A GRASP to solve the multi-constraints multi-modal team orienteering problem with time windows for groups with heterogeneous preferences. (December 2021) - Ruiz-Meza, José; Brito, Julio; Montoya-Torres, Jairo R.
Graphical abstract: Highlights: A new variant of TOP for groups with heterogeneous preferences is presented. Multi-constraint, multi-modal, and heterogeneous preference variants added to the TOP. A MIP model for the new TOP is formulated and solved by obtaining exact solutions. An adapted GRASP is developed that includes two steps for initial greedy solutions. The results of applying the exact procedure and the approximate GRASP are tested. Abstract: Improving the travel experience is a goal of tourist destinations. Tourists demand information and services that help plan and organise the trips adapted to their preferences and resources. Intelligent systems, recommendation systems, and electronic tourist guides can play a decisive role in tourist satisfaction and shaping the offer at the destination. These systems must satisfy the interests of tourists which travel alone or as a group. However, the development of these tools to generate group tourism itineraries is still limited. Group itineraries must also consider individual preferences and the selection of transport modes. The problem associated with the construction of tourist routes is called the Tourist Trip Design Problem. In this work, an extension of the Team Orienteering Problem with Time Windows is developed to model tourism planning. The model considers the construction of group routes, a cost and time limit associated with each participant, the selection of the mode of transport to go from one location to another, and heterogeneous preferences in the group. The model aims to maximise the preferences of group members by considering real and challenging constraints that increase complexity and avoid solutions in polynomial time. This is a novel model, and we generate sets of instances to assess the accuracy of our solution approach. A Greedy Randomised Adaptive Search Procedure is proposed to solve the model. Evaluation criteria based on time, cost, and preferences are used to guide the candidate's selection in the construction phase. The results provided by our meta-heuristic are compared with those obtained by solving the Mixed Integer Programming formulation with exact solver. Computational results show the effectiveness and efficiency of the proposed approach.