There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.

Many real-world problems in manufacturing system, for instance, the scheduling problems, are formulated by defining several objectives for problem solving and decision making. Recently, research on dispatching rules allocation has attracted substantial attention. Although many dispatching rules methods have been developed, multi-objective scheduling problems remain inherently difficult to solve by any single rule. In this paper, a hybrid genetic-based gravitational search algorithm (GSA) in weighted dispatching rule is proposed to tackle a scheduling problem by achieving both time and job-related objectives. Genetic algorithm (GA) is used to select two appropriate dispatching rules to combine as a weighted multi-attribute function, while the GSA is used to optimize the contribution weightage of each rule in each stage of the flow shop. The results show that the proposed algorithm is significantly better than the traditional dispatching rules and the rules allocation algorithm. The proposed algorithm not only improved the quality of the schedule in multi-objective problems but also maintained the advantages of traditional dispatching rules in terms of ease of implementation.