چكيده به لاتين
Today, tourism is one of the most important economic recourses and one of the largest and the most diverse industries in many countries. Therefore having a proper plan in this industry may have cause positive economical, social, and environmental effects in a country. Tourists who are visiting a destination in one or more days, because of the time limit, are facing a decision in choosing more attractive points of interests (POIs) as well as finding a route for their daily visit. On the other hand, costs of their visit and the environmental effects may also be a point of matter in this decision making. In this research a tourist trip design model is formulated which minimizes the total costs of the tour as well as the amount of produced pollutant, in addition to maximizing the total utility of visiting POIs according to personal preferences of the tourist.. Moreover, it is considered that if a tourist may choose among available transportation modes for moving between points of interest. Each of these transportation modes may have a different travel time and cost and Co2 emission factor. This problem is called the Multi-Objective Multi-Modal Green Tourist Trip Design Problem (MO-MM-GTTDP) which is in fact formulated as a new variant of the orienteering problem (OP). Then, the Multi objective mixed integer model is implemented in CPLEX and solved using the ε- constraint method. Furthermore, a Multi-Objective Variable Neighborhood Search (MOVNS) meta-heuristic algorithm is developed and implemented in visual C++ to solve the larger instances of the problem. Taguchi experiments are then used to tune the controllable parameters of the proposed algorithm. New benchmark instances of the problem is generated based on the existed OP instances from the literature. Afterwards, the performance of the presented MOVNS is evaluated according to the multi-objective evaluating criteria by comparing the obtained results with the results obtained by the ε- constraint method. The conclusion is the high quality of the proposed MOVNS algorithm solutions in a practically acceptable computation time (few seconds). In addition, a small case study based on the some real data on a number of POIs in the city of Tehran is generated and used to demonstrate the performance of the proposed model and algorithm in practice. For this case study, , by using the multi attribute decision making method of TOPSIS the obtained non-dominated solutions are ranked and best ones are presented to the tourist.
