Distribution of goods is one of the main issues that directly affect the performance of the companies since efficient distribution of goods saves energy costs and also leads to reduced environmental impact. The multi-compartment vehicle routing problem (MCVRP) with a heterogeneous fleet of vehicles is encountered when dealing with this situation in many practical cases. This paper is motivated by the fuel delivery problem where the main objective of this research is to minimize the total driving distance using a minimum number of vehicles. Based on a case study of twenty petrol stations in northeastern Thailand, a novel two-phase heuristic, which is a variant of the Fisher and Jaikumar Algorithm (FJA), is proposed. The study first formulates an MCVRP model and then a mixed-integer linear programming (MILP) model is formulated for selecting the numbers and types of vehicles. A new clustering-based model is also developed in order to select the seed nodes and all customer nodes are considered as candidate seed nodes. The new Generalized Assignment Problem model (GAP model) is formulated to allocate the customers into each cluster. Finally, based on the traveling salesman problem (TSP), each cluster is solved in order to minimize the total driving distance. Numerical results show that the proposed heuristic is effective for solving the proposed model. The proposed algorithm can be used to minimize the total driving distance and the number of vehicles of the distribution network for fuel delivery.

The recent paradigm shift from recruitment of international students to retention of students encouraged radical changes on the consumption of educational services from university serving the students for creation of mutually beneficial relationship between the students and the university. The impact of globalization hit the economy in two paradoxes; first, consumers have more choices but less satisfaction; second, service providers have more strategic options, which yield less value. Against this background, this study proposed and validated destination loyalty model to arrest the increasing students’ attrition at study location. The declining state funding of higher education challenged the management of education sector to design sustainable competitive advantage strategy not only to attract but also to retain students at their study destination. A total of 498 data was solicited from international students at Malaysian public universities, partial least square structural equation modeling (PLS-SEM) was used to test the reliability and validity of items and constructs. The results show that relationship marketing dynamism of service quality, students’ satisfaction, perceive image, perceived value and personal reasons were stable for international student loyalty to study destination. A new model called 2S2P was developed for emerging education destinations. Theoretical and practical implications as well as directions for future studies were documented in the paper.

Small and Medium Enterprises (SMEs) play a major role in most developing countries because they are able to improve and develop the economy. e-Commerce is also an important topic in vari-ous research fields as it brings about benefits to these countries. However, the adoption of e-Commerce by SMEs in developing countries is low due to several factors. Therefore, a parsimony framework has been proposed to investigate the factors which influence the adoption of e-Commerce among SMEs in West Malaysia. Three factors are considered in this research, namely Perceived Barriers, Organization Readiness, and Competitor Pressures. Questionnaires were used for data collection while SmartPLS was used to analyze the data. The results showed that Perceived Barriers contain negative significant influence on the adoption of e-Commerce among medium-sized SMEs, but not for small-sized SMEs. Organization Readiness shows a huge difference between small and medium-sized SMEs. Competitor Pressures are important for both small and medium-sized SMEs. In conclusion, the results obtained will assist and enable stakeholders to restructure e-Commerce applications which fulfill the needs of their organization and therefore, increase the effectiveness of the organization.

Effective transshipment network is currently recognized as an important success determinant for most manufacturing organizations, because the transshipment management has significant impact on cost and environmental impact. Due to the complexity of the multi-criteria transshipment problem for infectious waste management (IWM) for this case, forty hospitals and three candidate disposal municipalities in Northeastern Thailand, a novel holistic approach (combination of fuzzy AHP, transshipment model and DEA) was developed for solving this problem. We first utilized the fuzzy AHP technique to calculate the location weights of each candidate disposal municipalities. Secondly, a new cost-based transshipment model was formulated and solved in order to provide the set of feasible solutions. These solutions can be viewed as decision making units (DMUs), inputs and outputs. Finally, DEA-CCR model was applied to calculate the efficiency scores of candidate DMUs. The study results demonstrated that the proposed holistic approach can help decision makers (DMs) to choose a suitable transshipment network for IWM. The major advantage of the proposed holistic approach is that both costs and environmental impacts under constraints are focused on simultaneously. Future work will apply the developed approach with other real-world complex problems to enhance the validity of the research output further. For large-size transshipment problems in which an exact solution cannot be found, meta-heuristics must be applied.

In production/inventory control systems, the goal of the controller is to generate sophisticated decisions by controlling the order rate and inventory level. This paper aims at modeling a dynamic-stochastic production/inventory control system under two sources of variability (uncertainty) including uncertainties on demand rate and frustrating rate. The study deals with obtaining a robust optimal design of a Proportional-Integral-Derivative (PID) controller in in the stochastic control system. For this purpose, a new robust simulation-optimization method in the class of computational intelligence is proposed. To cope with the unknown distribution of uncertainty, the crossing weighted uncertainty scenarios are combined with the proposed method. Within this study, a new sequential robust efficient global optimization is proposed to make a trade-off between optimal and robustness terms in final optimization results. Finally, a numerical case with simulation experiments is conducted to demonstrate the advantages of the proposed policy in terms of optimal result, robustness, and computational cost.

Customer satisfaction is the bedrock of e-commerce. This study identifies the factors and consequences of the perceived flow of consumers with shopping websites. The research scrutinizes the association linking purchase intentions arising from customer satisfaction, website quality and perceived flow by incorporating the model of Stimulus Organism Response. The data were collected from online shoppers using purposive sampling targeted through social media. The data from 270 respondents have been analyzed using SmartPLS incorporating the Partial Least Square-Structural Equation Modeling. The assessment showed an important beneficial impact of website usability, their security, functionality, and privacy features on the perceived flow of customers. Furthermore, perceived flow has been found to affect customers’ purchase intentions as well as satisfaction, and lastly, purchase intention is induced by how satisfied customers are with their shopping experience. The findings identify how important perceived flow functions in enhancing purchase intentions and customer satisfaction online. The findings can be noteworthy for online businesses and banks to improve online shopping experience and transactions.

Agriculture products are one of the main income sources of developing countries. This study analyzes the road transport system (network) to reveal the network’s structural properties and identifies hubs that consolidate agricultural products to export to China via the China-Laos railway. Analysis of 20 provinces in the Northeast of Thailand has found that the network has 340 districts connected by 1,015 transport routes. The network is sparse, in which all districts cannot be connected to each other. The network has low connectivity efficiency but has high intra-connectivity among districts in the same province. In addition, the network has a modularity structure that can develop the communities. Hubs consolidating agricultural products of the region are Na Khu, Kuchinaria, Mueng Chiyaphum, Nam Phong, Na Wa, Mueang Mukdahan, Prasat and Rasi Salai. The findings of the study draw implications for the government, sector and exporters to design and improve their operations and services.

The purpose of this study is to explore how various online marketing tools, including Content Marketing, Email Marketing, Social Media Marketing, and SEO, affect customers' decisions to purchase Internet services. The study utilized a survey distributed through simple random sampling, with 410 questionnaires given to Zain Telecom customers in Zarqa city. After excluding incomplete questionnaires, 385 were analyzed using multiple regressions, descriptive analysis, and reliability tests. Results showed that all independent variables had a significant impact on customers' purchasing decisions of Internet services, with Email Marketing having the strongest relationship. The study recommends further investment in Email Marketing strategies to improve customer retention and acquisition.

The COVID-19 pandemic has led to different health outcomes, including long COVID (LCo) and mental health (MH) disorders, impacting millions globally. To enable early healthcare diagnosis, including the prediction of MH conditions and LCo, various research studies have utilized machine learning (ML) techniques. However, there is still a gap in understanding the mental health of recovered COVID-19 patients with long COVID using ML techniques. This study aims to bridge this gap by developing and evaluating ML models, including support vector machine, multilayer perceptron (MLP), k-nearest neighbor, gradient boosting, voting classifier, and extreme gradient boosting, tailored for mental health and long COVID datasets from recovered COVID-19 patients. Additionally, feature selection methods, e.g., Recursive Feature Elimination (RFE) and Extra Trees (ET), and optimized models with hyper-parameter tuning will be employed. Our experiments utilize the dataset of recovered COVID-19 patients. Among these ML models, the MLP with ET-based features achieved the highest accuracy and AUC scores in this dataset, with 1.00 and 0.97 ± 0.02, respectively. The research reveals the high prevalence and risk factors of mental health disorders and long COVID from the dataset. These findings will contribute to personalized healthcare strategies for individuals navigating the complexities of post-COVID-19 recovery, integrating machine learning insights into mental health and long COVID support.

This study explores the factors influencing personal brand identity and their impact on successful personal branding. It examines the positive influence of social media usage, self-expression, professionalism, and self-disclosure on personal brand identity, while also considering the inconsistent findings regarding the role of self-efficacy. The study finds that social media usage, self-expression, professionalism, and self-disclosure play crucial roles in developing a strong personal brand identity, which is essential for achieving successful personal branding. However, the findings on self-efficacy's influence on personal brand identity are inconsistent with previous research. This study contributes to the understanding of personal brand identity and provides valuable insights for individuals and organizations seeking to develop and maintain strong personal brands.