Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.

Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain less switches than the maximum. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations improves over the results obtained by a recent state-of-the-art Hybrid Genetic Algorithm for pump scheduling using level-controlled triggers.

Understanding bystanders’ willingness to Intervene in traditional and cyberbullying scenarios.

Bullying incidents in traditional and online settings are a cause for concern to many parties. The goal of the current study was to explore the extent to which a bystander would intervene in a bullying incident and the degree to which this behavior is influenced by group size (the number of other witnesses), the setting (traditional or cyberbullying), and gender of the victim. Using an online survey method, participants were presented with eight bullying scenarios, each of which involved verbal bullying of a victim. Participants (N = 82) were asked to report how likely they would be to intervene in each of these scenarios. Results showed that female victims were more likely to be helped than male victims. Furthermore, female participants were more willing to intervene than the male participants in the cyberbullying scenarios. Altruism was a positive predictor of participants’ willingness to intervene. The present findings suggest that certain gender differences in helping behavior may depend on the context in which bullying is observed (traditional or cyberbullying).