Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning

Li, Jingwen; Liang, Xin; Cao, Zhiguang; Lim, Andrew; Song, Wen; Zhang, Jie

doi:10.1109/tits.2021.3056120

Cited by 54 publications

(31 citation statements)

References 24 publications

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“…The node coordinates are also generated uniformly from the unit square for all three problems, following [21,23]. For CVRP, the demands of customers are generated uniformly from integers {1..9} with the capacity fixed as 40 + 0.1 × |V |, compatible with the largest CVRP (100 nodes) studied in [21].…”

Section: Experiments On Other Routing Problemsmentioning

confidence: 99%

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

Liang¹,

Song²,

Cao³

et al. 2021

Preprint

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We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to much larger sizes. Also, we show that NeuroLKH can be applied to other routing problems such as Capacitated Vehicle Routing Problem (CVRP), Pickup and Delivery Problem (PDP), and CVRP with Time Windows (CVRPTW).

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Section: Experiments On Other Routing Problemsmentioning

confidence: 99%

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

Liang¹,

Song²,

Cao³

et al. 2021

Preprint

Self Cite

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show abstract

“…Some literature proposes special designs for the coupled challenge. For instance, Li et al [111] propose a special attention-based structure to leverage different relationships among all customer nodes in VRP with pick and delivery. Six different attention mechanisms in total are computed as a thorough measurement upon all nodes.…”

Section: Coupled Spatial-temporal Representationsmentioning

confidence: 99%

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Zong¹,

Feng²,

Tong³

et al. 2021

Preprint

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Recent technology development brings the booming of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehousing. In DDS, a service loop is an elemental structure, including its service worker, the service providers and corresponding service targets. The service workers should transport either humans or parcels from the providers to the target locations. Various planning tasks within DDS can thus be classified into two individual stages: 1) Dispatching, which is to form service loops from demand/supply distributions, and 2)Routing, which is to decide specific serving orders within the constructed loops. Generating high-quality strategies in both stages is important to develop DDS but faces several challenging. Meanwhile, deep reinforcement learning (DRL) has been developed rapidly in recent years. It is a powerful tool to solve these problems since DRL can learn a parametric model without relying on too many problem-based assumptions and optimize long-term effect by learning sequential decisions. In this survey, we first define DDS, then highlight common applications and important decision/control problems within. For each problem, we comprehensively introduce the existing DRL solutions, and further summarize them in https://github.com/tsinghua-fib-lab/DDS Survey. We also introduce open simulation environments for development and evaluation of DDS applications. Finally, we analyze remaining challenges and discuss further research opportunities in DRL solutions for DDS.

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“…Recently, researchers tend to apply deep reinforcement learning (DRL) to automatically learn the searching rules in heuristic methods for solving routing problems including CVRP and TSP [19]- [24], by discovering the underlying patterns from a large number of instances. Generally, those DRL models are categorized as two classes, i.e., construction and improvement methods, respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem

Li,

Ma,

Gao

et al. 2021

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Existing deep reinforcement learning (DRL) based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with homogeneous vehicle fleet, in which the fleet is assumed as repetitions of a single vehicle. Hence, their key to construct a solution solely lies in the selection of the next node (customer) to visit excluding the selection of vehicle. However, vehicles in real-world scenarios are likely to be heterogeneous with different characteristics that affect their capacity (or travel speed), rendering existing DRL methods less effective. In this paper, we tackle heterogeneous CVRP (HCVRP), where vehicles are mainly characterized by different capacities. We consider both min-max and min-sum objectives for HCVRP, which aim to minimize the longest or total travel time of the vehicle(s) in the fleet. To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step. Experimental results based on randomly generated instances show that, with desirable generalization to various problem sizes, our method outperforms the state-of-theart DRL method and most of the conventional heuristics, and also delivers competitive performance against the state-of-the-art heuristic method, i.e., SISR. Additionally, the results of extended experiments demonstrate that our method is also able to solve CVRPLib instances with satisfactory performance.

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Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning

Cited by 54 publications

References 24 publications

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem

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