scholarly journals Anytime Multi-Agent Path Finding via Large Neighborhood Search

2021 ◽  
Author(s):  
Jiaoyang Li ◽  
Zhe Chen ◽  
Daniel Harabor ◽  
Peter J. Stuckey ◽  
Sven Koenig
Keyword(s):  
Initial Solution ◽  
Neighborhood Search ◽  
Path Finding ◽  
Large Neighborhood Search ◽  
Challenging Problem ◽  
Anytime Algorithms ◽  
Solution Quality ◽  
Large Neighborhood ◽  
Multi Agent ◽  
Suboptimal Algorithms

Multi-Agent Path Finding (MAPF) is the challenging problem of computing collision-free paths for multiple agents. Algorithms for solving MAPF can be categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that can find high-quality solutions for small problems. At the other end are unbounded-suboptimal algorithms that can solve large problems but usually find low-quality solutions. In this paper, we consider a third approach that combines the best of both worlds: anytime algorithms that quickly find an initial solution using efficient MAPF algorithms from the literature, even for large problems, and that subsequently improve the solution quality to near-optimal as time progresses by replanning subgroups of agents using Large Neighborhood Search. We compare our algorithm MAPF-LNS against a range of existing work and report significant gains in scalability, runtime to the initial solution, and speed of improving the solution.

scholarly journals Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

2017 ◽  
Vol 05 (02) ◽  
pp. 79-95 ◽  
Author(s):  
Armin Sadeghi ◽  
Stephen L. Smith
Keyword(s):  
Traveling Salesman Problem ◽  
Task Allocation ◽  
Branch And Cut ◽  
Traveling Salesman ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Solution Quality ◽  
Solution Approach ◽  
Large Neighborhood ◽  
Generalized Traveling Salesman Problem

This paper focuses on decentralized task allocation and sequencing for multiple heterogeneous robots. Each task is defined as visiting a point in a subset of the robot configuration space — this definition captures a variety of tasks including inspection and servicing. The robots are heterogeneous in that they may be subject to different differential motion constraints. Our approach is to transform the problem into a multi-vehicle generalized traveling salesman problem (GTSP). To solve the GTSP, we propose a novel decentralized implementation of large-neighborhood search (LNS). Our solution approach leverages the GTSP insertion methods proposed in Fischetti et al. [A branch-and-cut algorithm for the symmetric generalized traveling salesman problem, Oper. Res. 45(3) (1997) 378–394]. to repeatedly remove and reinsert tasks from each robot path. Decentralization is achieved using combinatorial-auctions between the robots on tasks removed from robot’s path. We provide bounds on the length of the dynamically feasible robot paths produced by the insertion methods. We also show that the number of bids in each combinatorial auction, a crucial factor in the runtime, scales linearly with the number of tasks. Finally, we present extensive benchmarking results to characterize both solution quality and runtime, which show improvements over existing decentralized task allocation methods.

scholarly journals ANALISA PERBANDINGAN METODE SIMULATED ANNEALING DAN LARGE NEIGHBORHOOD SEARCH UNTUK MEMECAHKAN MASALAH LOKASI DAN RUTE KENDARAAN DUA ESELON

2020 ◽  
Vol 4 (1) ◽  
pp. 35-46
Author(s):  
Winarno (Universitas Singaperbangsa Karawang) ◽  
A. A. N. Perwira Redi (Universitas Pertamina)
Keyword(s):  
Simulated Annealing ◽  
Computational Time ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Location Routing ◽  
Node Removal ◽  
Better Than

AbstractTwo-echelon location routing problem (2E-LRP) is a problem that considers distribution problem in a two-level / echelon transport system. The first echelon considers trips from a main depot to a set of selected satellite. The second echelon considers routes to serve customers from the selected satellite. This study proposes two metaheuristics algorithms to solve 2E-LRP: Simulated Annealing (SA) and Large Neighborhood Search (LNS) heuristics. The neighborhood / operator moves of both algorithms are modified specifically to solve 2E-LRP. The proposed SA uses swap, insert, and reverse operators. Meanwhile the proposed LNS uses four destructive operator (random route removal, worst removal, route removal, related node removal, not related node removal) and two constructive operator (greedy insertion and modived greedy insertion). Previously known dataset is used to test the performance of the both algorithms. Numerical experiment results show that SA performs better than LNS. The objective function value for SA and LNS are 176.125 and 181.478, respectively. Besides, the average computational time of SA and LNS are 119.02s and 352.17s, respectively.AbstrakPermasalahan penentuan lokasi fasilitas sekaligus rute kendaraan dengan mempertimbangkan sistem transportasi dua eselon juga dikenal dengan two-echelon location routing problem (2E-LRP) atau masalah lokasi dan rute kendaraan dua eselon (MLRKDE). Pada eselon pertama keputusan yang perlu diambil adalah penentuan lokasi fasilitas (diistilahkan satelit) dan rute kendaraan dari depo ke lokasi satelit terpilih. Pada eselon kedua dilakukan penentuan rute kendaraan dari satelit ke masing-masing pelanggan mempertimbangan jumlah permintaan dan kapasitas kendaraan. Dalam penelitian ini dikembangkan dua algoritma metaheuristik yaitu Simulated Annealing (SA) dan Large Neighborhood Search (LNS). Operator yang digunakan kedua algoritma tersebut didesain khusus untuk permasalahan MLRKDE. Algoritma SA menggunakan operator swap, insert, dan reverse. Algoritma LNS menggunakan operator perusakan (random route removal, worst removal, route removal, related node removal, dan not related node removal) dan perbaikan (greedy insertion dan modified greedy insertion). Benchmark data dari penelitian sebelumnya digunakan untuk menguji performa kedua algoritma tersebut. Hasil eksperimen menunjukkan bahwa performa algoritma SA lebih baik daripada LNS. Rata-rata nilai fungsi objektif dari SA dan LNS adalah 176.125 dan 181.478. Waktu rata-rata komputasi algoritma SA and LNS pada permasalahan ini adalah 119.02 dan 352.17 detik.

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