This study tackles a complex binary multi-objective optimization problem focused on minimizing the risk of pandemic importation through strategic passenger air traffic management. The approach involves determining whether international connections to destination airports within a specified country should be activated or deactivated over a defined time frame, considering epidemiological, economic, and socio-political impacts. We introduce a preliminary decision support system designed to assist decision-makers in the parametrization of the problem and quantify their preferences, thereby facilitating the derivation of a compromise solution via a binary particle swarm optimization (BPSO) metaheuristic. The standard BPSO is prone to particles getting trapped in local optima instead of searching for new solution and does not handle infeasible solutions properly. To overcome these inherent limitations, we propose an enhanced version of the BPSO metaheuristic. This enhanced algorithm incorporates novel mechanisms to promote solution space exploration and a robust strategy for managing infeasible solutions. A rigorous comparative analysis is conducted to evaluate the performance of the enhanced BPSO against both the original BPSO and several established state-of-the-art metaheuristics utilizing three benchmark datasets of a constrained problem. Finally, the effectiveness of the proposed enhanced metaheuristic is demonstrated in the context of the pandemic importation risk reduction problem, where it outperforms the original BPSO.

In this paper, we use Monte Carlo simulation techniques to conduct a comparative analysis of the cardinal sum reciprocal method (CSR) and the full consistency method (FUCOM). CSR and FUCOM are two multi-attribute utility/value theory (MAUT/MAVT) weighting methods. They are based on ordinal information, i.e., a ranking of criteria arranged in descending order of importance, where decision makers use a semantic scale or criteria ratios, respectively, to provide additional information in order to establish the strength of differences between the weights of consecutive criteria in the ranking. We consider several scenarios accounting for different numbers of alternatives and criteria and use the hit ratio and the rank-order correlation (Kendall’s τ) as quality measures. We conclude that CSR outperforms FUCOM in all scenarios and for all quality measures.

The air traffic system has been identified as one of the main channels of the global spread of COVID-19 during the early stages of the pandemic, and entry control is crucial for mitigating its transmission and reducing the impact on the population. International airspace closure/opening plays a key role during the early stages of the spread of a pandemic. This is a multi-objective optimization problem where, aside from epidemiological concerns, different conflicting economic and social impacts must be taken into account, such as economic losses for airlines, airports and the regions in which they are located or restrictions on the mobility of the population and connectivity issues. We propose the NSGA-III metaheuristic to solve this airspace closure/opening problem and achieve a good approximation of the Pareto frontier. We then add expert preferences to the problem to derive a compromise solution. The proposed approach is illustrated using the example of Spain with 38 national airports and 569 international connections involving 1196 flights.

This paper deals with a variation of the air traffic controller (ATCo) work shift scheduling problem focusing on the tactical phase, in which the plan for the day of operations can be modified according to real-time traffic demand or other possible incidents (one or more ATCos become sick and/or there is an increase in unplanned air traffic), which may lead to a new sectorization and a lower number of available ATCos. We propose a new methodology consisting of two phases. The goal of the first phase is to build an initial possibly infeasible solution, taking into account the sectors that have been closed or opened in the new sectorization, together with the ATCos available after the incident. In the second phase, we use the simulated annealing (SA) and variable neighborhood search (VNS) metaheuristics to derive a feasible solution in which the available ATCos are used and all the ATCo labor conditions are met. A weight additive objective function is used in this phase to account for the feasibility of the solution but also for the number of changes in the control center at the time the incident happens and the similarity of the derived solution with templates usually used by the network manager operations center.

A/B Testing can be used in digital contexts to optimize the e-commerce purchasing process so as to reduce customer effort during online purchasing and assure that the largest possible number of customers place their order. In this paper we focus on the checkout process. Most of the companies are very interested in agilice this process in order to reduce the customer abandon rate during the purchase sequence and to increase the customer satisfaction. In this paper, we use an adaptation of A/B testing based on multi-armed bandit algorithms, which also includes the definition of alternative stopping criteria. In real contexts, where the family to which the reward distribution belongs is unknown, the possibilistic reward (PR) methods become a powerful alternative. In PR methods, the probability distribution of the expected rewards is approximately modeled and only the minimum and maximum reward bounds have to be known. A comparative numerical analysis based on the simulation of real checkout process scenarios is used to analyze the performance of the proposed A/B testing adaptations in non-Bernoulli environments. The conclusion is that the PR3 method can be efficiently used in such environments in combination with the PR3-based stopping criteria.

In this paper, we account for a digital marketing content recommendation system, called campaign management, used by marketers to create specific digital content that can be issued or configured for viewing by certain population segments according to a series of business variables, user profile or behavior. We analyze the most representative allocation strategies to deal with the multi-armed bandit problem in a context with delayed rewards by means of a numerical study based on a discrete event simulation. Both batch mode and online update architectures are considered for feedback from the different contents displayed to users.

It is very common to use linguistic term scales, whose terms are previously associated with different fuzzy numbers to assign probabilities to events in decision-making processes. However, the rigidity of such scales generates bias in the probability elicitation process and does not allow experts to adequately express their probabilistic judgments. We propose a betting and lottery-based method for eliciting a fuzzy number from the expert that represents his/her probabilistic judgments for a given event, along with a quality measure of the probabilistic judgments based on precision and consistency measures. We also enact a simulation process to analyze possible biases in the proposed fuzzy probability elicitation method.

In this paper we propose a new approach to solving the air traffic controller (ATC) work shift scheduling problem. This approach that minimizes the number of ATCs required to cover a given airspace sectoring, while satisfying a set of ATC labor conditions. First, initial feasible solutions are built using a heuristic, and then multistart simulated annealing is used to reach optimal solutions. In the search process, we use regular expressions to check the feasibility of the visited solutions. This provides high testing speed. Once the optimal ATC number is reached, it is used as the initial solution for a new optimization process aimed at balancing the ATC workloads.

Different allocation strategies can be found in the literature to deal with the multi-armed bandit problem under a frequentist view or from a Bayesian perspective. In this paper, we propose a novel allocation strategy, the possibilistic reward method. First, possibilistic reward distributions are used to model the uncertainty about the arm expected rewards, which are then converted into probability distributions using a pignistic probability transformation. Finally, a simulation experiment is carried out to find out the one with the highest expected reward, which is then pulled. A parametric probability transformation of the proposed is then introduced together with a dynamic optimization, which implies that neither previous knowledge nor a simulation of the arm distributions is required. A numerical study proves that the proposed method outperforms other policies in the literature in five scenarios: a Bernoulli distribution with very low success probabilities, with success probabilities close to 0.5 and with success probabilities close to 0.5 and Gaussian rewards; and truncated in [0,10] Poisson and exponential distributions.

We propose a novel double ant colony system to deal with accessibility issues after a natural or man-made disaster. The aim is to maximize the number of survivors that reach the nearest regional center (center of economic and social activity in the region) in a minimum time by planning which rural roads damaged by the disaster should be repaired given the available financial and human resources. The proposed algorithm is illustrated by means of a large instance based on the Haiti natural disasters in August-September 2008.

Dominance measuring methods are an approach for dealing with complex decision-making problems with imprecise information within multi-attribute value/utility theory. These methods are based on the computation of pairwise dominance values and exploit the information in the dominance matrix in different ways to derive measures of dominance intensity and rank the alternatives under consideration. In this paper we review dominance measuring methods proposed in the literature for dealing with imprecise information (intervals, ordinal information or fuzzy numbers) about decision-makers' preferences and their performance in comparison with other existing approaches, like SMAA and SMAA-II or Sarabando and Dias' method.

In this paper we focus on the selection of safeguards in a fuzzy risk analysis and management methodology for information systems (IS). Assets are connected by dependency relationships, and a failure of one asset may affect other assets. After computing impact and risk indicators associated with previously identified threats, we identify and apply safeguards to reduce risks in the IS by minimizing the transmission probabilities of failures throughout the asset network. However, as safeguards have associated costs, the aim is to select the safeguards that minimize costs while keeping the risk within acceptable levels. To do this, we propose a dynamic programming-based method that incorporates simulated annealing to tackle optimizations problems.

Assets are interrelated in risk analysis methodologies for information systems promoted by international standards. This means that an attack on one asset can be propagated through the network and threaten an organization's most valuable assets. It is necessary to valuate all assets, the direct and indirect asset dependencies, as well as the probability of threats and the resulting asset degradation. These methodologies do not, however, consider uncertain valuations and use precise values on different scales, usually percentages. Linguistic terms are used by the experts to represent assets values, dependencies and frequency and asset degradation associated with possible threats. Computations are based on the trapezoidal fuzzy numbers associated with these linguistic terms.