A simulation-based performance evaluation model for decision support on drone location and delivery scheduling

被引:2
|
作者
Ghelichi, Zabih [1 ]
Gentili, Monica [1 ]
Mirchandani, Pitu [2 ]
机构
[1] Univ Louisville, Dept Ind Engn, Louisville, KY 40292 USA
[2] Arizona State Univ, Sch Comp & Augmented Intelligence, Tempe, AZ USA
关键词
Simulation; Optimization; Delivery drone; Humanitarian logistics; LOGISTICS; OPTIMIZATION; FLEET;
D O I
10.1108/JHLSCM-04-2023-0036
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
PurposeThis paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system - on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.Design/methodology/approachThis simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones' operations. An optimization model integrated with the simulation system can update the optimality of drones' schedules and delivery assignments.FindingsAn extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.Originality/valueThe goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery's energy is impacted and requires battery change/recharging while flying.
引用
收藏
页码:304 / 327
页数:24
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