Stochastic facility location model for drones considering uncertain flight distance

Kim, Dong-Wook; Lee, Kyungsik; Moon, Ilkyeong

doi:10.1007/s10479-018-3114-6

Cited by 53 publications

(38 citation statements)

References 26 publications

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“…MCDM Techniques (5): John et al 2018;Laguna-Salvado et al 2018;Venkatesh et al 2018;Malekpoor et al 2018;Mediouni et al 2018 Optimization ( 34): Anparasan and Lejeune, 2017;Baharmand et al 2017;Baidya and Bera, 2018;Bao et al 2017;Cankaya et al 2018;de Mattos et al 2018;Elluru et al 2017;Fazli-Khalaf et al 2017;Griffith et al 2017;Han et al 2018;Hoskins and Medal, 2018;Ivanov and Sokolov, 2019;Jana et al 2018;Kaur and Singh, 2016;Khalilpourazari and Khamseh, 2017;Kim, Lee and Moon, 2018;Kim, Ramkumar and Subramaniyan, 2018;Leigh et al 2017;Li and Teo, 2018;Lodree et al 2018;Nilsang et al 2018;Pyakurel et al 2018;Prasad et al 2017;Rahmani, 2018;Sabouhi et al 2018;Salehi et al 2017;Samani and Hosseni-Motlagh, 2018;Shareef et al 2018;Sharma et al 2017;Tayal and Singh, 2017;Turkes et al 2017;Yahyaei and Bozorgi-Amiri, 2018;Yang et al 2017;...…”

Section: Future Research Directionsmentioning

confidence: 99%

Disaster relief operations: past, present and future

2019

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Section: Future Research Directionsmentioning

confidence: 99%

Disaster relief operations: past, present and future

2019

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“…Marín et al (2018) propose a stochastic programming modeling framework for a general class of covering location problems and develop a Lagrangian relaxation-based heuristic to tackle large-scale instances. In the context of humanitarian logistics, Kim et al (2019) investigate the problem of finding optimal locations for drone facilities taking into account that flight distance is uncertain due to battery consumption being affected by weather conditions. Khodaparasti et al (2018) address the problem of designing a network of nursing homes under uncertain demand and a budget constraint on each time period.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated facility location and capacity planning under uncertainty

Correia

Melo

2021

Comp. Appl. Math.

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We address a multi-period facility location problem with two customer segments having distinct service requirements. While customers in one segment receive preferred service, customers in the other segment accept delayed deliveries as long as lateness does not exceed a pre-specified threshold. The objective is to define a schedule for facility deployment and capacity scalability that satisfies all customer demands at minimum cost. Facilities can have their capacities adjusted over the planning horizon through incrementally increasing or reducing the number of modular units they hold. These two features, capacity expansion and capacity contraction, can help substantially improve the flexibility in responding to demand changes. Future customer demands are assumed to be unknown. We propose two different frameworks for planning capacity decisions and present a two-stage stochastic model for each one of them. While in the first model decisions related to capacity scalability are modeled as first-stage decisions, in the second model, capacity adjustments are deferred to the second stage. We develop the extensive forms of the associated stochastic programs for the case of demand uncertainty being captured by a finite set of scenarios. Additional inequalities are proposed to enhance the original formulations. An extensive computational study with randomly generated instances shows that the proposed enhancements are very useful. Specifically, 97.5% of the instances can be solved to optimality in much shorter computing times. Important insights are also provided into the impact of the two different frameworks for planning capacity adjustments on the facility network configuration and its total cost.

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“…Kim et al (30) develop a chance constraint formulation using an exponential distribution to model the impact of battery uncertainty on coverage of a location. Unlike Kim et al (30), the RMCFLPD adopts a robust optimization approach where the battery consumption and capacity is assumed to vary in a pre-specified range and therefore is distribution-free. Goodchild and Toy (31) and Figliozzi (32) evaluate relative efficiency, energy consumption, and emissions from UAVs relative to trucks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, Kim et al ( 29 ) do not model variations in energy consumption with payload, allocation of drones to facilities, or facility capacity, and use CPLEX to solve the problem. Kim et al ( 30 ) develop a chance constraint formulation using an exponential distribution to model the effect of battery uncertainty on coverage of a location. Unlike Kim et al ( 30 ), the RMCFLPD adopts a robust optimization approach where the battery consumption and capacity is assumed to vary in a pre-specified range and is therefore distribution free.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Maximum Coverage Facility Location Problem with Drones Considering Uncertainties in Battery Availability and Consumption

Chauhan

Unnikrishnan

Figliozzi

et al. 2020

Transportation Research Record: Journal of the Transportation R

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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.

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Stochastic facility location model for drones considering uncertain flight distance

Cited by 53 publications

References 26 publications

Disaster relief operations: past, present and future

Disaster relief operations: past, present and future

Integrated facility location and capacity planning under uncertainty

Robust Maximum Coverage Facility Location Problem with Drones Considering Uncertainties in Battery Availability and Consumption

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