14 research outputs found
Ramping Up for Cost and Performance Improvements
The objective of this Research Project was to study the possible benefits of developing an improved Metering Control System. This study identified potential cost savings and reduced emissions through the greater use of a Metering Control System. The results of this research have indicated an annual cost reduction of $720,000 in fuel burn and a significant delay time reduction caused by accumulated ground traffic. In addition, during peak hours, it was concluded that savings could be potentially higher. Therefore, by optimizing the ground movement, it is also expected a lower level of CO2 emissions, an increased safety level due to more organized apron movements, and finally, an improved customer experience.
The data presented in this research is in line with current Brazilian regulations, focused on reducing or minimizing airline ground delays. The selected airport sample is a main
hub of an airline, concentrating 95% of its operation. Also, the airport is classified as the fourth busiest in terms of operation in Brazil.
An inefficient apron management is responsible for causing a variety of disruptions in terms of on-time performance, fuel consumption and customer satisfaction. Some events may be reduced or even eliminated by applying simple practices, therefore, increasing efficiency without compromising safety
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to
their goal locations without collisions. In this paper, we study the lifelong
variant of MAPF, where agents are constantly engaged with new goal locations,
such as in large-scale automated warehouses. We propose a new framework
Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by
decomposing the problem into a sequence of Windowed MAPF instances, where a
Windowed MAPF solver resolves collisions among the paths of the agents only
within a bounded time horizon and ignores collisions beyond it. RHCR is
particularly well suited to generating pliable plans that adapt to continually
arriving new goal locations. We empirically evaluate RHCR with a variety of
MAPF solvers and show that it can produce high-quality solutions for up to
1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse
instances, significantly outperforming existing work.Comment: Published at AAAI 202
Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS)
Conflict-Based Search (CBS) is a widely used algorithm for solving
multi-agent pathfinding (MAPF) problems optimally. The core idea of CBS is to
run hierarchical search, when, on the high level the tree of solutions
candidates is explored, and on the low-level an individual planning for a
specific agent (subject to certain constraints) is carried out. To trade-off
optimality for running time different variants of bounded sub-optimal CBS were
designed, which alter both high- and low-level search routines of CBS.
Moreover, anytime variant of CBS does exist that applies Focal Search (FS) to
the high-level of CBS - Anytime BCBS. However, no comprehensive analysis of how
well this algorithm performs compared to the naive one, when we simply
re-invoke CBS with the decreased sub-optimality bound, was present. This work
aims at filling this gap. Moreover, we present and evaluate another anytime
version of CBS that uses FS on both levels of CBS. Empirically, we show that
its behavior is principally different from the one demonstrated by Anytime
BCBS. Finally, we compare both algorithms head-to-head and show that using
Focal Search on both levels of CBS can be beneficial in a wide range of setups.Comment: This is a preprint of the paper accepted to MICAI 202
Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers
In this paper, we study the one-shot and lifelong versions of the Target
Assignment and Path Finding problem in automated sortation centers, where each
agent needs to constantly assign itself a sorting station, move to its assigned
station without colliding with obstacles or other agents, wait in the queue of
that station to obtain a parcel for delivery, and then deliver the parcel to a
sorting bin. The throughput of such centers is largely determined by the total
idle time of all stations since their queues can frequently become empty. To
address this problem, we first formalize and study the one-shot version that
assigns stations to a set of agents and finds collision-free paths for the
agents to their assigned stations. We present efficient algorithms for this
task based on a novel min-cost max-flow formulation that minimizes the total
idle time of all stations in a fixed time window. We then demonstrate how our
algorithms for solving the one-shot problem can be applied to solving the
lifelong problem as well. Experimentally, we believe to be the first
researchers to consider real-world automated sortation centers using an
industrial simulator with realistic data and a kinodynamic model of real
robots. On this simulator, we showcase the benefits of our algorithms by
demonstrating their efficiency and effectiveness for up to 350 agents.Comment: AAAI 2020, to appea