Comparision of the walk techniques for fitness state space analysis in vehicle routing problem

The Vehicle Routing Problem (VRP) is a highly researched discrete optimization task. The first article dealing with this problem was published by Dantzig and Ramster in 1959 under the name Truck Dispatching Problem. Since then, several versions of VRP have been developed. The task is NP difficult, it can be solved only in the foreseeable future, relying on different heuristic algorithms. The geometrical property of the state space influences the efficiency of the optimization method. In this paper, we present an analysis of the following state space methods: adaptive, reverse adaptive and uphill-downhill walk. In our paper, the efficiency of four operators are analysed on a complex Vehicle Routing Problem. These operators are the 2-opt, Partially Matched Crossover, Cycle Crossover and Order Crossover. Based on the test results, the 2-opt and Partially Matched Crossover are superior to the other two methods.

Univariate and multivariate forecasting of tourism demand using state-space models

Tourism forecasting plays a major role in tourism planning and management and it is one of the main economic activities in many countries. For this reason, it is fundamental to provide several models that allow describing and forecasting the tourist demand. International visitants who arrive at a certain tourist destination may come from countries or regions with similar or different customs and behaviours and therefore be able to present correlated arrival patterns. Based on the state-space methods with time-varying parameters, this study develops the application and comparison of univariate and multivariate models in the applied case of German and British tourist at Canary Islands (Spain). The choice of model can be conditioned by the volume of tourists from one country with respect to the other. Structural models will be used incorporating intervention and exogenous variables, among which airline seat reservations for regular flights.