Optimizing Personalized Touristic Itineraries by a Multiobjective Evolutionary Algorithm

2016 ◽  
Vol 15 (06) ◽  
pp. 1269-1312 ◽  
Author(s):  
Ivanoe De Falco ◽  
Umberto Scafuri ◽  
Ernesto Tarantino
Keyword(s):  
Weather Forecasting ◽  
Time Windows ◽  
Operating Conditions ◽  
Orienteering Problem ◽  
Environmental Constraints ◽  
Optimal Solutions ◽  
Team Orienteering Problem ◽  
Team Orienteering ◽  
Np Complete ◽  
Electronic Tourist Guide

The paper presents an electronic tourist guide, relying on an evolutionary optimizer, able to plan personalized multiple-day itineraries by considering several contrasting objectives. Since the itinerary planning can be modeled as an extension of the NP-complete team orienteering problem with time windows, a multiobjective evolutionary optimizer is proposed to find in reasonable times near-optimal solutions to such an extension. This optimizer automatically designs the itinerary by aiming at maximizing the tourists’ satisfaction as a function of their personal preferences and environmental constraints, such as operating hours, visiting times and accessibility of the points of interests, and weather forecasting. Experimental evaluations have demonstrated that the proposed optimizer is effective in different simulated operating conditions.

scholarly journals Uncertain Team Orienteering Problem With Time Windows Based on Uncertainty Theory

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63403-63414 ◽  
Author(s):  
Jian Wang ◽  
Jiansheng Guo ◽  
Jicheng Chen ◽  
Shan Tian ◽  
Taoyong Gu
Keyword(s):  
Uncertainty Theory ◽  
Time Windows ◽  
Orienteering Problem ◽  
Team Orienteering Problem ◽  
Team Orienteering

Efficient Cluster-Based Heuristics for the Team Orienteering Problem with Time Windows

2019 ◽  
Vol 36 (01) ◽  
pp. 1950001 ◽  
Author(s):  
Damianos Gavalas ◽  
Charalampos Konstantopoulos ◽  
Konstantinos Mastakas ◽  
Grammati Pantziou
Keyword(s):  
Execution Time ◽  
Time Window ◽  
Time Budget ◽  
Time Windows ◽  
Experimental Tests ◽  
Orienteering Problem ◽  
Solution Quality ◽  
Routing Problem ◽  
Team Orienteering Problem ◽  
Team Orienteering

In the Team Orienteering Problem with Time Windows (TOPTW), a variant of the Vehicle Routing Problem with Profits, a set of locations is given, each associated with a profit, a visiting time and a time window. The aim is to maximize the overall profit collected by a number of routes, while the duration of each route must not exceed a given time budget. TOPTW is NP-hard and is typically used to model the Tourist Trip Design Problem. The latter deals with deriving near optimal multiple-day tours for tourists visiting a destination with several points of interest (POIs). The most efficient known heuristic approach to TOPTW which yields the best solution quality versus execution time, is based on Iterated Local Search (ILS). However, the ILS algorithm treats each node separately, hence it tends to overlook highly profitable areas of nodes situated far from the current solution, considering them too time-expensive to visit. We propose two cluster-based extensions to ILS addressing the aforementioned weakness by grouping nodes on separate clusters (based on geographical criteria), thereby making visits to such nodes more attractive. Our approaches improve ILS with respect to solutions quality and execution time as evidenced by experimental tests exercised on both existing and new TTDP-oriented benchmark instances.

scholarly journals Lion optimization algorithm for team orienteering problem with time window

2021 ◽  
Vol 21 (1) ◽  
pp. 538
Author(s):  
Esam Taha Yassen ◽  
Alaa Abdulkhar Jihad ◽  
Sudad H. Abed
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Real Life ◽  
Search Space ◽  
Orienteering Problem ◽  
Team Orienteering Problem ◽  
Team Orienteering ◽  
Lion Optimization Algorithm

<span>Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.</span>

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