In recent years, the Vehicle Routing Problem (VRP) has become an important issue for distribution companies. Also, the rapid development of communication means and the appearance of reverse logistics have given rise to new variants of the VRP. This article deals with an important variant of the VRP which is Dynamic Vehicle Routing Problem with Simultaneous Delivery and Pickup (DVRPSDP), in which new customers appear during the working day and each customer requires simultaneous delivery and pickup. A Memetic Algorithm (MA) that combines Genetic Algorithm (GA) and local search procedure have been proposed to solve the problem. The performance of the algorithm is evaluated with the tests carried out on a set of benchmarks found in the literature. The proposed memetic algorithm is very efficient and gives many good solutions.

This research investigates the impact of stimulus, capability, collusion, opportunity, rationalization, ego, lack of empathy, and spirituality on financial reporting fraud. This chapter also explores how social media can moderate these impacts as a channel for whistleblowing information. Based on balanced panel data with a sample of 441 research data, the research focuses on commercial banks registered with the Financial Services Authority, Indonesia, from 2016 to 2022. The research results reveal that opportunity has a negative influence on fraudulent financial reporting; lack of empathy also has a negative influence on fraudulent financial reporting; lack of spirituality has a positive influence on fraudulent financial reporting; social media as an information channel for the whistleblowing system weakens the influence of lack of spirituality on fraudulent financial reporting, and social media strengthens the influence of opportunity and lack of empathy on fraudulent financial reporting. This research not only aims to bridge the research gap regarding financial reporting that contains fraud (fraud theory) but also provides practical insights and recommendations for regulators, especially in monitoring financial reporting that contains fraud in the banking sector. Hence, these findings are very relevant and can be applied.

Internal supply chains exist in many global enterprises, where manufacturing tasks and sales jobs operate separately, but the management needs to integrate their financial performance reports. In addition, the fabrication planning must meet specific operational goals, such as meeting external clients’ requirements on quality and short order due dates, avoiding internal fabricating interruptions due to inevitable equipment breakdowns, and minimizing overall manufacturing and stock holding costs. Motivated by helping multinational corporations deal with the issues mentioned earlier, this study aims to optimize a finite production rate (FPR)-based supplier-retailer cooperative problem with multi-shipment, rework, subcontracting, probabilistic failure, and expedited rate. Wherein using an outsourcer and expedited-rate help shorten the needed batch producing time significantly; the rework of defects and corrective action on unanticipated breakdown assist in up-keeping the quality and avoiding fabricating delay. We develop an FPR-based model to cautiously represent the considered manufacturing features and activities involved in transporting end products and retailers’ stock holding. Model’s formulating and investigating assists us in gaining the function of operating costs. In addition, optimization procedures with a proposed algorithm help us verify its convexity and decide the model’s best fabricating runtime solution. Finally, we validate how this study works and what important information our model can disclose using a numerical example to facilitate management’s decision-making to end our work.

Supply chain resilience has rapidly expanded as a research area due to increased vulnerability to disruptions and uncertainties. Integration and re-engineering are essential components of a resilient supply chain that can improve its performance. Nevertheless, no one has yet investigated the effect of Artificial intelligence (AI) on the relationship between integration and re-engineering. Therefore, this study aims at investigating the roles of supply chain integration and re-engineering on supply chain performance. Similarly, it investigated the moderating role of AI in these relationships. This study develops a theoretical framework based on resource-based view and the social construction of technology theory. Based on a quantitative study of 564 responses collected from supply chain and clinical unit managers in the Qatari public healthcare sector, an empirical analysis was made using the partial least squares (PLS) path modelling technique. Results revealed that supply chain integration and re-engineering positively affect supply chain performance. Most significantly, these relationships are found to be positively moderated by AI. This study confirms the impact of supply chain integration and re-engineering on performance, providing empirical evidence for AI's role in strengthening these relationships.

Artificial neural networks (ANNs) are one of the artificial intelligence techniques used in real-world problems and applications encountered in almost all industries such as education, health, chemistry, food, informatics, logistics, transportation. ANN is widely used in many techniques such as optimization, modelling, classification and forecasting, and many empirical studies have been carried out in areas such as planning, inventory management, maintenance, quality control, econometrics, supply chain management and logistics related to ANN. The most important and just as hard stage of ANNs is the learning process. This process is about finding optimal values in the search space for different datasets. In this process, the values generated by training algorithms are used as network parameters and are directly effective in the success of the neural network (NN). In classical training techniques, problems such as local optimum and slow convergence are encountered. Meta-heuristic algorithms for the training of ANNs in the face of this negative situation have been used in many studies as an alternative. In this study, a new hybrid algorithm namely MVOSANN is suggested for the training of ANNs, using Simulated annealing (SA) and Multi-verse optimizer (MVO) algorithms. The suggested MVOSANN algorithm has been experimented on 12 prevalently classification datasets. The productivity of MVOSANN has been compared with 12 well-recognized and current meta-heuristic algorithms. Experimental results show that MVOSANN produces very successful and competitive results.

This study examines the impact of social and economic factors on community adherence to COVID-19 health protocols in Surabaya, Indonesia, through a supply chain management perspective. It applies the five-component supply chain model encompassing supply chain policies, governance structures, consumer attitudes, process efficiency, and the integration of culture/technology. The primary data is derived from a survey of 119 participants, supplemented by secondary data on national health protocols and local COVID-19 cases. The analysis reveals critical gaps in compliance with health protocols, particularly regarding mask usage, social distancing, and avoiding crowded spaces. Specifically, only 17.6% of religious adherents follow these protocols, while 82.3% do not. In traditional markets, compliance stands at 19.2%, while 80.8% of participants ignore the guidelines. Among the youth, only 12.4% adhere to the protocols, with 87.6% disregarding them. The study highlights the need to improve the supply chain of public health interventions, from awareness campaigns (demand generation) to efficient delivery systems (process optimization) and monitoring mechanisms (evaluation and feedback loops). Emphasizing a supply chain approach, the findings suggest that strengthening the upstream (policy and governance), midstream (public behavior and attitudes), and downstream (cultural and technological adaptations) components can enhance compliance rates and reduce COVID-19 transmission. The study concludes with actionable recommendations, such as increasing public health awareness, strengthening governance frameworks, targeting interventions for vulnerable groups, and fostering multi-stakeholder partnerships to create a resilient health compliance supply chain in Surabaya, Indonesia.

Business Process Automation (BPA) is widely recognized for its potential in enhancing process efficiency, reducing costs, and boosting customer satisfaction, ultimately driving organizational success. This study makes a significant contribution to the existing knowledge on implementing Quality Management Systems (QMS) through BPA by offering a detailed analysis of their integration. By examining the motivations, strategies, and impacts, this research provides valuable insights for practitioners, academics, and decision-makers who aim to optimize quality management practices through automation. The findings demonstrate remarkable improvements in performance efficiency, error reduction, and flexibility in upgrading enterprise management systems. However, challenges related to technology integration and change management require careful planning and strategic alignment. This study offers critical insights for service-oriented organizations, highlighting the transformative potential of BPA in revolutionizing quality management practices and providing a comprehensive roadmap for organizations seeking operational excellence. Future research should focus on cross-industry comparisons and longitudinal studies to assess the sustainable impact of BPA. This research significantly enhances the literature on BPA and QMS, presenting both practical and theoretical implications.

Batch adsorption and desorption kinetic experiments of fenitrothion on clay soil and sandy clay loam soil indicated that the equilibration time was approximately 30 hours. The kinetics of adsorption and desorption exhibited two distinct stages: a rapid process in the initial stages followed by a slow process. The pseudo-first-order model followed by the Elovich kinetic model fit the experimental adsorption and desorption data quite well, with high values of R2 and low values of ∆qe% and SSE. Accordingly, the pseudo-first-order model is most suitable for describing the adsorption and desorption kinetics of fenitrothion on clay soil and sandy clay soil. Pseudo-second-order model type-1 and type-2 models fit the experimental adsorption data; however, these models cannot be used to describe desorption kinetics. Moreover, the modified Freundlich model has limited applicability, and the intraparticle diffusion kinetic model cannot describe the kinetics of the adsorption and desorption of fenitrothion on clay and sandy clay loam soils.

This paper considers simultaneously two areas of facility location and assortment planning in a competitive environment. In fact, a chain store that has competitors in the market locates a new facility. As there are different products in the market that can substitute with each other, it is intended to determine the best product assortment as well. An integer nonlinear programming problem is proposed to model the mentioned subject. For solving the model, the problem is reformulated as a mixed integer linear programming one. Therefore, a MIP solver software can be used for solving the small- and medium-size problems. For large-scale problems, a firefly algorithm is designed for obtaining a satisfactory solution. By using the proposed model, it is numerically shown that, in addition to the optimal location, it is also necessary to determine simultaneously the best product assortment for the new store. Actually, comparison results reveal that the location significantly affects the assortment scenarios for the new store. In other words, the selection of new store locations may lead to loss of large profit if the assortment planning is neglected.

This study aims to explore how service quality and police performance contribute to building public trust and enhancing overall satisfaction. In an era where public perception of law enforcement is critically important, understanding the key factors that influence trust in the police is essential. The research involved 306 police officers actively serving in the field, with data collected through a questionnaire distributed via Google Forms. The primary data were analyzed using the Structural Equation Modeling Partial Least Squares (SEM-PLS) method. The analysis results indicate that the service quality of police officers does not directly enhance public satisfaction, as police performance is not adequately supported by service quality. However, police performance significantly contributes to public satisfaction, as this effect is strengthened by trust as a moderating factor. Additionally, police performance does not act as a mediating factor in the relationship between service quality and public satisfaction.