An optimization approach to resolve activity scheduling conflicts in ADAPTS activity-based model

Abstract

Activity conflict resolution as the core of scheduling process in activity-based modeling is a challenging step because the activity diary databases mostly report the outcome of the scheduling decisions and often fail to capture key factors influencing the resolution process itself. Consequently, most activity-based frameworks ignore modeling this process by using either predefined set of activity patterns or priority-based assumptions to schedule daily activities and prevent conflict occasions. ADAPTS is one of the few activity-based models that attempts to simulate the process of activity scheduling and resolve the conflicts as they occur. This paper advances the current rule-based conflict resolution model of ADAPTS by implementing an advanced and flexible non-linear optimization model. A set of linear optimization sub-models is then proposed that together perform the same task as the non-linear model, however they are much easier to implement and maintain, while fast to run and flexible to extend. The proposed approach defines an objective function, which aims to minimize the extent of changes in timing and duration of conflicting activities, while fitting them in the schedule. Comparing performance of the proposed model with TASHA scheduler and former resolution module of ADAPTS using CHASE scheduling process data reveals significant improvement in fitting the newly planned activities in the schedules with the minimal modifications in the timing and duration of activities.