On the tour planning problem

Abstract

Increasingly, tourists are planning trips by themselves using the vast amount of information available on the Web. However, they still expect and want trip plan advisory services. In this paper, we study the tour planning problem in which our goal is to design a tour trip with the most desirable sites, subject to various budget and time constraints. We first establish a framework for this problem, and then formulate it as a mixed integer linear programming problem. However, except when the size of the problem is small, say, with less than 20-30 sites, it is computationally infeasible to solve the mixed-integer linear programming problem. Therefore, we propose a heuristic method based on local search ideas. The method is efficient and provides good approximation solutions. Numerical results are provided to validate the method. We also apply our method to the team orienteering problem, a special case of the tour planning problem which has been considered in the literature, and compare our method with other existing methods. Our numerical results show that our method produces very good approximation solutions with relatively small computational efforts comparing with other existing methods.

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

“…Reference [14] uses a clustering algorithm based on location and tourist preferences and then computes the route using a greedy algorithm on those clusters. Other works iteratively construct the tour plan based on successive refinements of the initial user plan [9,15,16,27] and the solution is approximated via metaheuristics. References [15,27] use genetic algorithm whereas [16] uses a local search heuristic and [8] a binary search tree heuristic.…”

“…Other works iteratively construct the tour plan based on successive refinements of the initial user plan [9,15,16,27] and the solution is approximated via metaheuristics. References [15,27] use genetic algorithm whereas [16] uses a local search heuristic and [8] a binary search tree heuristic. The common term in the literature for these problems is tourist trip design problem (TTDP) or, simply, Orienting Problem [30].…”

Optimal duty rostering for toll enforcement inspectors

“…Therefore, a maximization problem with limited resources has to be solved. Since we assign a profit value to each motorway section to control, the TEP relates to a team orienteering problem (TOP) or a selective vehicle routing problem, see Archetti et al (2014); Zhu et al (2012). In the case of only one vehicle this is known as the Orienteering problem, a variant of the TSP with profits.…”

Route planning problem under fuzzy sightseeing times and satisfaction values of sightseeing places

“…It is important to develop a decision support system for the tour planning to suit personal satisfactions under various constraints. The tour planning is a multimodal and complex constructive activity affected by various factors, which can be classified into the following categories [19]; (1) personal features including socioeconomic and psychological factors such as age, education, income, experience, personality, and involvement, (2) travel features such as travel purpose, number of travelers, length of travel, distance, and transportation mode, (3) suitable management considering traveling and sightseeing times for the effective utilization of sightseeing. Thus, the tour planning should be prepared in advance, considering the above-mentioned factors and parameters such as transportation networks, personal context, properties of activities, and traveling and sightseeing times.…”