Fleet Management

 

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.

The complexity is clearer in the case of public transport which usually has a scheduled transportation network. They use techniques and ideas from mathematics as well as computer science. Tools and concepts include graph and network algorithms, combinatorial optimizations, approximations and online algorithms, stochastic and robust optimization. Newer models and algorithms can improve the productivity of resources, efficiency, and network capacity. One of the ways to do that has been to leverage a database and use parameterized queries. The order of the data in the database provides just the right framework for the query methods to return an accurate and complete set of results. The results might differ on consistency levels, responsiveness and coverage depending on whether the relational, batch or streaming mode was used.

When the transportation problems were modeled, they were often treated as combinatorial optimization problems which included vehicle routing, scheduling, and network design. These are notoriously difficult to solve, even in a static context. This led to the need for a human dispatcher in many fleet management scenarios. Emergence of powerful computing including meta-heuristics, distributed and parallel computing has now made that somewhat easier. One of the main challenges is the need to handle dynamic data.

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.

Mathematical formulations of this class of problems have bounded certain parameters and changed the criteria to obtain approximate solutions instead of optimal ones because the class of problems is inherently an NP-hard problem. In the last fifteen years, an incremental amount of metaheuristic algorithms has been designed. These include simulated annealing, genetic algorithms, artificial neural networks, tabu search, ant colony optimization, Greedy Randomized adaptive search procedure, Guided local search and variable neighborhood search along with several hybrid techniques. Local search is the most frequently used heuristic technique for solving combinatorial optimization problems. Sequential search is a general technique for the efficient exploration of local search neighborhoods. One of its key concepts is the systematic decomposition of moves, which allows pruning options within the local search based on associated partial gains.