Buying and implementing a Geographic Information System (GIS) to efficiently organize the operations of a tranporter can be costly. There are two main concerns when a carrier seek GIS technologies to support his operations: (1) accurate maps and powerful address matching and (2) flexibility to adapt the optimization tools to its context. The importance of the second need has pushed the transporter to seek sophisticated GIS at a high price, preventing small carrier from accessing these systems. However, the situation is changing since the cheaper GIS software are now not only offering accurate maps and powerful address matching capabilities but also some programming capabilities. This paper presents a frontline GIS application for a school-bus carrier operating in the Montreal, Canada area. After presenting the private school-bus problem, we demonstrates that we can effectively support the bus operations only using Microsoft Excel and MapPoint. We discuss the performance of the application, keeping in mind the low cost of the GIS.

Two new construction heuristics and a tabu search metaheuristic will be presented for the truck and trailer routing problem, a variant of the vehicle routing problem. Computational results indicate that the heuristic outperforms the previously reported approaches. The algorithm obtained new and better solutions for each of 21 benchmark problems.

This paper presents an algorithm that builds on the Savings based Ant System presented at the Optimization Days last year and enhances its performance in terms of computational effort. This is achieved by decomposing the problem and solving only the much smaller subproblems resulting from the decomposition. The computational study and statistical analysis conducted both on standard benchmark problem instances as well as on new large-scale Vehicle Routing Problem instances will show that the approach not only improves the effciency, but also improves the effectiveness of the algorithm leading to a fast and powerful problem solving tool for real-world sized Vehicle Routing Problems.