A Multiobjective Path-Planning Algorithm With Time Windows for Asset Routing in a Dynamic Weather-Impacted Environment
| Autor: | Manisha Mishra, James E Peak, Bala Kishore Nadella, David Sidoti, James A. Hansen, Krishna R. Pattipati, G.V. Avvari, Lingyi Zhang |
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| Rok vydání: | 2017 |
| Předmět: |
050210 logistics & transportation
Mathematical optimization Schedule 021103 operations research Operations research Computer science Node (networking) 05 social sciences 0211 other engineering and technologies 02 engineering and technology Computer Science Applications Human-Computer Interaction Dynamic programming Control and Systems Engineering 0502 economics and business Vehicle routing problem Shortest path problem K shortest path routing Electrical and Electronic Engineering Routing (electronic design automation) Dijkstra's algorithm Algorithm Software Constrained Shortest Path First |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 47:3256-3271 |
| ISSN: | 2168-2232 2168-2216 |
| DOI: | 10.1109/tsmc.2016.2573271 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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