The tourist trip design problem (TTDP) is a well-known extension of the orienteering problem, where the objective is to obtain an itinerary of points of interest for a tourist that maximizes his/her level of interest. In several situations, the interest of a point depends on when the point is visited, and the tourist may delay the arrival to a point in order to get a higher interest. In this paper, we present and discuss two variants of the TTDP with time-dependent recommendation factors (TTDP-TDRF), which may or may not take into account waiting times in order to have a better recommendation value. Using a mixed-integer linear programming solver, we provide solutions to 27 real-world instances. Although reasonable at first sight, we observed that including waiting times is not justified: in both cases (allowing or not waiting times) the quality of the solutions is almost the same, and the use of waiting times led to a model with higher solving times. This fact highlights the need to properly evaluate the benefits of making the problem model more complex than is actually needed.

Decisions made in areas such as economics, engineering, industry and medical sciences are usually based on finding and interpreting solutions to optimisation problems. When mod4 elling an optimisation problem, it should be clear that people do not make decisions in a vacuum or in isolation from the reality. So, there is always a decision-making context that, in addition to the natural constraints of the problem, acts as a filter on the candidate solutions available. If this fact is omitted, optimal but useless solutions to the problem can be obtained. In this paper, we propose a systematic way of modelling contexts based on fuzzy propositions and two approaches ( a priori and a posteriori ) for solving optimisation problems under their influence. In the proposed a priori approach, the context is explicitly included in the mathematical model of the problem. As this approach may have a limited application due to the increasing number of constraints and their nature, an a posteriori approach is proposed, in which a set of solutions, obtained by any means (like exact algorithms, simulation or metaheuristics), are checked for their suitability to the context by using a multi-criteria decision-making methodology. A simple fish harvesting problem in a sustainability context and a tourist trip design problem in a pandemic context were solved for illustration purposes. Our results provide researchers and practitioners with a methodology for more effective optimisation and decision-making.

Traffic congestion is a frequent problem on most urban roads. This may be due to incorrect configuration of traffic signals but planning analysis should also include a study of human behavior, which, often imprudent, contributes to traffic congestion. The aim of this paper is to analyze the influence of human factors and their reaction to the environment on the impact of traffic performance through waiting time. For this purpose, an agent-based simulation is developed to represent the autonomous and social behavior of road users. The waiting of vehicles at signals is modeled on the basis of a queuing system. Simulations and experiments are based on the analysis of the age of the people and the condition of the pavement. Results show that people’s age is the most important factor influencing their behavior on the road. It is also shown external factors that also affect driver response and thus signal impact, such as the condition of the pavement. Finally, traffic performance, measured by waiting time, depends strongly on the behavior of people facing signals, according to their characteristics and factors present in the environment.

The maximal covering location problem attempts to locate a limited number of facilities in order to maximize the coverage over a set of demand nodes. This problem is NP-Hard and it has been often addressed by using metaheuristics, where the execution time directly depends on the number of evaluations of the objective function. In this article, the principles of efficient discarding and partial evaluation are applied to obtain more efficient versions of the objective function of this problem, i.e. not-approximate surrogate objective functions. An experimental study is presented to compare the surrogate functions in terms of number of distance comparisons and runtime. The results show that (on average) the best surrogate function is more than 5 times faster than the original function in general, and more than 8 times faster in the largest instances. This proposal allows for a more efficient metaheuristic solution based on swap operators.

We focus here in the deployment of Wi-Fi hotspots (Internet access points) in several locations of Havana city, Cuba. The key point to consider is that the availability of the locations is related to the risk of sea flooding in the littoral of the city.We propose to model the problem as a Maximal Coverage Location Problem (MCLP) where the availability of locations is considered in fuzzy terms, thus leading to fuzzy constraints. Using real data from the Havana city, we solve the problem with a parametric approach using a metaheuristics portfolio. An extensive analysis of the solutions shows the benefits of the proposal.