at the same time suggest to the company which product can be paired up or bundled up together to clear up aging and slow-moving products. The partition technique is proposed, and the algorithm is coded using the Visual C++ programming language. The simulation results show that the proposed method can partition the task graph onto smaller subgraphs. The subgraphs called cluster consists of the nodes or products with similar purchase volume (the strong connection between the two nodes). Implementing the partitioning technique could help the companies or managers select the appropriate product to be paired together when doing the promotion.