No abstract

No abstract

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.

Purpose Risk management is crucial for all organizations, especially those in the global supply chain network. Failure may result in huge economic loses and damage to company reputation. Risk assessment usually involves quantitative and qualitative decisions. The purpose of this paper is to apply fuzzy logic to capture and inference qualitative decisions made in the House of Risk (HOR) assessment method. Design/methodology/approach In the existing HOR model, aggregate risk potential (ARP) is calculated by the risk event times the risk agent value and its occurrence. However, these values are usually obtained from interviews, which may involve subjective decisions. To overcome this shortcoming, a fuzzy-based approach is proposed to calculate ARP instead of the current deterministic approach. Findings Risk analyses are conducted in five major categories of risk sources: internal, global environment, supplier, customer and third-party logistics provider. Moreover, each category is further divided into different sub-categories. The results indicate that the fuzzy-based HOR successfully inferences the inputs of the risk event, risk agents and its occurrence, and can prioritize the risk agents in order to take proactive decisions. Practical implications The proposed fuzzy-based HOR model can be used practically by manufacturers in the global supply chain. It provides a framework for decision makers to systematically analyze the potential risks in different categories. Originality/value The proposed fuzzy-based HOR approach improves the traditional approach by more precise modeling of the qualitative decision-making process. It contributes to a more accurate reflection of the real situation that manufacturers are facing.

No abstract