The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.

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.

The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-period aggregate production planning (APP) problem, which was used to test the algorithm, which provides optimal levels of inventory, backorders, overtime and regular production rates, and other controllable variables. According to the findings of this study, the benefit of stable crossover is that it allows for more variety in the way offspring are created and increases the opportunity for offspring to obtain good genetic information directly.

This paper aims to study the relationship between human resource management practices (including work itself, training and development, supervisor support, co-workers ‘relation, salary, and working environment) and job satisfaction among employees in Japanese agri-businesses in Vietnam. The structured questionnaires were distributed to 200 employees to collect the primary data out of which 179 useable were returned to test the research hypotheses. Results shows that the work itself, training and development, co-workers’ relation, salary and working environment had positive effects on employees’ job satisfaction. However, there were no relationship found between supervisor support with job satisfaction. Finally, based on the results of qualitative and quantitative evidence as well of the comprehensive interview, some suggestions were given to human resource management practices in Japanese agricultural companies to achieve high level of job satisfaction.

The present study examines the relationship between high performance human resource (HPHR) system and organizational citizenship behavior (OCB) in Vietnamese enterprises, and explores the mediating role of job satisfaction in this interaction. Data collected from 179 line managers working in Vietnamese companies by structured questionnaires. The results indicate that the positive relationship between HPHR system and organizational citizenship behavior was mediated by job satisfaction. The study also provides an evidence in terms of the application of HPHR system to foster the organizational outcomes and contributes to understand the underlying mechanism in human resource system and employee’s behaviors relationship.

Total quality management (TQM) is one of the popular management methods in many devel-oped countries, while it is an entirely new method for emerging economies. Although TQM practices contribute to the sustainable development of organizations, it is challenging to adopt this method efficiently. Six criteria factors are playing important roles in TQM implementation, which are: leadership, process management, quality information, employee training, customer focus, and supplier management. Each factor has different roles for firm performance represented by financial performance and non – financial performance. Identifying the relationship between TQM practices and the performance in Vietnamese enterprises, the research investigates the role of non – financial performance as a mediator variable in the relationship between TQM practices and financial performance. Through designing a questionnaire to conduct a survey, this paper studies the mediating role of non – financial performance on the relationship between TQM practices and financial performance utilizing SPSS 20 and AMOS 22 software with 211 Vietnamese enterprises by using SEM analysis. The estimation results propose that non – financial performance plays a vital role as a full mediator in the relation-ship between TQM practices and financial performance in the Vietnam context. In this research, there is no difference in the mediating effect level of non – financial performance between TQM companies & non – TQM companies as well as ISO adopters & non – ISO adopters. The study suggests that Vietnamese enterprises should have strict commitment to place TQM philosophy in actions at this early step of TQM implementation process to achieve considerable benefits in long-term development.

The primary objective of this study is associated with the management of educational system in schools using Three Law Auguste Comte at Pesantren Tebuireng Jombang, East Java, Indonesia. Since June 2006 the Tebuireng Islamic Boarding School has been providing the Islamic education. However, in these schools, the students coming from other schools do not have Islamic education and face difficulty while taking courses. Additionally, the students of Tebuireng Islamic Boarding Schools are not familiar with traditional education, such as science-based subjects; mathematics, chemistry, physics, biology and management. Therefore, man-agement of educational system is required in Tebuireng Islamic Boarding Schools. For this purpose, data were collected from the teachers of these schools by using a questionnaire survey. Partial Least Square (PLS)-Structural Equation Modeling (SEM) was used to analyze the data. Findings of the study revealed that all levels of education in Islamic schools (Pesantren) should provide science-based subjects rational, among others: mathematics, chemistry, physics, biology, management, etc. Moreover, there should be a balance between Islamic education and science-based education.

This paper has been retracted.

Paddy is a staple crop and a vital component of Indonesia's agriculture, significantly contributing to food security and rural livelihood. Nevertheless, paddy cultivation is highly vulnerable to risks such as pests, diseases, extreme weather, and natural disasters, which can lead to significant productivity losses. Thus, schemes like Area Yield Index (AYI) insurance have a critical role in mitigating these risks because. It provides financial protection to farmers by compensating them for losses due to area-wide productivity shortfalls. Hence, accurate predictions of paddy productivity are essential for setting fair and precise AYI premiums. Therefore, this study proposes an innovative framework by developing a four-parameter beta distribution Generalized Linear Mixed Model (GLMM) based on a Bayesian approach for predicting paddy productivity. The approach is motivated by the model’s ability to apply a four-parameter beta distribution that effectively models the bounded nature of paddy productivity and ensures that predictions remain within realistic range value. The inclusion of random effects also accounts for variability of paddy productivity across areas, which is commonly found in Indonesia and other countries. Meanwhile, the Bayesian framework further enhances robustness by integrating prior knowledge and providing probabilistic predictions. Based on the proposed approach, we then design an enhanced AYI policy based on district and sub district conditions. The framework is first developed through simulation studies designed to replicate real paddy productivity conditions. Comparative testing of the Stan and BRMS packages in R reveals that the proposed four-parameter beta GLMM implemented in Stan is more flexible and accurate. The methodology is then applied to an empirical case study predicting paddy productivity in Central Kalimantan (2020), using farmer survey data and lagged values of Sentinel-2A satellite indices (bands 4, 8, and NDVI) as covariates. Results show that agronomic practices such as pest management and current and historical satellite data enhance prediction accuracy, demonstrating the model's potential to predict productivity with high precision, proving that the proposed method is well-suited for calculating premiums and risks under AYI crop insurance policies. The estimated pure AYI premium ranges from IDR 300.000 to 410.000. Unlike conventional premium calculations based on average historical yields, the proposed GLMM approach provides a nuanced, data-driven alternative that accounts for various productivity factors, ensuring greater adaptability, accuracy, and responsiveness to changes in agricultural conditions, including those driven by climate change.

The use of technology in business in the era of 4.0 is a difficult task for Small and Medium Enterprises (SMEs) due to lack of resources and other market problems. To attract customers to buy a product, the marketing of a product is usually influenced by various factors, such as price, product quality, and brand. In this study, the researchers analyzed the impact of price, product quality, and product brand on customer’s buying interest. This research is a case study conducted in Fahmi Mandiri SMEs aiming to increase the income. The method used is multiple regression analysis and the data is collected through questionnaires distributed to the customers. The results show that the biggest partial correlation influencing customer buying interest are price of a product and product brand. The quality of a product does not affect customers' buying interest significantly, because consumers will trust the quality of a product when the product brand is known and has the right price.

This article has been retracted in Management Science Letters 2020 10(17),2.

Since the outbreak of the COVID-19 pandemic, the widespread use of online tools has diversified business communication strategies. However, there are few studies that look at the unique characteristics of these digital environments, especially when cameras are turned off. In this study, how the combination of visual images and voice expressiveness influences customer interest in online sales situations was investigated. Using quantitative and qualitative data, the change in customer interest for six video stimuli that combine different nonverbal cues from salespeople was analyzed: visual images (name only, photo without facial expression, smiling photo) and voice (with expression vs. without expression). The McNemar test revealed significant changes in customer interest for all combinations, with the combinations of expressive voice and smiling photo proving to be the most effective. The qualitative analysis with KH Coder’s Co-occurrence network showed that the characteristics of the voice (e.g., brightness and intonation) and the consistency between the visual image and voice are decisive for the customer’s impression. In particular, customers who initially showed interest tended to lose interest owing to discomfort and mistrust caused by monotone voices and the inconsistency of nonverbal cues, which prevented them from engaging with the content. Conversely, customers who initially showed no interest were persuaded by expressive voices and the consistency of nonverbal cues to become enthusiastic about the content and engage with it. These findings offer practical insights into digital communication strategies in sales, education, and advertising, particularly for customizing approaches based on initial audience interest.