Developing trust among players in a vendor-managed inventory model for random demand under environmental impact.

Retailers play a vital role in supply chain management because they deal directly with consumers. Occasionally, retailers may cover the entire system's statistics and not disclose these data to the manufacturer. Therefore, asymmetry is generated in the data throughout the system. The main motive of this research was to prevent unreliability throughout the system using a vendor-managed inventory policy. This research shows that by applying a cap and trade policy, the total carbon emitted from the production and transportation sectors can be controlled in the atmosphere. Finally, numerical and sensitivity analyses, along with pictorial representations of various parameters, are performed to examine the optimal results of this study. In addition, the retailer's lead time demand for items is assumed to be random rather than fixed and follows uniform and normal distribution functions. Under these two distribution functions, the optimal retailer lot size, service provided by the retailer to customers, and retailer reorder points are assessed. Furthermore, an evaluation of the total carbon released from an environmental viewpoint is illustrated using numerical findings. The numerical results show that this research is 50.24% more economically beneficial than the methods used in previous studies, whereas the mean value of demand follows a uniform distribution.

A sustainable smart production model for partial outsourcing and reworking.

Smart production plays a significant role to maintain good business terms among supply chain players in different situations. Adjustment in production uptime is possible because of the smart production system. The management may need to reduce production uptime to deliver products ontime. But, a decrement in production uptime reduces the projected production quantity. Then, the management uses a limited investment for pursuing possible alternatives to maintain production schedules and the quality of products. This present study develops a mathematical model for a smart production system with partial outsourcing and reworking. The market demand for the product is price dependent. The study aims to maximize the total profit of the production system. Even in a smart production system, defective production rate may be less but unavoidable. Those defective products are repairable. The model is solved by classical optimization. Results show that the application of a variable production rate of the smart production for variable market demand has a higher profit than a traditional production (52.65%) and constant demand (12.45%).

Recent review of natural and artificial background radiation dosimetry studies in Saudi Arabia.

This paper includes a review of the natural background radiation of the Kingdom of Saudi Arabia. The review deals with natural radioactivity measurements conducted in the past few decades in the Kingdom. The numerous research works reviewed refer to different materials soils processed building material, terrestrial (dwellings) and mining sites. For the measurements, different experimental techniques were adopted. The highest mean specific activity of 238U, 232Th and 40 K in soil samples was found to be 39.0, 25.6, and 343.0 Bq/kg, respectively. While the world average values are 33, 45 and 420 Bq/kg, respectively. For building materials, the highest mean values for 226Ra, 232Th and 40 K were 89, 106 and 773 Bq/kg, respectively. The mean indoor and outdoor dose rates were 455 µGy/y (Riyadh City) and 883 µGy/y (Al-Khamis City), respectively. For the mining sites the mean values for 238U, 226Ra, 228Ra, gross α and gross ß, were 0.12, 0.33, 21, 0.78 and 2.44 Bq/kg, respectively. Based on the available data it is concluded that most of the natural background radiation levels in the measured locations were within acceptable limits, while a few isolated locations showed elevated dose rates. This review suggests that new improved radiological survey methods be employed to cover the entire country, and that areas identified with comparably high dose rates be re-assessed, especially, in dwellings and mining sites.

Product outsourcing policy for a sustainable flexible manufacturing system with reworking and green investment.

Production of defective products is a very general phenomenon. But backorder and shortages occur due to this defective product, and it hampers the manufacturer's reputation along with customer satisfaction. That is why, these outsourced products supply, a portion of required products for in-line production. This study develops a flexible production model that reworks repairable defective products and outsources products to prevent backlogging. A percentage of total in-line production is defective products, which is random, and those defective products are repairable. A green investment helps the reworking process, which has a direct impact on the market demand for products. A classical optimization solves the profit maximization model, and a numerical method proves the global optimal solutions. Sensitivity analysis, managerial insights, and discussions provide the highlights and decision-making strategies for the applicability of this model.

Proactive visual prediction auditing the green eco-safety through backcasting approach booster by grey recruitment priority conceptual framework.

Climate change is a challenge that endangers societal TBL elements' stability. The countries' economies focus on planning for reducing carbon emissions 'CE' and replacing them with low CE energy. This objective needs accurate prediction for CE till 2030 via recording the most significant variables related to CE causes. The variables ( L i q ) are divided into two types tacking in phase I through two steps. The first step classifying the government policies that tackling by the backcasting approach to ranking them. The second step classifies the nature of the energy source which produces CE in Mega ton by SVM. The second phase is fed by phase I outputs to generate a series of prediction values by the LSTM, which is supported by the grey recruitment technique GRPT ( 1 , 1 ) to reduce the forecasting errors. The proposed conceptual framework named (Green Eco-Safety Monitoring; GESM), which considered a methodology gathering the backcasting, SVM, and LSTM provided by GRPT ( 1 , 1 ) in phase II. The paper tracks 21 governorates' CE. Proactive monitoring helps take corrective actions, enhancing the reduction in errors gap to less than 2.4%. The paper reveals that the industrial sector extracting CE at (38.67%) to 2020 with hopeful reduction of 1.72% annually if the government's interested in supporting carbon sinks, which drastically decreased to 1% by 2020 and to 0.72% annually by 2030.

Modeling the relationship between perceived service quality, tourist satisfaction, and tourists' behavioral intentions amid COVID-19 pandemic: Evidence of yoga tourists' perspectives.

Purpose: This study aims to investigate the impact of perceived service quality (PSQ) on tourist satisfaction and behavioral intentions and explore the potential mediating role of tourist satisfaction in the relationship between service quality and behavioral intentions in the yoga tourism context during the COVID-19 pandemic. Further, this is to examine to what extent yoga tourist satisfaction directly affects their behavioral intentions. Design/methodology/approach: Based on a review of literature, the study proposes a conceptual model to test four hypothesized relationships among the constructs of perceived service quality, tourist satisfaction, and behavioral intentions. Data was collected by using a self-administrated questionnaire that was developed and directed to a convenience sample of yoga tourists (380 forms). Structural equation modeling (SEM) was employed to determine the relationship between study constructs. Findings: The results of SEM illustrated that all the hypothesized relationships are supported. The findings confirm that yoga tourists' behavioral intentions are significantly affected directly and indirectly (through tourist satisfaction) by perceived service quality. Additionally, tourist satisfaction significantly partially mediates the relationship between PSQ and tourists' behavioral intentions. Research limitations: The subject of this study was yoga tourists staying in yoga retreats/studios in Egyptian destinations (South Sinai Governorate). Future research may focus on other geographical destinations and other influential variables of yoga tourists' satisfaction and behavioral intentions should be investigated. Practical implications: For improving tourists' satisfaction and behavioral intentions, yoga service providers should take care by giving tourists personalized attention, and understanding, fulfilling their specific needs. Health and hygiene practices must be considered during the COVID-19 pandemic. Originality/value: This study is perhaps the first empirical study that examines the relationship between PSQ and tourists' satisfaction and behavioral intentions in the yoga tourism context. A new integrated conceptual model that combined three service quality dimensions, namely, tangibles, intangibles as well as health and hygiene was developed and validated.

Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics.

The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.