The Heterogeneous Fleet Capacitated Vehicle Routing Problem with Time Windows and Three- Dimensional Loading Constraints (3L-HFCVRPTW) combines the aspects of 3D loading, heterogeneous transport with capacity constraints and time windows for deliveries. It is the first formulation that comprises all these aspects and takes its inspiration from a practical problem of distributing daily fibre board deliveries faced by our industry partner. Given the shape of the goods to transport, the delivery vehicles are customised and their loading constraints take a specialised form. This study introduces the problem and its constraints as well as a specialised procedure for loading the boards. The loading module can be called during or after the route optimisation. In this initial work, we apply simple local search procedures to the routing problem to two data sets obtained from our industry partner and subsequently employ the loading module to place the deliveries on the vehicles. Simulated Annealing outperforms Iterated Local Search, suggesting that the routing problem is multimodal, and operators that shift deliveries between routes appear most beneficial

This article addresses the dynamic vehicle routing problem (DVRP). DVRP is a challenging variation of the classic vehicle routing problem in which some customers are not known in advance. The objective is to incorporate new customers into the schedule as they become known while still attempting to minimize the cost of serving all customers without violating the problem constraints. This work proposes an effective population-based approach that integrates various algorithmic components to address DVRP. The approach combines a local search algorithm with various evolutionary operators (crossover and mutation) in an adaptive manner. To promote diversity, the proposed approach utilizes a population of solutions and uses a quality-and-diversity strategy to retain only promising solutions. The well-known 21 DVRP benchmark instances are utilized to test the performance of the proposed approach. An experimental comparison is carried out to assess the contribution of the integrated components. Results demonstrate that the integrated components significantly improve search performance. It is also shown that the proposed approach produces new best results for several instances when compared with the best methods reported in the literature.

The inability to perceive visual and other non-verbal cues for individuals with visual impairment can pose a significant challenge for their correct conversational interactions and can be an impediment for various daily life activities. Recent advancements in computational resources, particularly the computer vision capabilities can be utilized to design effective applications for visually impaired people (VIP).Among various assistive technologies, automated facial impression recognition with real-time accurate interpretation can be proven useful to tackle the above problem. Using such approach, facial emotions (e.g., sad, happy) can be robustly recognized and conveyed to the associated individuals. In this paper, a partial transfer learning approach was adopted utilizing a custom trained Convolutional Neural Network (CNN) for facial emotion recognition. A novel model that transfers features from one dataset to another is proposed. This model enables the transfer of features learned from a small number of instances to solve new challenges instances. Using the proposed approach based on a newly trained CNN, a portable lightweight facial expression recognition system with wireless connectivity and high detection accuracy was constructed and targeted specifically for VIP. The proposed recognition model provides a notable improvement over the current state-of-the-art, by providing the highest recognition accuracy of 82.1% on the enhanced Facial Expression Recognition 2013 (FER2013) FER2013 dataset. Moreover, with only 1.49M parameters, the model is operable on edge devices with limited memory and processing power. Overall, three labeled emotions happy, sad, surprise were recognized by the model with high accuracy whereas a relatively lower accuracy rate for anger, disgust, fear was noticed with higher misclassification labels for sad.