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

Integrated production and inventory routing planning of oxygen supply chains

In this work, we address a production and inventory routing problem for a liquid oxygen supply chain comprising production facilities, distribution network, and distribution resources. The key decisions of the problem involve production levels of production plants, delivery schedule and routing through heterogeneous vehicles, and inventory strategies for national stock-out prevention. Due to the problem complexity, we propose a two-level hybrid solution approach that solves the problem using both exact and metaheuristic methods. At the upper level, we develop a mixed-integer linear programming (MILP) model that determines production and inventory decisions and customer allocation. In the lower level, the original problem is reduced to several multi-trip heterogeneous vehicle routing problems by fixing the optimal production, inventory, and allocation decisions and clustering customers. A well-recognised metaheuristic, guided local search method, is adapted to solve the low-level routing problems. A real-world case study in the UK illustrates the applicability and effectiveness of the proposed optimisation framework

A clustering approach for vehicle routing problems with hard time windows

Dissertação para obtenção do Grau de Mestre em Logica ComputicionalThe Vehicle Routing Problem (VRP) is a well known combinatorial optimization problem and many studies have been dedicated to it over the years since solving the VRP optimally or near-optimally for very large size problems has many practical applications (e.g. in various logistics systems). Vehicle Routing Problem with hard TimeWindows (VRPTW) is probably the most studied variant of the VRP problem and the presence of time windows requires complex techniques to handle it. In fact, finding a feasible solution to the VRPTWwhen the number of vehicles is fixed is an NP-complete problem. However, VRPTW is well studied and many different approaches to solve it have been developed over the years. Due to the inherent complexity of the underlying problem VRPTW is NP-Hard. Therefore, optimally solving problems with no more than one hundred requests is considered intractably hard. For this reason the literature is full with inexact methods that use metaheuristics, local search and hybrid approaches which are capable of producing high quality solutions within practical time limits. In this work we are interested in applying clustering techniques to VRPTWproblem. The idea of clustering has been successfully applied to the basic VRP problem. However very little work has yet been done in using clustering in the VRPTW variant. We present a novel approach based on clustering, that any VRPTW solver can adapt, by running a preprocessing stage before attempting to solve the problem. Our proposed method, tested with a state of the art solver (Indigo), enables the solver to find solutions much faster (up to an order of magnitude speed-up). In general this comes with at slightly reduced solution quality, but in somes types of problems, Indigo is able to obtain better solutions than those obtained with no clustering