Abstract: The Team OrienteeringProblem with Time Windows (TOPTW)
deals with deriving a number of tours comprising a subset of candidate
nodes (each associated with a \prot" value and a visiting time window)
so as to maximize the overall \prot", while respecting a specied time
span. TOPTW has been used as a reference model for the Tourist Trip
Design Problem (TTDP) in order to derive near-optimal multiple-day
tours for tourists visiting a destination featuring several points of inter-
est (POIs), taking into account a multitude of POI attributes. TOPTW
is an NP-hard problem and the most ecient known heuristic is based on
Iterated Local Search (ILS). However, ILS treats each POI separately;
hence it tends to overlook highly protable areas of POIs situated far
from the current location, considering them too time-expensive to visit.
We propose two cluster-based extensions to ILS addressing the afore-
mentioned weakness by grouping POIs on disjoint clusters (based on
geographical criteria), thereby making visits to such POIs more attrac-
tive. Our approaches improve on ILS with respect to solutions quality,
while executing at comparable time and reducing the frequency of overly
long transfers among POIs.
Abstract: The Time Dependent Team OrienteeringProblem with Time Windows (TDTOPTW) can be used to model several real life problems. Among them, the route planning problem for tourists interested in visiting multiple points of interest (POIs) using public transport. The main objective of this problem is to select POIs that match tourist preferences, while taking into account a multitude of parameters and constraints and respecting the time available for sightseeing in a daily basis. TDTOPTW is NP-hard while almost the whole body of the related literature addresses the non time dependent version of the problem. The only TDTOPTW heuristic proposed so far is based on the assumption of periodic service schedules. Herein, we propose two efficient cluster-based heuristics for the TDTOPTW which yield high quality solutions, take into account time dependency in calculating travel times between POIs and make no assumption on periodic service schedules. The validation scenario for our prototyped algorithms included the metropolitan transit network and real POI sets compiled from Athens (Greece).