The research aimed to examine the role of business incubators and accelerators in promoting innovation among Saudi entrepreneurs, in support of the Kingdom's Vision 2030 agenda of diversifying the economy and strengthening entrepreneurship. The study population consisted of male and female entrepreneurs in Saudi Arabia, and a random sample of 120 participants with varying educational levels and experiences was selected. The researcher used a quantitative approach based on a questionnaire whose validity and reliability were statistically verified. The results showed that most participants had previously joined incubators and accelerators, and emphasized their significant importance in developing ideas, building relationships, accelerating growth, and supporting projects with training and modern technologies. The research recommended the continuous development of these programs, facilitating government policies and procedures, and increasing entrepreneurs' awareness of the importance of innovation to achieve sustainable economic and social development.

Here, we consider the generalized travelling salesman problem (GTSP), which is a generalization of the travelling salesman problem (TSP). This problem has several real-life applications. Since the problem is complex and NP-hard, solving this problem by exact methods is very difficult. Therefore, researchers have applied several heuristic algorithms to solve this problem. We propose the application of genetic algorithms (GAs) to obtain a solution. In the GA, three operators—selection, crossover, and mutation—are successively applied to a group of chromosomes to obtain a solution to an optimization problem. The crossover operator is applied to create better offspring and thus to converge the population, and the mutation operator is applied to explore the areas that cannot be explored by the crossover operator and thus to diversify the search space. All the crossover and mutation operators developed for the TSP can be used for the GTSP with some modifications. A better combination of these two operators can create a very good GA to obtain optimal solutions to the GTSP instances. Therefore, four crossover and three mutation operators are used here to develop GAs for solving the GTSP. Then, GAs is compared on several benchmark GTSPLIB instances. Our experiment shows the effectiveness of the sequential constructive crossover operator combined with the insertion mutation operator for this problem.

One of the primary goals of optimization approaches is to strike a balance between exploitation and exploration strategies, thereby enhancing the efficiency of the search process. To improve this balance, considerable research efforts have been directed towards refining these strategies. This paper introduces a novel exploration approach for the Black Widow Optimization (BWO) algorithm, termed Improved BWO (IBWO), aimed at achieving a robust equilibrium between global and local search strategies. The proposed approach tracks and remembers the effective research areas during the research iteration and uses them to direct the subsequent research process toward the most promising areas of the search space. Consequently, this method facilitates convergence towards optimal global solutions, leading to the generation of higher-quality solutions. To evaluate its performance, IBWO is compared with five optimization techniques, including BWO, GA, PSO, ABC, and BBO, across 39 benchmark functions. Simulation results demonstrate that IBWO consistently maintains precision in performance, achieving superior fitness values in 87.2%, 74.4%, and 69.2% of total trials across three distinct simulation settings. These outcomes underscore the efficacy of IBWO in effectively leveraging prior search space information to enhance the balance between exploitation and exploration capabilities. The proposed IBWO has broad applicability, addressing real-world optimization challenges in pilgrim crowd management and transportation during Hajj, supply chain logistics, and energy distribution optimization.

The security of smart grids is seriously threatened by false data injection (FDI) attacks. Falsified data is maliciously injected into the grid's measurement and control systems as part of these attacks, which might seriously disrupt the power supply and jeopardize system integrity. In the context of smart grids, it is also imperative to address the issue of consumer privacy and the protection of their sensitive data. The main objective of this work is to provide a collaborative framework based on federated learning to detect various FDI dangers while protecting SG's resources and privacy. We have implemented several technologies that provide a good solution in order to accomplish this goal. Using a dataset designed to replicate attacks on the power system environment, we used federated learning to locally train models using the data stored on the sensors. The best model should then be chosen by comparing the outcomes. These outcomes demonstrate the potential of our framework, which has used mixed models to repel attacks, short-circuit faults, and maintain lines with a 98% accuracy rate during the federated learning phase.

In the Green Supply Chain (GSC) environment, collaboration became a revolutionary philosophy. Understanding this fact is crucial for the improvement of the competitive level of any organization. Recent findings of the adoption of GSC collaboration inspire companies to invest and innovate in order to obtain a greener supply chain network by sharing information and cooperating with partners in their objectives, risks, and decision making. In this study, literature is reviewed for the collection of collaborative green supply chain enablers, validated and evaluated by experts. We focused on the interrelationships between a set of collaboration enablers on the implementation of GSC using the ‘Decision Making Trial and Evaluation Laboratory’ (DEMATEL) method. Thus, the findings could be considered as a support for researchers and practitioners to enhance strategies and perspectives for implementing collaborative GSC processes.

This research introduces a comprehensive scheme to tackle the Mixed-Model Assembly Line Balancing Problem (MALBPLW) within manufacturing contexts. The primary aim is to optimize assembly line task assignments by integrating both the learning effect and worker prerequisites. The learning effect recognizes the enhanced efficiency of workers over time due to learning and experience. A novel mathematical model and solution approach are proposed, encompassing factors like cycle time, task interdependencies, worker classifications, and the learning effect. The model endeavors to minimize the overall costs related to both workers and workstations while simultaneously maximizing production efficiency. Experimental assessments are conducted to evaluate the efficacy of this proposed approach. Diverse manufacturing scenarios are inspected, comparing and analyzing cost reductions and production efficiency. The outcomes highlight the effectiveness of this approach in achieving enhanced cost-effectiveness and resource utilization in contrast to conventional methods. This study contributes significantly to advancing assembly line balancing and production planning techniques by presenting a pragmatic framework for optimizing resource usage and reducing costs in manufacturing environments. The knowledge extracted from these discoveries can significantly assist professionals in the industry seeking to improve manufacturing processes and strengthen competitiveness.

This research examines Saudi Arabia's stock market performance from 1991 to 2021 while exploring the long-term and immediate effects of economic factors. The study employs Johansen cointegration tests alongside the Vector Error Correction Model (VECM) to analyse the influence of Gross Domestic Product (GDP), inflation, Foreign Direct Investment (FDI), trade balance and government expenditure on stock market performance. The research shows that macroeconomic variables affect market capitalisation results since GDP growth and trade openness lead to positive stock market performance; however, FDI and inflation create inconsistent effects. Results indicate the stock market maintains a long-term equilibrium between variables, leading to long-term correction of market deviations. The present study extends the discussion in the literature by offering a comprehensive and empirical analysis of the Saudi Equity Market as related to the economic diversification process, with a focus on the Saudi Arabia Vision 2030. This research calls for a solid and pro-active fiscal framework to stem inflation, attract good quality foreign capital and enhance the financial institutions. They are very useful in the formulation of strategic development plans that would foster long-term sustainable growth and increase investor confidence. The findings of this study are useful for policymakers, investors, and other analytical experts interested in the relationship between economic determinants and capital market returns, specifically within the context of Saudi Arabia, a young economy in the group of commodity-exporting countries. This study contributes original empirical evidence on the long- and short-run effects of macroeconomic indicators on stock market performance in Saudi Arabia. By applying the VECM approach across a 31-year period, it offers valuable insights for policymakers and investors in managing financial markets during economic diversification under Vision 2030.

Point-of-sale (POS) transactions are considered one of the fundamental components of retail financial systems. The performance of the POS service network relies heavily on a reliable and secure e-banking infrastructure driven by usage patterns. In this study, we perform a structural break analysis to identify all points in the POS transaction time series where major structural behavioral shifts have occurred. We then investigated the potential sources of these shifts in the data. The analysis revealed four major shifts between 2019 and 2023. The corresponding events, such as the introduction of Apple Pay, the broader rollout of MADA’s contactless and mobile payment infrastructure, COVID-19, SAMA’s regulatory sandbox and open banking pilot projects, and structural changes in banking technology and regulation, are evident as potential drivers of these changes. These results are useful for planners doing digital infrastructure management, expansion, and investment planning, as well as performing policy formulation and responses.

Effective inventory management primarily relies on precise demand forecasting, an essential yet challenging aspect for companies pursuing operational excellence. This paper outlines the design and implementation of a forecasting interface integrated with a sales dashboard to enhance demand prediction accuracy and inventory decision-making. The interface incorporates four established forecasting techniques—Naïve, Moving Average, Weighted Moving Average, and Exponential Smoothing—to systematically address demand fluctuations. Created in Excel and automated with VBA, it provides reorder points, safety stock levels, and forecasted demand, along with other distinctly user-friendly inputs and outputs. In addition, a dynamic sales dashboard has been developed with visual features representing historical and projected demand, sales distribution by products and regions which further facilitate detailed analysis and informed inventory management decisions. This study outlines the interface and dashboard development process along with important codes. It also highlights the practical implications of integrating technical forecasting methods with intuitive visualization tools to enhance inventory management substantially. The forecasting interface and sales dashboard were further verified and validated through different scenarios including: high demand in season peaks, managing with variation in lead time issues, and forecasting in case of launching new product.

It is crucial to identify alternative energy sources owing to the ever-increasing demand for energy and the other environmental problems associated with using fossil fuels. Biomass as a source of bioenergy is considered a promising alternative to fossil fuels. This study aims to optimize the biomass supply chain by developing an integrated model incorporating typical tactical supply chain decisions based on market or demand selection decisions. To this end, a novel mixed-integer linear programming (MILP) model is proposed to maximize the profit of the corresponding biomass supply chain and to commercialize electricity production by selecting electricity demand and making supply chain decisions regarding power plant operations, biomass feedstock purchase and storage, and biomass transport trucks. Owing to the intricacy of the MILP model, a fix-and-optimize-based solution strategy is developed and validated by applying it to several instances of a real-world case study. The results demonstrate that the proposed strategy can significantly reduce computational time while preserving high solution quality. Additionally, it helps improve planning and decision-making as it reveals the effect of essential biomass logistics characteristics on routing outcomes.