An Agile Simheuristic for the Stochastic Team Task Assignment and Orienteering Problem: Applications to Unmanned Aerial Vehicles

Abstract

Efficient coordination of unmanned aerial vehicles (UAVs) requires the solving of challenging operational problems. One of them is the integrated team task assignment and orienteering problem (TAOP). The TAOP can be seen as an extension of the well-known team orienteering problem (TOP). In the classical TOP, a homogeneous fleet of UAVs has to select and visit a subset of customers in order to maximize, subject to a maximum travel time per route, the total reward obtained from these visits. In the TAOP, a number of different tasks (customer services) have to be assigned to a fleet of heterogeneous UAVs, while the best routing plan must also be determined to cover these services. Since factors such as weather conditions might influence travel times, these are modeled as random variables. Reliability issues are also considered, since random times might prevent a route from being successfully completed before a UAV runs out of battery.

A biased-randomized iterated local search for the vehicle routing problem with optional backhauls

“…Firstly, an initial solution is obtained through the BR-CWS (Algorithm 2 and line 2 in Algorithm 1). The savings list is created for each edge not connected with the depot, however, since each BH customer has a penalty cost for not being served, the traditional way to calculate such savings (s ij = c i0 + c 0j − c ij ) is replaced with the expression in Equation ( 21) (Panadero et al, 2018) only for those edges whose both nodes are backhaul. 2018) affirm that its concrete value only depends on "the heterogeneity of the customers in terms of rewards", i.e, the higher the heterogeneity, the closer to 0 α should be.…”

Battery Management in Electric Vehicle Routing Problems: A Review

“…In their work, Panadero et al [89] explored the increasing use of UAVs across various fields like smart cities, logistics in urban areas, humanitarian aid, response to natural disasters, and military operations. They focused on one particular challenge: optimizing UAV operations in scenarios where travel times are unpredictable.…”

On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment

“…In (Das et al, 2018) an optimization algorithm is proposed for the number of UAVs for tracking multiple mobile targets. The Team Orienting Problem (TOP) is applied to drones in (Panadero et al, 2018), as a range of limitations need to be taken into account when optimizing their operations and management. A methodology to maximize the persistent coverage of a given terrain is described in (Bogdanowicz, 2018) and while it is focused on military applications, the same concept could be applied for transportation purposes.…”