A column generation algorithm for the demand‐responsive feeder service with mandatory and optional, clustered bus‐stops

With the rise of smart cities, relevant passenger data can be collected to improve quality transport services. In this article, a demand-responsive feeder service is presented. A transports passengers from low-demand area, like sub-urban transportation hub, city center. The modeled in article considers two sets bus stops: mandatory stops and optional stops. Mandatory are always visited by bus, while only when client nearby makes request for transportation. This gives both flexibility some predictability. To optimize performance service, mathematical modeling techniques model's runtime developed. It concluded that combination column generation separation sub-tour elimination constraints decreases computing time small midsize instances significantly.