E. Marchitto

A Bus Driver Scheduling Problem: a new mathematical model and a GRASP approximate solution

This paper addresses the problem of determining the best scheduling for Bus Drivers, a

Integrating support vector machines and neural networks

\mathcal{NP}

Solving a bus driver scheduling problem with randomized multistart heuristics

-hard problem consisting of finding the minimum number of drivers to cover a set of Pieces-Of-Work (POWs) subject to a variety of rules and regulations that must be enforced such as spreadover and working time. This problem is known in literature as Crew Scheduling Problem and, in particular in public transportation, it is designated as Bus Driver Scheduling Problem. We propose a new mathematical formulation of a Bus Driver Scheduling Problem under special constraints imposed by Italian transportation rules. Unfortunately, this model can only be usefully applied to small or medium size problem instances. For large instances, a Greedy Randomized Adaptive Search Procedure (GRASP) is proposed. Results are reported for a set of real-word problems and comparison is made with an exact method. Moreover, we report a comparison of the computational results obtained with our GRASP procedure with the results obtained by Huisman et al. (Transp. Sci. 39(4):491–502, 2005).

A new meta-heuristic for the Bus Driver Scheduling Problem: GRASP combined with Rollout

Support vector machines (SVMs) are a powerful technique developed in the last decade to effectively tackle classification and regression problems. In this paper we describe how support vector machines and artificial neural networks can be integrated in order to classify objects correctly. This technique has been successfully applied to the problem of determining the quality of tiles. Using an optical reader system, some features are automatically extracted, then a subset of the features is determined and the tiles are classified based on this subset.

Autoregression and artificial neural networks for financial market forecast

The Bus Driver Scheduling Problem is an extremely complex part of the Transportation Planning System (e.g., [12]) that generally can be divided in different subproblems due to its complexity: Timetabling, Vehicle Scheduling, Crew Scheduling (Bus and Driver Scheduling), and Crew Rostering (see Figure 1 for the relationship between these subproblems). The transportation service is composed of a set of lines that corresponds to a bus traveling between two locations of the same city or between two cities. For each line, the frequency is determined by the demand. Then, a timetable is constructed, resulting in journeys characterized by a start and end point, and a start and end time. The Vehicle Scheduling Problem consists in finding a schedule for the buses, each schedule being defined as a bus journey starting at the depot and returning to the same depot. The objective is to minimize the total cost given by the cost of buses used for the service and running costs. Running costs can be minimized avoiding unnecessary deadheads, i.e., trips carrying no passengers. The daily schedule of each single bus is known as a running board (or vehicle block).

Pianificazione, programmazione e gestione innovativa per il trasporto pubblico locale

The bus driver scheduling problem (BDSP) is one of the most important planning decision problems that public transportation companies must solve and that appear as an extremely complex part of the general transportation planning system. It is formulated as a minimization problem whose objective is to determine the minimum number of driver shifts, subject to a variety of rules and regulations that must be enforced, such as overspread and working time. In this article, a greedy randomized adaptive search procedure (GRASP) and a rollout heuristic for BDSP are proposed and tested. A new hybrid heuristic that combines GRASP and rollout is also proposed and tested. Computational results indicate that these randomized heuristics find near-optimal solutions