# Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows
## 7 Experimental Results  
### Test Instances  
To evaluate and compare the proposed algorithms, we have created test instances containing data related to the city of Athens, Greece ([[http://dgavalas.ct.aegean.gr/public/aop_instances/instance.zip](http://dgavalas.ct.aegean.gr/public/aop_instances/instance.zip)]([http://dgavalas.ct.aegean.gr/public/aop_instances/instance.zip](http://dgavalas.ct.aegean.gr/public/aop_instances/instance.zip))). Our topology contains 18 scenic routes and 113 points of interest (POIs) that have been compiled from various tourist portals ([[http://www.tripadvisor.com/](http://www.tripadvisor.com/)]([http://www.tripadvisor.com/](http://www.tripadvisor.com/)), [[http://index.pois.gr/](http://index.pois.gr/)]([http://index.pois.gr/](http://index.pois.gr/))) and web services offering open APIs ([[https://developers.google.com/places/documentation/](https://developers.google.com/places/documentation/)]([https://developers.google.com/places/documentation/](https://developers.google.com/places/documentation/))). We have also included 100 hotels in our topology. The hotels are used as starting and ending locations of daily tourist walks. Therefore, the graph consists of 249 nodes, _i.e._, the endpoints of the scenic routes (36 nodes) plus the 113 POIs and the 100 hotels. The travel costs between the locations of the topology were calculated using the OpenTripPlanner project ([[http://www.opentripplanner.org/](http://www.opentripplanner.org/)]([http://www.opentripplanner.org/](http://www.opentripplanner.org/))).  
The attributes of the POIs (time windows, visiting times and profits) are chosen as follows (see also Table 1):* 20% of POIs are open all day long in both weekdays and weekends, _i.e._, their time window is 0-1439, while they have profit and visiting time between 15-30.
* 20% of the POIs have time windows 540-960 for weekdays and are closed in weekends, while their profit and visiting time is between 30-60.
* 20% of POIs have time windows 540-960 for weekdays and weekends, while their profit is between 70-100 and the visiting time is between 60-120.
* 20% of POIs have time windows 840-1140 for weekdays and weekends, while their profit and visiting time is between 30-60.
* 20% of POIs have time windows 480-780 for weekdays and are closed in weekends, while their profit is between 30-60.  
The attributes of the scenic routes (time windows and profits) are chosen as follows:  
* 25% of them are open all day long (their time windows is between 0-1439) for each day of the week.
* 25% of them have time windows 540-960 for weekdays and weekends.
* 25% of scenic routes have time windows 480-840 for weekdays and weekends.
* 25% of the scenic routes have time windows 540-960 for weekdays and are closed in weekends.
* All scenic routes have profit between 10 and 50.  
Solutions of 1, 2, 3 and 4 walks are required. For each case, 100 test instances are considered, each with starting/ending location one of the hotels. In particular, pref100-pref199 preferences ask for solutions of 1 walk, while pref2*(pref200-pref299), pref3*(pref300-pref399) and pref4*(pref400-pref499) ask for \(2,3\) and \(4\) walks, respectively. Considering the starting/ending time of each walk in an instance, we assign the starting time to a randomly chosen time between 480-600, while we assign a randomly chosen number between 840-1080 to the ending time. Note, that for instances requiring more than 1 walk, different walks have the same starting/ending location, however they usually have different starting/ending times.