A Multiobjective Path-Planning Algorithm With Time Windows for Asset Routing in a Dynamic Weather-Impacted Environment

Abstract

This paper presents a mixed-initiative tool for multiobjective planning and asset routing (TMPLAR) in dynamic and uncertain environments. TMPLAR is built upon multiobjective dynamic programming algorithms to route assets in a timely fashion, while considering fuel efficiency, voyage time, distance, and adherence to real world constraints (asset vehicle limits, navigator-specified deadlines, etc.). TMPLAR has the potential to be applied in a variety of contexts, including ship, helicopter, or unmanned aerial vehicle routing. The tool provides recommended schedules, consisting of waypoints, associated arrival and departure times, asset speed and bearing, that are optimized with respect to several objectives. The ship navigation is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather, multiple constraints on asset operation, and the added capability of waiting at a waypoint with the intent to avoid bad weather, conduct opportunistic training drills, or both. The key algorithmic contribution is a multiobjective shortest path algorithm for networks with stochastic nonconvex edge costs and the following problem features: 1) time windows on nodes; 2) ability to choose vessel speed to next node subject to (minimum and/or maximum) speed constraints; 3) ability to select the power plant configuration at each node; and 4) ability to wait at a node. The algorithm is demonstrated on six real world routing scenarios by comparing its performance against an existing operational routing algorithm.

Weather-Routing System Based on METOC Navigation Risk Assessment

“…Most of the available weather-routing systems are defined by an underlying common architecture of 4 interconnected components [1,4]: Environmental layer, Ship modeling, Planning layer and Decision layer. Each component provides a set of functionalities and data required for the complete setup of a weather-routing problem.…”

“…Several approaches were developed to model the weather-routing problem. They go from methods in optimal control theory [2], dynamic programming to constrained graph problems [3,4], constrained nonlinear optimization problem [5] to a combination of them [6]. These mathematical methods are used either in a single-objective or multi-objective optimization setup.…”

“…Reference systems in the open literature [3,4,7] use the navigation safety guidelines [8] for the definition of navigation constraints, which limit the areas where the navigation is allowed and not allowed based on the METOC conditions and the derived navigation conditions. In contrast to these reference systems [3,4,7] and similar to the approach discussed in [9,10], the focus of our paper is on the translation of the navigation safety guidelines [8] into a safety criterion to be minimized within a multi-criteria optimization setup. This design choice is based on the fact that the navigation safety of a route is usually determined by the vessel operator during the decisional stage.…”

A Mathematical Model and Grouping Imperialist Competitive Algorithm for Integrated Quay Crane and Yard Truck Scheduling Problem with Non-crossing Constraint

“…Further, a mathematical model and a GA have been developed for solving the problem [23]. Also there are numerous studies on the automated guided vehicles (AGVs) in automated container terminals [24,25]. Implementation of AGVs instead of yard trucks can lead to higher efficiency and stability, cost reduction and safer environment in container terminals [26].…”

Optimization-Based Control for Bearing-Only Target Search With a Mobile Vehicle

“…In the literature, evolutionary algorithms are commonly-used, such as the genetic algorithm in [23], pigeon inspired optimization method in [24], and value iterationlike algorithm in [28]. Moreover, the graph-based searching method is thoroughly investigated in [25], [26]. Different from the aforementioned approaches, we attack the complicated optimization problem by decomposing the forward velocity of the vehicle and then transforming the optimization problem into a solvable form.…”