Abstract
This paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power-flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi-objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small-sized instances. We also proposed a multi-objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade-off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4-6% increase), while at the same time allowing for a small reduction in energy consumption.

Abstract

Abstract
This paper fits in the theory of international agreements by studying the success of stable coalitions of agents seeking the preservation of a public good. Extending Baliga and Maskin, we consider a model of N homogeneous agents with quasi-linear utilities of the form u(j) (r(j); r) = r(alpha) - r(j), where r is the aggregate contribution and the exponent alpha is the elasticity of the gross utility. When the value of the elasticity alpha increases in its natural range (0, 1), we prove the following five main results in the formation of stable coalitions: (i) the gap of cooperation, characterized as the ratio of the welfare of the grand coalition to the welfare of the competitive singleton coalition grows to infinity, which we interpret as a measure of the urge or need to save the public good; (ii) the size of stable coalitions increases from 1 up to N; (iii) the ratio of the welfare of stable coalitions to the welfare of the competitive singleton coalition grows to infinity; (iv) the ratio of the welfare of stable coalitions to the welfare of the grand coalition decreases (a lot), up to when the number of members of the stable coalition is approximately N/e and after that it increases (a lot); and (v) the growth of stable coalitions occurs with a much greater loss of the coalition members when compared with free-riders. Result (v) has two major drawbacks: (a) A priori, it is difficult to convince agents to be members of the stable coalition and (b) together with results (i) and (iv), it explains and leads to the pessimistic Barrett's paradox of cooperation, even in a case not much considered in the literature: The ratio of the welfare of the stable coalitions against the welfare of the grand coalition is small, even in the extreme case where there are few (or a single) free-riders and the gap of cooperation is large. Optimistically, result (iii) shows that stable coalitions do much better than the competitive singleton coalition. Furthermore, result (ii) proves that the paradox of cooperation is resolved for larger values of.. so that the grand coalition is stabilized.

Abstract
Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Abstract
We propose an approach to cluster and classify compositional data. We transform the compositional data into directional data using the square root transformation. To cluster the compositional data, we apply the identification of a mixture of Watson distributions on the hypersphere and to classify the compositional data into predefined groups, we apply Bayes rules based on the Watson distribution to the directional data. We then compare our clustering results with those obtained in hierarchical clustering and in the K-means clustering using the log-ratio transformations of the data and compare our classification results with those obtained in linear discriminant analysis using log-ratio transformations of the data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
When directional data fall in the positive orthant of the unit hypersphere, a folded directional distribution is preferred over a simple directional distribution for modeling the data. Since directional data, especially axial data, can be modeled using a Watson distribution, this paper considers a folded Watson distribution for such cases. We first address the parameter estimation of this distribution using maximum likelihood, which requires a numerical algorithm to solve the likelihood equations. We use the Expectation-Maximization (EM) algorithm to obtain these estimates and to analyze the properties of the concentration estimator through simulation. Next, we propose the Bayes rule for a folded Watson distribution and evaluate its performance through simulation in various scenarios, comparing it with the Bayes rule for the Watson distribution. Finally, we present examples using both simulated and real data available in the literature.