Robust Maximum Coverage Facility Location Problem with Drones Considering Uncertainties in Battery Availability and Consumption
| Autor: | Stephen D. Boyles, Darshan Rajesh Chauhan, Avinash Unnikrishnan, Miguel A. Figliozzi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Consumption (economics)
Battery (electricity) 050210 logistics & transportation 021103 operations research Operations research Computer science Mechanical Engineering 05 social sciences 0211 other engineering and technologies 02 engineering and technology Facility location problem Drone Set (abstract data type) 0502 economics and business Agency (sociology) Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2675:25-39 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.1177/0361198120968094 |
| Popis: | Given a set of a spatially distributed demand for a specific commodity, potential facility locations, and drones, an agency is tasked with locating a pre-specified number of facilities and assigning drones to them to serve the demand while respecting drone range constraints. The agency seeks to maximize the demand served while considering uncertainties in initial battery availability and battery consumption. The facilities have a limited supply of the commodity being distributed and also act as a launching site for drones. Drones undertake one-to-one trips (from located facility to demand location and back) until their available battery energy is exhausted. This paper extends the work done by Chauhan et al. and presents an integer linear programming formulation to maximize coverage using a robust optimization framework. The uncertainty in initial battery availability and battery consumption is modeled using a penalty-based approach and gamma robustness, respectively. A novel robust three-stage heuristic (R3SH) is developed which provides objective values which are within 7% of the average solution reported by MIP solver with a median reduction in computational time of 97% on average. Monte Carlo simulation based testing is performed to assess the value of adding robustness to the deterministic problem. The robust model provides higher and more reliable estimates of actual coverage under uncertainty. The average maximum coverage difference between the robust optimization solution and the deterministic solution is 8.1% across all scenarios. |
| Databáze: | OpenAIRE |
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