In this research, an extension of TOPTW called Set Team Orienteering Problem with Time Windows (STOPTW) is developed. In STOPTW, customers are grouped into clusters, and each clusters are associated with profit that will be collected if at least a customer is visited in the cluster. The objective of STOPTW is to find the best route that can maximize the total collected profit without violating given time windows. A mathematical model is formulated to address this problem. Furthermore, an adaptive large neighborhood search is proposed to solve this problem. Two type of datasets originated from TOPTW benchmark dataset are generated for this research, called set A (small dataset with up to 20 clusters) and set B (large dataset with up to 100 clusters). STOPTW finds it applications in mass distribution where distributor need to find the best routes to deliver products to customers in a chain (e.g. convenience store chain). It is more preferable to have a delivery during store leisure time, so distributor need to delivers in between the provided time by customers.

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