A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A Mixed Integer Non-Linear Programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4–10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions

Optimized Model Simulation of a Capacitated Vehicle Routing problem based on Firefly Algorithm

This paper presents an optimized solution to a capacitated vehicle routing (CVRP) model using firefly algorithm (FFA). The main objective of a CVRP is to obtain the minimum possible total travelled distance across a search space. The conventional model is a formal description involving mathematical equations formulated to simplify a more complex structure of logistic problems. These logistic problems are generalized as the vehicle routing problem (VRP). When the capacity of the vehicle is considered, the resulting formulation is termed the capacitated vehicle routing problem (CVRP). In a practical scenario, the complexity of CVRP increases when the number of pickup or drop-off points increase making it difficult to solve using exact methods. Thus, this paper employed the intelligent behavior of FFA for solving the CVRP model. Two instances of solid waste management and supply chain problems is used to evaluate the performance of the FFA approach. In comparison with particle swarm optimization and few other ascribed metaheuristic techniques for CVRP, results showed that this approach is very efficient in solving a CVRP model

Handling disruptions in a network with cross-docking

Cross-docking (CD) is a commonly used technique to consolidate freight for more efficient delivery to customers; CD is continuing to see increased use by companies. Synchronization of inbound and outbound freight is clearly critical to operations and so is having the cross-dock able to support the freight flow with available doors and material handling equipment. The latter is particularly important when there is a disruption in the inbound freight. One delayed truck can impact several outbound trucks. A methodology is proposed to address explicitly both the scheduling of trucks and material handling within the CD. Two models are proposed – one for routing inbound and outbound trucks and the other to schedule the cross-dock. Results from each model when run separately are presented as well as results from when the two models are run iteratively