In this study, an Economic Return Quantity (ERQ) model for the Empty Container Reposition (ECR) problem using the reverse logistics (RL) approach is developed. Some of the model’s primary considerations are the return rate that depends on the quantity and quality of the empty container, and the capacity constraints to hold the empty container in the port. The model of ERQ is optimized using an analytical approach. Based on the result of the hypothetical case, the authors examined that the acceptable quality level of reusable containers should be set at 67%, 55%, and 50% for the three types of containers to be able to obtain minimum inventory costs. Two cases of binding and nonbinding constraints are investigated, and it is found that the binding constraint gives 3.4% higher cost than the latter. The results of this study help the container depots to plan, manage, and handle empty containers so that the container utility can be increased, and inventory costs can be minimized.

Rainfall and temperature are essential inputs for agricultural production, especially climate change-related ones. In turn, it is necessary for the development and survival of both men and animals at all biotic levels. In a reserve of great importance for the country, such as the Pacaya Samiria, these two factors play a preponderant role in the survival of animals and inhabitants of it. In this paper, VAR analysis with Granger's causality was used to determine the relationship between both variables. It was shown that there was a short-term positive relationship between climate change and rainfall in Pacaya Samiria.

Saudi Arabia's Vision 2030 seeks to transition towards a cashless society and increase non-cash transactions to 70% by 2025. The COVID-19 pandemic has further accelerated cashless activities in Saudi Arabia, with e-payments increasing by 75% in the past year. This study explores consumer behavior towards using e-wallets in the post-COVID-19 era by employing the extended Technology Acceptance Model (TAM). The results of an online survey conducted among 303 Saudi citizens were analyzed using SPSS. This study examines the correlation coefficients between the variables and conducts an ANOVA to determine the influence of all variables on consumer behavior towards e-wallets in Saudi Arabia post-COVID-19. The findings indicate that consumer behavior is positively influenced by perceived usefulness, perceived ease of use, lifestyle compatibility, and traceability, whereas cost does not significantly impact the use of e-wallets. Additionally, the results show that approximately 28.1% of the respondents used e-wallet services due to the pandemic. This study adds to the literature by expanding on previous work on the topic and providing detailed insights into the factors of e-wallet acceptability in Saudi Arabia.

Educational institutions generate a large amount of digital data in their daily operations, which is stored in servers, forming a substantial educational data set. Extracting valuable information through practical data analysis has become a critical problem that needs to be solved urgently. Students' examination results are an essential basis for evaluating their learning status, which reflects the effect of school education to some extent. Therefore, we propose a model based on the BP network and Pandas to construct a prediction model for Pandas' performance in the first year and their successful graduation to explore the potential relationship between Pandas' performance in the freshman year and graduation, thus realizing the principle of early guidance and improvement of teaching quality. Through the random prediction experiment of 9,424 scores data of 304 students in 2017 and 2018 majoring in network engineering at a university, the accuracy rate is 96.71% after the experimental data analysis and verification, which has proved that there is a potential correlation between the students' first-year course scores and graduation. Meanwhile, the improved BP network proposed in the present research exhibits reasonable practicability and extensibility in the college student achievement prediction model.

The research was conducted based on the theory of planned behavior (TPB), with the research model built on 06 influencing factors to the digital transformation process of businesses, with the intermediate variable “Decision on digital transformation”. There were 456 small and medium-sized enterprises in Hanoi surveyed for this research from March to June 2023. Research results found that the digital transformation process of small and medium-sized businesses in Hanoi are strongly affected by the “digital transformation decision”; while the technology platform and employees' capabilities largely determine these businesses’ decision to digital transformation and its process. On that basis, three policy implications are proposed to promote the digital transformation process at small and medium-sized enterprises in Hanoi.

The Multiple Traveling Salesman Problem (MTSP) was able to model and solve various theoretical and real-life applications. This problem is one of the many difficult issues that have no perfect solution yet. In this paper, on the one hand genetic algorithms with different combinations of operators and simulated annealing were used to solve the MTSP. On the other hand, the genetic algorithm with the combination of operators that gave the best solutions of the MTSP was hybridized with a Simulated Annealing algorithm. The simulation results showed that the hybrid algorithm significantly outperforms most of the comparable methods in obtaining the best-fitness solutions compared to the other methods in most of the test cases. In addition, by scaling the fitness function according to the amplitude of tours, it was obvious that the non-dominated front obtained by the hybrid algorithm was better than the non-dominated front obtained by the other algorithms.

This paper studies the problem of assigning tasks to a workforce with different skills. The problem is modeled as an unrelated parallel scheduling problem, incorporating sequence-dependent setup times (UPMSPSDST). Exact methods generally are not able to solve real large problems of UPMSPSDST. Hence, this research introduces an efficient, straightforward metaheuristic solution leveraging the Ant Colony Optimization (ACO) algorithm. The objective is to minimize the total completion time while assigning jobs to unrelated parallel machines with sequence-dependent preparation times. The algorithm establishes a threshold for improving the Ants (solutions) to select only promising ants for the improvement phase, thereby reducing the computational effort performed by local search operators. The proposed ACO algorithm maintains a basic structure and could be extended to solve other scheduling problems. A set of test instances available in the literature has been used to validate the efficiency of the proposed methodology. In addition, the results have been compared with the best previously published works. The ACO algorithm improves 30% of the best-known solutions (BKS) and reaches 30% of the BKS. The results show that the average performance of the ACO algorithm exceeds the average performance of the methods used by the best previously published works for the UPMSPSDST.

This study endeavors to evaluate the effectiveness of green distribution and green supply chain practices at PT Bio Farma (Persero) in relation to polio vaccine products. The ultimate objective is to establish and attain future green distribution targets. The research methodology utilizes the Green Supply Chain Operations Reference (Green SCOR) model and the Analytical Hierarchy Process (AHP) to analyze the production and distribution data of polio vaccine (OPV) and inactivated polio vaccine (IPV) products from January to December 2023. The performance values for each key component of Green Distribution and Green Supply Chain are as follows: Plan = 22.9, Source = 16.60, Make = 21.53, Deliver = 11.24, and Return = 7.08. The total performance value for Green Distribution and Green Supply Chain is 79.41. According to APICS standards, the performance of Green Distribution and Green Supply Chain is rated as “Good”. Future performance improvements will focus on supply chain and collaborative planning, establishing long-term partnerships with suppliers and customers, optimizing environmentally friendly transportation, and developing the enterprise information system. Our study proposes that the distribution and logistics managers at PT Bio Farma should persistently monitor and assess their operations to enhance the efficiency of their green distribution and green supply chains. By doing so, they can ensure the successful attainment of performance objectives for disseminating the polio vaccine in Indonesia. This research presents a significant advancement by incorporating the Green SCOR framework into the specific domain of polio vaccines, emphasizing sustainability and environmental efficiency. This approach paves the way for new avenues of investigation in green supply chain research and offers a novel viewpoint on how the pharmaceutical industry, specifically vaccine distribution, can utilize sustainable practices to enhance their operations while mitigating environmental ramifications.

This research presents a novel approach to address the Virtual Machine Placement Problem (VMPP) in cloud data centers with the aim of minimizing energy consumption. The main contributions of this study are threefold. Firstly, a Duelist Algorithm specifically designed for VMPP, which introduces a unique concept of duelists combined with optimization techniques. The algorithm aims to strike a balance between exploration and exploitation in the search space, leading to more effective resource allocation and energy-efficient cloud data center management. Secondly, enhance the performance of the Duelist Algorithm by reducing the number of algorithm-specific parameters. This simplifies the implementation process and increases the algorithm's adaptability to various real-world problems, making it more user-friendly and robust. Lastly, conduct a comprehensive comparison of the Duelist Algorithm with the widely used Hybrid Harmony Search Algorithm (HS+SA+LS) in terms of energy consumption and overall efficiency. The experimental results demonstrate that the Duelist Algorithm consistently outperforms the Hybrid Harmony Search Algorithm, achieving remarkable improvements in both best and mean fitness values. Additionally, the Duelist Algorithm exhibits lower standard deviation values, indicating more stable and consistent performance. The findings of this research validate the effectiveness of the proposed Duelist Algorithm in minimizing energy consumption and optimizing cloud resource allocation. The reduction of algorithm-specific parameters further contributes to its versatility and simplicity.