RESUMEN
The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs' shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs' shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster's rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model.
RESUMEN
Aiming at fleet deployment issue and cargo allocation issue under the background of COVID-19 epidemic and the "dual carbon" strategy, in order to meet the requirements of liner companies for the balanced development of fleet transportation efficiency, economic benefits, service quality and environmental benefits, a multi-objective fleet deployment and cargo allocation optimization model was established to achieve the goal of maximizing fleet average space utilization and operating profit, minimizing cargo time value loss and single container carbon emission. According to the internal relationship between subproblems, the model was decomposed into a two-level model. The upper level was mixed integer nonlinear programming to deal with route ship allocation and speed optimization, and the lower level was linear programming to deal with cargo allocation. The solution algorithm was designed based on NSGA-Ⅱ algorithm framework. Taking the fleet of a liner company as example, the results show that the model and the optimization solution method are feasible, and the liner company can adopt the mixed strategy of slightly increasing the speed and increasing the number of small and medium-sized ships to achieve the effect of carbon emission reduction while meeting more freight demand and coping with port congestion. © 2022, Editorial Office of Journal of Dalian Maritime University. All right reserved.
RESUMEN
Bed occupancy ratio reflects the state of the hospital at a given time. It is important for management to keep track of this figure to proactively avoid overcrowding and maintain a high level of quality of care. The objective of this work consists in proposing a decision-aid tool for hospital managers allowing to decide on the bed requirements for a given hospital or network of hospitals on a short-medium term horizon. To that extent we propose a new data-driven discrete-event simulation model based on data from a French university hospital to predict bed and staff requirements. We propose a case study to illustrate the tool's ability to monitor bed occupancy in the recovery unit given the admission rate of ED patients during the pandemic of Sars-Cov-2. These results give an interesting insight on the situation, providing decision makers with a powerful tool to establish an enlightened response to this situation.