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Dial-a-ride problem (DARP) deals with the transportation of users between pickup and drop-off locations associated with specified time windows. This paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve static dial-a-ride problem. Two new local search operators (burn and reform), a new construction heuristic and two request sequencing mechanisms (Sorted List and Random List) are developed. Computational experiments conducted on various standard DARP test instances prove that MATA is an expeditious meta-heuristic in contrast to other existing methods. In all experiments, MATA demonstrates the capability to obtain high quality solutions, faster convergence, and quicker attainment of a first feasible solution. It is observed that MATA attains a first feasible solution 29.8 to 65.1% faster, and obtains a final solution that is 3.9 to 5.2% better, when compared to other algorithms within 60 sec.

Aiming to develop methods for real-time 3D scanning of building interiors, this work evaluates the performance of state-of-the-art LiDAR-based approaches for 3D simultaneous localisation and mapping (SLAM) in indoor environments. A simulation framework using ROS and Gazebo have been implemented to compare different methods based on LiDAR odometry and mapping (LOAM). The featureless environments typically found in interiors of commercial and industrial buildings pose significant challenges for LiDAR-based SLAM frameworks, resulting in drift or breakdown of the processes. The results from this paper provide performance criteria for indoor SLAM applications, comparing different room topologies and levels of clutter. The modular nature of the simulation environment provides a framework for future SLAM development and benchmarking specific to indoor environments.

This paper introduces a novel meta-heuristic approach to optimize depot locations for multi-vehicle shared mobility systems. Dial-a-ride problem (DARP) is considered as a case study here, in which routing and scheduling for doorto-door passenger transportation are performed while satisfying several constraints related to user convenience. Existing literature has not addressed the fundamental problem of depot location optimization for DARP, which can reduce cost, and in turn promote the use of shared mobility services to minimize carbon footprint. Thus, there is a great need for fleet management systems to employ a multi-depot vehicle dispatch mechanism with intrinsic depot location optimization. In this work, we propose a deterministic annealing meta-heuristic to optimize depot locations for the dial-a-ride problem. Numerical experiments are conducted on several DARP benchmark instances from the literature, which can be categorized as small, medium and large based on their problem size. For all tested instances, the proposed algorithm attains solutions with travel cost better than that of the best-known solutions. It is also observed that the travel cost is reduced up to 6.13% when compared to the best-known solutions.

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