ABSTRACT
Household energy consumption (HEC) is one of the major contributors to global emissions, making it a critical area for addressing sustainability challenges. The impact of personality traits on human behaviour is significant in shaping HEC patterns, and therefore, have important implications for sustainability policies. This study aims to investigate role of biologically predicted big-two personality traits (i.e., stability and plasticity), a higher order solution to five-factor traits and orthogonal traits, on HEC. To that end, a structural equation model is developed using a national household survey in Australia. The performance of the model is benchmarked against a five-factor (i.e., agreeableness, consciousness, emotional stability, extraversion and openness) personality trait model. The performance of the models is measured using six goodness-of-fit indices, all of which show a superior performance in the big-two traits model. The results indicate that a higher score in stability poses energy-intensive behaviour, while a higher plasticity score poses energy-saving behaviours. The plasticity trait is linked to environmentally friendly behaviours, while the stability trait is associated with environmentally unfavourable behavioural practices. The effects of socioeconomic status on HEC are mediated by stability and plasticity to identify those who are more likely to change their energy consumption habits as the target group for policy intervention. This study can assist policy makers to determine energy-intensive and energy-saving behaviours from the big-two traits, and to develop more effective and targeted sustainability policies that can help in reducing HEC and promote sustainable living in societies.
ABSTRACT
Dial a ride problem (DARP) is a complex version of the pick-up and delivery problem with many practical applications in the field of transportation. This study proposes an enhanced deterministic annealing algorithm for the solution of large-scale multi-vehicle DARPs. The proposed method always explores the feasible search space; therefore, a feasible solution is guaranteed at any point of termination. This method utilises advanced local search operators to accelerate the search for optimal solutions and it relies on a linearly decreasing deterministic annealing schedule to limit poor jumps during the course of search. This study puts forward a systematic series of experiments to compare the performance of solution methods from various angles. The proposed method is compared with the most efficient methods reported in the literature i.e., the Adaptive Large Neighbourhood Search (ALNS), Evolutionary Local Search (ELS), and Deterministic Annealing (DA) using standard benchmarks. The results suggest that the proposed algorithm is on average faster than the state-of-the-art algorithms in reaching competitive objective values across the range of benchmarks.
