Fleet Management continued...

The need for fleet management arose from the requirements of passengers and freight transportation services. Usually, their fleet is considered heterogeneous because it includes a variety of vehicles. Some of the fleets must perform tasks that may be known beforehand or are done repetitively. Most of them respond to demand. The scale and size of the fleet can be massive.

Vehicle routing and scheduling is one such class of problems. A fleet of vehicles with limited capacity based at one or several depots must be routed serving a certain number of customers to minimize the number of routes, total traveling time and the distance traveled. Additional restrictions can specialize this class of problems with time windows where each customer is served in a specified time interval. This class of problems is central to the field of transportation, distribution, and logistics.

Dynamic fleet management is another class of problems. While classical fleet management problems address routing and scheduling plans, unforeseen events might force additional requirements. When communication is leveraged to get this additional information, real-time usage of fleet resources can be improved. The changes in vehicle location, travel time and customer orders can be used with an efficient re-optimization procedure for updating the route plan as dynamic information arrives. When reacting to real-time events leaves no time, it can be worked around by finding ways to anticipate future events in an effective way. Data processing and forecasting methods, optimization-simulation models, and decision heuristics can be included to improve comprehensive decision-support systems.

Another field of increasing interest is the urban freight transportation and the development of new organizational models for management of freight. As for any complex systems, city logistics transportation systems require planning at strategic, tactical, and operational levels. While wide area road networks require routing based on distances, that within the city logistics network demands time-dependent travel times estimates for every route section. While static approaches are well studied, time-dependent vehicle routing still appears to be unexplored. One of the ways to bridge this gap has been to use an integration framework that brings dedicated systems together for a holistic simulation that performs something like a dynamic router and scheduler.

Urban public transport deserves to be called a class of problems by itself. It consists of determining ways to provide good quality of service to passengers with finite resources and operating costs. Their planning process often involves 1. Network route design, 2. Frequency setting and timetable development, 3. Vehicle scheduling and 4. Crew scheduling and rostering. Some state-of-the-art models involve tuning the routing and scheduling with minimization of passenger cost functions. Metaheuristics schemes that combine simulated annealing, tabu, and greedy search methods serve this purpose. One of the distinguishing features of this problem space is that customers often formulate two requests per day, specifying an outbound request from pick-up to drop-off and an inbound request for the round trip. Another feature is that the quality of service needs to be maximized while minimizing operating costs incurred to satisfy all the requests.