RESUMO
Under COVID-19 outbreak, retail operations are seriously threatened. There are lots of cases in which physical stores basically have to stop operating. This creates problems to the firm, its employees, and consumers. Recently, Timberland in Hong Kong and various other brands such as Joyce Boutiques and The North Face have established the "WhatsApp Shopping Service Operation" (WSO) in which consumers can shop by using the well-established communication tool "WhatsApp." Salespeople in stores provide services via WhatsApp to assist the consumers without them having to visit the stores. We collect primary data from real-world cases and theoretically explore WSO. We build a standard consumer utility based model to derive the firm's optimal pricing and employment decisions under different cases. We evaluate the impacts of COVID-19 and values of WSO implementation from the "Worker-Consumer-Company" (WCC) welfare perspective. Our results interestingly imply that WSO is superior to the traditional online channel in terms of keeping business under the pandemic; meanwhile, implementing WSO can help stimulate demand in the physical store under COVID-19. However, whether WSO is effective to help increase the firm's profit and WCC welfare depends on both consumer type' distribution and consumers' fear of infection. When consumers' fear of infection is very polarized (i.e., extremely low or high), WSO is not recommended. We further propose that the government's subsidy for WSO implementation could be an effective way to help the firm improve its profit and WCC welfare. We also check the robustness of our study by extending the model to consider endogenous consumer type, endogenous service level, and WCC-welfare-oriented firm.
RESUMO
This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era.
