Abstract: Vehicle Routing Problem (VRP) is important in the transportation and logistics industries. Vehicle Routing Problem with Time Window (VRPTW) is a kind of VRP with the additional time windows constraint in the model and is classified as an NP-hard problem. In this study, we proposed Stas crossover in Genetic Algorithm (GA) to solve VRPTW by developing the problem with K-mean clustering. The experiments use the standard Solomon’s benchmark problem instances for VRPTW. The results with K-mean clustering are shown to perform better for minimum distance and average distance than without K-mean clustering. In the case of location and dispersion characteristics of the customer, the paths with K-mean clustering are arranged into groups and are orderly, but the paths without K-mean clustering are disordered. After that, this paper shows the comparison of the crossover operator performance on instances of Solomon benchmark, and appropriate crossover operators are recommended for each type of problem. The results of the proposed algorithm are better than the best-known solutions from the previous studies for some instances. Moreover, our proposed research will serve as a guideline for a real-world case study.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The research investigated the dynamics affecting the implementation of target costing in startups in Thailand. Startups face turbulent and competitive environments, lack of market demand and regulatory hurdles, which require effective cost management strategies. The study used quantitative methodology to evaluate the effect of various factors - perceived environmental uncertainty, competitor influence, product diversity, firm revenue, and business strategy for the adoption of target costing. Primary data from a sample of 314 respondents were used. The constructs validity and reliability were analyzed using Confirmatory Factor Analysis while Multiple regression analysis was used to evaluate the study hypotheses. The findings indicated that adoption of target costing was positively and significantly influenced by perceived environmental uncertainty, competitor influence, firm revenue, and business strategy, while product diversity has an insignificant influence. The study recommended that startup managers should consider using complex cost management techniques, as a means of acquiring competitive market advantage, strategic alignment of cost management and using competitors as a benchmark to evaluate their market competitiveness.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Hospital hazards and human errors pose a significant and complex problem, with rising incidents and irreversible consequences. Managing laboratory errors and risks is vital due to the presence of chemicals, electrical equipment, and the involvement of students, professors, and staff. The high value of laboratory equipment further underscores the need for robust risk management strategies. To address these challenges, researchers have explored the Failure Mode and Effects Analysis (FMEA) method for risk identification and assessment in healthcare settings. However, recognizing its limitations, this study aims to prioritize and evaluate laboratory errors using an integrated approach that combines the Best-Worst Method (BWM) and Complex Proportional Assessment with a Fuzzy Spherical Environment (CoCoSo-FSE). By applying the BWM, criteria such as severity, detectability, and occurrence probability are weighted to account for the nature of laboratory errors. The CoCoSo-FSE is then employed to evaluate and prioritize 18 identified laboratory errors, reducing uncertainty and enhancing decision-making. The fuzzy spherical set is used to address uncertainties by providing a flexible framework for decision-makers to define membership functions in specific spherical regions, enhancing the representation of knowledge and decision-making information. The proposed approach is compared with other decision-making methods, namely MOORA and COPRAS, demonstrating reliable ranking results. Sensitivity analysis confirms the stability of the approach's ranking when adjusting the flexibility parameter. This integrated approach offers a reliable and robust decision-making technique for managing laboratory errors, providing valuable insights to enhance laboratory safety and risk management for stakeholders, managers, and policymakers.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Continuously increased order and variability of the inventory in the uppermost level of the supply chain node create the Bullwhip effect. In the context of closed-loop supply chains, this dynamic phenomenon is still little understood despite modern nations' increasing interest in exploring the potential for a circular economy. The problem-specific literature has produced results that are a little bit contradictory. I derive formulas in four archetypes for computing inventory order and variance amplification with different information transparency structures to better understand the Bullwhip Effect in the closed-loop structure. It’s interesting to note that the visibility of the supply chain's degree significantly influences how lead time and return rate affect the performance of that system. From this vantage point, I may review differences from earlier studies. Later on, I switched the perspective of the study from operational to economic. Here, the ideal return rate was established, and the four closed-loop supply chain (CLSC) archetypes where it might be expressed were provided. I demonstrate that the lead times, demand unpredictability, and the cost structure of all nodes affect the ideal rate of return. In this study, I also address pertinent management implications and the properties of various closed-loop systems from the perspective of Bangladesh.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The study aimed to discover the effect of the main characteristics of accounting information (AI) in achieving sustainable development (SD) in Al-Kharj Governorate by studying the characteristics of (AI) represented in relevance and reliability with independent variables and studying the dimensions of sustainable development (economic, social and environmental). The theoretical and applied study will use the descriptive and analytical approach. Data were collected through a questionnaire distributed to the study sample represented by business organizations in Al-Kharj Governorate. The data is analyzed using structural equation modeling with partial least squares. The expected results of the study are: The relevance of (AI) positively affects the economic dimension of (SD) in Al-Kharj Governorate, the relevance of (AI) positively affects the social dimension of (SD) in Al-Kharj Governorate, the relevance of (AI) positively affects the environmental dimension of (SD) in Al-Kharj Governorate, the reliability of (AI) positively affects the economic dimension of (SD) in Al-Kharj Governorate, the reliability of (AI) no effects on the social dimension of (SD) in Al-Kharj Governorate, the reliability of (AI) no affects the environmental dimension of (SD) in Al-Kharj Governorate.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The increasing population in Indonesia results in the rising demand for consumer goods, including salt. Meanwhile, salt production in Indonesia remains traditional, using direct methods by evaporating seawater in open ponds near the coast, producing a final product called “solar salt”. This process depends on sunlight, air, weather, and seasonal climate conditions. This research aims to analyze the technical, technological, and managerial disparities among traditional solar salt farming businesses operated by local communities across regions in Madura Island—the foremost solar salt-producing region in Indonesia. This study employs primary data collected through surveys conducted during the production season in 2023/2024 in three regencies in Madura: Pamekasan, Sampang, and Sumenep. The structured questionnaires captured the input and output data. Meta-frontier Data Envelopment Analysis (meta-DEA) was applied to assess the technical efficiency of conventional solar salt productions across the research regions. The efficiency analysis revealed that, with the current production methods, solar salt farmers achieved an efficiency rate of 46.98%, with an average technical efficiency of 80.83%. This result shows that the decision-making units (DMUs) can enhance their technical efficiency by 19.07%. Meanwhile, the technology gap ratio (TGR) analysis indicates that Sumenep Regency has the highest TGR value, nearing the threshold of 1, suggesting a relatively low technology gap in this regency. The meta-DEA decomposition indicates that the determinant of the average meta-technical inefficiency among solar salt farmers is the technological disparities, with average technological gap inefficiency (TGI) values surpassing managerial gap inefficiency (MGI) values. Conversely, Sumenep Regency has a larger MGI than TGI value, implying that solar salt farmers in Sumenep possess lower managerial decision-making skills than in other regions. The findings suggest the need to enhance the adoption of the latest production technology innovations to address technological gaps in the research locations.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Conveying the products to the customers under optimized circumstances is as crucial for the companies as the production itself. One optimization strategy to consider is transportation with the minimum quantity of vehicles and the selection of courses with the minimum distance between the locations. In other words, it is the examination of the solution to the Vehicle Routing Problem (VRP), particularly the Capacitated VRP (CVRP), which is a more realistic modelization approach. For businesses that perform distribution to customers frequently, such as management work with the coordination of daily distribution, finishing the distribution on time is of great importance. In big cities with complicated roads and many dropping points, this can be achieved by benefiting from the systematic modeling of the CVRP. In this study, the delivery network investigation for one production facility of the Istanbul People's Bread positioned on the Asian side of Istanbul, Türkiye that distributes three times a day will be the focus of interest. The corresponding Asymmetric CVRP (ACVRP) for the facility network and 215 bread-selling buffets with authentic driving distances will be solved with the Genetic Algorithm (GA), and an optimized transportation network will be presented.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: There is a global trend towards adopting green energy, with solar energy being the primary source derived from solar panel technologies. Solar panels can generate enough power for general and household use. However, to effectively function and serve their purpose, they require regular cleaning and effective maintenance, and robotic cleaning is among the current applicable technologies. This research aims to determine the intention of using solar panel cleaning robots in Thailand for individual solar panel users. The study was hinged on the extended C-TAM-TPB model. The quantitative survey study design was employed using primary data collected from individual solar panel users in their households. 419 respondents were used to collect the data. The C-TAM-TPB model proposed using reliability, validity, and model fitness which employed confirmatory factor analysis (CFA). They adopted structural equation modeling (SEM) in the evaluation of the variables' relationships and study hypotheses. Subjective norms and trust in technology, individual control perception, and awareness of renewable energy significantly and positively affected behavioral intention to use solar panel cleaning robots as indicated by the study. Trust in technology, awareness of renewable energy, and environmental concerns were found to be pivotal mediators to the attitude effect on individual users' intention to act in using solar panel cleaning robots. The authors recommend that to improve the adoption of solar panel cleaning robots; the concerned stakeholders should consider, firstly, enhancing trust in the technology of these robots, which is crucial, focusing on aspects like reliability, privacy, security, and reputation. Secondly, considering the influence of subjective norms, including perceptions from family, friends, colleagues, and experts, is essential. Perceived behavioral control should also be a focal point, encompassing self-efficacy, resources, and complexity. Moreover, increasing awareness of renewable energy and environmental benefits is vital to encourage individual adoption. The research also recommended that to encourage the adoption and use of solar panel cleaning robots, the aspects that should be emphasized include subjective norm, perceived behavior control, trust in technology, and awareness of renewable energy.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study investigates the relationships between creativity, digital leadership, decision-making quality, and public service performance in Sidoarjo Regency. The primary objective is to examine how creativity and digital leadership influence decision-making quality and, subsequently, public service performance. A quantitative approach utilizing a cross-sectional study design was employed. Data were collected from 200 employees of public service institutions in Sidoarjo Regency using Google Forms and direct interviews. The main variables were assessed using Likert scales, measuring creativity, digital leadership, decision-making quality, and public service performance. The analysis involved descriptive and inferential statistics, including regression analysis and mediation analysis. The findings reveal significant positive relationships between creativity, digital leadership, decision-making quality, and public service performance. Creativity and digital leadership were found to positively impact decision-making quality, which in turn influenced public service performance. The implications suggest that fostering a culture of creativity and digital leadership is crucial for enhancing decision-making quality and, consequently, improving public service performance. Public service managers should invest in initiatives to develop creativity and digital leadership skills among employees and prioritize transparent decision-making processes. Furthermore, the study highlights the need for continuous monitoring and evaluation to ensure sustained improvements in public service delivery. The novelty lies in examining the interplay between creativity, digital leadership, decision-making quality, and public service performance within the context of Sidoarjo Regency, providing valuable insights for public service management in the region.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Manufacturing companies constantly strive to produce products that promote better competitiveness. In addition, the current business environment requires manufacturing companies to adopt environmentally friendly concepts, which have become a global customer concern. Therefore, companies must inevitably meet environmental protection requirements through ecologically friendly processes and products. Meanwhile, environmentally friendly adoption requires a capital-intensive investment, which doubts the management regarding the investment return. Hence, top management commitment is highly needed to maintain eco-friendly products and contribute to the company's performance. This study examines the role of top management commitment to operational performance through adopting information technology, supply chain integration, and green supply chain management. This study surveyed manufacturing companies that have implemented ISO 14000, as many as 73 companies with criteria of having more than 100 employees. Data is collected using questionnaires directly and online with Google Forms. The results of data processing analysis found that top management commitment influences information technology adoption with a priority scale to maintain competitiveness and strategies that increase competitiveness. Top management commitment through information technology can improve supply chain integration and green supply chain management. In addition, supply chain integration improves green supply chain management and operational performance. Implement environmental-friendly measures by involving external partners to impact operational performance. The results of this study contribute to enriching supply chain management theory by significantly adopting green supply chain management to improve sustainable development and performance. It also makes a practical contribution by providing insight for practitioners to generate added value for customers.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)