Cannot avoid penalty? Let’s minimize

Sarkar, Chayan; Agarwal, Marichi

doi:10.1109/icra.2019.8794338

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…For this reason, most solution methods are approximate, i.e., they do not guarantee that solutions will have the minimum overall cost, but they are fast and provide solutions that are acceptable in practice. In this context, there are works that employ auction-based algorithms [25], heuristics [9,12,18], meta-heuristics [26,27,14] and hybrid algorithms [19,13,11], for example.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In other words, the robot fleet (vehicles) is heterogeneous and dispersed on the map, and our scenario owns several stations for delivering products (multi-depot). Many other works approached VRP to address the multi-robot task allocation in warehouses [19,9,17,18,30,33], and other indoor environments [34,35]. However, to the best of our knowledge, this is the first work that addresses the HFMDVRP-DV, and such VRP variation applied indoors.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Edelkamp and Lee [17] Sarkar and Agarwal [18] tackled the MRTA in smart warehouses, where tasks are also associated with deadlines and unmet penalties. Robots must transport goods spread across the picking station to the (single) delivery station.…”

Section: Vrp Variations In Smart Warehousesmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Task Allocation in Smart Warehouses with Multi-delivery Stations and Heterogeneous Robots

Oliveira¹,

Röning²,

Plentz³

et al. 2022

Preprint

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The task allocation problem in multi-robot systems (MRTA) is an NPhard problem whose viable solutions are usually found by heuristic algorithms. Considering the increasing need of improvement on logistics, the use of robots for increasing the efficiency of logistics warehouses is becoming a requirement. In a smart warehouse the main tasks consist of employing a fleet of automated picking and mobile robots that coordinate by picking up items from a set of orders from the shelves and dropping them at the delivery stations. Two aspects generally justify multi-robot task allocation complexity: (i) environmental aspects, such as multi-delivery stations and dispersed robots (since they remain in constant motion) and (ii) fleet heterogeneity, where robots' traffic speed and

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