Extensions of the Activity Chain Optimization Method

For the optimization of daily activity chains a novel method has been elaborated, where flexible demand points were introduced. Some activities are not necessarily fixed temporally and spatially, therefore they can be realized in different times or locations. By using flexible demand points, the method is capable of finding new combinations of activity chains and choosing the optimal set of activities. The optimization algorithm solves the TSP-TW (Traveling Salesman Problem – Time Window) problem with many flexible demand points, which resulted in high complexity and long processing times. Therefore, two extensions were developed to speed up the processes. A POI (Point Of Interest) search algorithm enabled to search demand points in advance and store them in an offline database. Furthermore GA (genetic algorithm) was applied and customized to realize lower optimization times. During the implementation, three different transportation modes were defined: car, public transport, and combined (public transport with car-sharing opportunity). The simulations were performed on arbitrarily chosen test networks using Matlab. Promising test results were obtained for all transportation modes with total travel time reduction of 10–15 percent. The application of the extended optimization method produced shorter activity chains and decreased total travel time for the users.