Transport On-Demand in a Service Supply Chain Experiencing Seasonal Demand: Managing Persistent Backlogs

Successful transport-on-demand (TOD) requires having sufficient capacity in the right location to meet demand when it occurs. Consumer and recovery vehicle locations are variable, and the vehicle recovery service is contracted out in the service supply chain. This research aims to identify how different variables/factors influence backlogs during busy periods and service performance. A case study of a vehicle recovery company was undertaken using observation and analysis of historical data to map the process. Discrete event simulation (DES) was used to model several processes to evaluate the operational impact of changes. We find that ensuring complete and accurate information transmission over the chain supports the TOD service by enhancing the ‘allocation’ activity of the dispatch center staff; i.e., pairing vehicles to consumer requirements. Simple changes to how information is collected, shared, and used in the service supply chain can significantly reduce the percentage of jobs taking more than a given time

Simulation and Control of Groups of People in Multi-modal Mobility

Tourism and transport are constantly growing and, with it, the movements of travellers. This entails two fundamental effects on which we must focus: control of mass tourism and the organization of transport. Good transport organization and travel planning avoid crowds and therefore mass tourism. This allows promoting sustainable tourism in which it is sought to offer a quality service to tourists taking care of the environment. In this thesis the objective is to manage the flow of groups of people through means of transport. This control of groups of people is aimed at customer satisfaction by offering quality tourism. On the one hand, the study focuses on the problem to mitigate the negative effects due to mass arrivals in touristic locations. A TEN network has been developed to define the optimal tours for different groups of tourists. A related mixed integer quadratic optimization model has been developed with three main objectives: it minimizes the maximum value of occupancy in the selected destinations to limit mass tourism, reduces the divergence between the proposed visit tour and one required by the tourist group and the overall duration of their visit, and a heuristic approach has been introduced. On the other hand, it has been implemented a railway scheduling and rescheduling problem introducing optimization-based and min-max approaches on the regional and high-speed railway network. The scheduling model defines the best schedules for a set of trains considering costumers\u2019 demand and the priority of the trains to cover the rail sections in case of conflict on the railway lines. Consecutively, the generated feasible timetables are used to minimize possible consequences due to events that may negatively affect the real time traffic management. The main contribution of this section is the introduction in the second approach the innovative concept to prioritize the train that can access on the block section in case of conflicts on the network

Incorporating Weather Impact in Railway Traffic Control

Abnormal weather events can have significant impacts on the safety and operational performance of the railways. In Great Britain, weather related train delays run into 1 to 2 million of minutes each year. With the rapid advances in weather forecasting and emerging information technology, the weather forecasting data can be utilised to improve the performance of train control models in dealing with weather events. In this thesis, the forecasted moving weather fronts are map in terms of their temporal and spatial coverage, as well as the corresponding speed restrictions and/or track blockages according to the severity of the weather fronts, onto the railway lines. This enables the control models to consider multiple disruptions in advance of them commencing, instead of dealing with them one by one after they have commenced. Then the proactive train control methods are proposed, i.e. mixed integer liner programming (MILP) and genetic algorithm (GA) for single-track rescheduling in adverse condition, and an MILP model for simultaneous train rerouting and rescheduling model, taking into account forecasted severe weather perturbations. In the models, the forecasted moving weather perturbations on different parts of the rail network are represented as individual constraints, whereby, trains travelling through the adversely impacted zones follow reduced speed limits and in the severely impacted zones where the tracks are blocked, trains need to be rerouted or wait until the blockage disappears. The case studies indicate: a) compared with existing control methods our rescheduling methods have shown to make significant reduction in total train delays (in the case studies examined, an average 21% reduction in delays); b) within the timescale considered, the further ahead the weather forecast information is considered, the less the overall delay tends to be; c) under severe weather disruptions (with track blockage), the proposed rerouting and rescheduling model is shown to be able to effectively and efficiently find a cost effective route and timetable