The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.

As the complexity and scale of software applications increase, the challenges associated with testing these systems grow correspondingly, necessitating innovative and sustainable testing strategies. This paper explores a multifaceted approach aimed at addressing the intricate challenges inherent in testing large-scale software applications. Through a comprehensive examination of current industry practices and emerging trends, this study introduces a novel framework that integrates advanced testing techniques with state-of-the-art tools. This framework not only mitigates the challenges posed by the complexity and size of modern applications but also enhances the efficiency and effectiveness of the testing process. Key aspects of this research include a detailed exploration of test methodologies suited for large-scale applications, an evaluation of advanced tools designed for complex test scenarios, and an analysis of the impact of the test environment on sustainability. The findings offer valuable insights and actionable strategies for software development and testing professionals aiming to optimize testing processes and improve the quality and sustainability of their software in a rapidly evolving technological landscape.

The emission trading scheme (ETS) is arguably one of the most effective approaches for encouraging industries to transition to a low-carbon economy and, as a result, assisting nations in meeting their goals under the United Nations Framework Convention on Climate Change to mitigate the challenge of climate change. ETS is gaining popularity as more governments throughout the world contemplate implementing it, particularly in developing countries. Much of the existing research has concentrated on debates concerning ETS operations in developed nations. This study is to give a discourse of the success criteria for ETS implementation that have been identified in the literature and then cross-referenced in the context of Malaysia. For this, the research used an integrated approach of scoping review of existing literature and in-depth interviews with Malaysian stakeholders. Using Narassimhan et al. (2018)’s ETS assessment framework, the scoping review identified five major attributes that lead to successful ETS implementation in a global context that are environmental effectiveness, economic efficiency, market management, stakeholder engagement, and revenue management. In-depth interviews with several groups of discovered stakeholder engagement as an essential attribute that would play a critical role in advancing ETS implementation in Malaysia. The study concludes by proposing a complete strategy based on empirical information and first-hand narratives, providing useful insights for politicians, industry players, and environmental activists. The recommendation is especially important as Malaysia strives to improve its commitment to sustainable and responsible development in light of the challenges posed by climate change.

African countries have shown interest in developing the legal framework for electronic payment as part of digital law. The article aims to analyze the role of the legal framework for electronic payment in the field of digital economy. It relies on a legal methodology through analyzing legal texts related to electronic payment. It also relies on the comparative and descriptive approaches whenever there is a scientific necessity. The article concluded that the legal framework plays an important role in the field of digital economy. This framework appears in the general rules of civil and commercial laws or through the laws of money and credit. Other laws also play a complementary role, such as criminal law and personal data protection laws.