Proposal for Management and Control of Intermodal Containers Using Monte Carlo Simulation

When examining the increased attention in freight transportation, a necessity for sustainable and efficient transportation system appears. The presence of road and traffic complications as well as environmental concerns forces us to develop or adapt an alternative operational system that allows us to overcome these issues. Moreover, the escalation in fuel prices in addition to driver issues for road-only haulage forms an unprofitable and unreliable approach. In this on-going MBA project, container management issues are addressed. A number of delays and their casuals are discussed and analysed. Moreover a Monte Carlo simulation framework is proposed to be used to analyse the interaction of different loading and unloading resources and containers within intermodal container management

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed