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Introduction This paper presents a proposal to apply the meta-heuristic Optimization Based on Variable Meshes (VMO) to the discreet problem of the Traveler Seller (TSP); This model explores the search space to Objective from a population of solutions called mesh that expands and contracts in order to find good quality solutions. In this context, the expansion operator is modified in such a way that it is applicable in a discrete domain, making combinations among the solutions in order to obtain new nodes. Another element that is modified is the clearing operator, which is responsible for maintaining the diversity of the mesh in each interaction. Methodology. A study of VMO model parameters is summarized in this paper, using a set of TSP instances with different characteristics; In addition, it can be observed that the proposal of this work obtains competitive results when compared with other international reference algorithms mentioned in the state of the art. The work is structured as follows: Section 1 describes the fundamental aspects of the TSP study problem. Then, in the second, the general functioning of VMO is explained, in the third one, each of the expansion and contraction operators is defined for the study problem. Later in the fourth section a study of the parameters of the proposal and an experimental comparative analysis with the results obtained with other algorithms mentioned in the state of the art is made. Conclusions Other operators of generation of new nodes were applied in the expansion process, where a combination of solutions is made in order to comply with the restrictions imposed by the problem. [ABSTRACT FROM AUTHOR]

This paper presents an alternative procedure to solve the Capacitated Vehicle Routing Problem (CVRP) with homogeneous fleet. The paper proposes a two-phase metaheuristic algorithm: routes design and fleet scheduling. The first phase is based on heuristics and metaheuristics procedures in order to build an initial solution that is then improved using tabu search to obtain non-dominated solutions in polynomial computational time. For the second phase, corresponding to fleet scheduling, the problem is approached using an analogy with the identical parallel machine scheduling problem. This procedure looks for the minimization of the fixed cost of using installed capacity as the objective function. The proposed procedure was tested using both a random-generated instance and real data, giving competitive results in comparison with other heuristics tested. [ABSTRACT FROM AUTHOR]