Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 14 of 14
Full-Text Articles in Physical Sciences and Mathematics
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Research Collection School Of Computing and Information Systems
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance …
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Research Collection School Of Computing and Information Systems
This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac …
Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen
Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen
Research Collection School Of Computing and Information Systems
Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated …
Learning Improvement Heuristics For Solving Routing Problems, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim
Learning Improvement Heuristics For Solving Routing Problems, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim
Research Collection School Of Computing and Information Systems
Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by hand-crafted rules which may limit their performance. In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention based deep architecture as the policy network to guide the selection of next solution. We apply our method to two important routing problems, i.e. travelling salesman …
Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
Research Collection School Of Computing and Information Systems
Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the …
Step-Wise Deep Learning Models For Solving Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Step-Wise Deep Learning Models For Solving Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Routing problems are very important in intelligent transportation systems. Recently, a number of deep learning-based methods are proposed to automatically learn construction heuristics for solving routing problems. However, these methods do not completely follow Bellman's Principle of Optimality since the visited nodes during construction are still included in the following subtasks, resulting in suboptimal policies. In this article, we propose a novel step-wise scheme which explicitly removes the visited nodes in each node selection step. We apply this scheme to two representative deep models for routing problems, pointer network and transformer attention model (TAM), and significantly improve the performance of …
Multi-Decoder Attention Model With Embedding Glimpse For Solving Vehicle Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Multi-Decoder Attention Model With Embedding Glimpse For Solving Vehicle Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that train only one policy. A customized beam search strategy is designed to fully exploit the diversity of MDAM. In addition, we propose an Embedding Glimpse layer in MDAM based on the recursive nature of construction, which can improve the quality of each policy by providing more informative embeddings. Extensive experiments on six different routing problems show …
Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou
Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou
Research Collection School Of Computing and Information Systems
This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. …
Energy-Neutral Scheduling And Forwarding In Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Weng Seng Soh, Hwee-Pink Tan
Energy-Neutral Scheduling And Forwarding In Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Weng Seng Soh, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In environmentally-powered wireless sensor networks (EPWSNs), low latency wakeup scheduling and packet forwarding is challenging due to dynamic duty cycling, posing time-varying sleep latencies and necessitating the use of dynamic wakeup schedules. We show that the variance of the intervals between receiving wakeup slots affects the expected sleep latency: when the variance of the intervals is low (high), the expected latency is low (high). We therefore propose a novel scheduling scheme that uses the bit-reversal permutation sequence (BRPS) – a finite integer sequence that positions receiving wakeup slots as evenly as possible to reduce the expected sleep latency. At the …
Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim
Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim
Research Collection School Of Computing and Information Systems
We address the problem of minimizing the worst-case broadcast delay in multi-radio multi-channel multi-rate (MR2-MC) wireless mesh networks (WMN). The problem of 'efficient' broadcast in such networks is especially challenging due to the numerous interrelated decisions that have to be made. The multi-rate transmission capability of WMN nodes, interference between wireless transmissions, and the hardness of optimal channel assignment adds complexity to our considered problem. We present four heuristic algorithms to solve the minimum latency broadcast problem for such settings and show that the 'best' performing algorithms usually adapt themselves to the available radio interfaces and channels. We also study …
Using Trusted Computing Technology To Facilitate Security Enforcement In Wireless Sensor Networks, Yanjiang Yang, Robert H. Deng, Feng Bao, Jianying Zhou
Using Trusted Computing Technology To Facilitate Security Enforcement In Wireless Sensor Networks, Yanjiang Yang, Robert H. Deng, Feng Bao, Jianying Zhou
Research Collection School Of Computing and Information Systems
Security enforcement in wireless sensor networks is by no means an easy task, due to the inherent resource-constrained nature of sensor nodes. To facilitate security enforcement, we propose to incorporate more powerful high-end Security Enforcement Facilitators (SEFs) into wireless sensor networks. In particular, the SEFs are equipped with TCG-compliant Trusted Platform Modules (TPMs) to protect cryptographic secrets, perform authenticated booting and attest their platform state to a remote base station.As such, the SEFs act as online trusted third parties toeffectively monitor the states of sensor nodes, help in keymanagement, simplify secure routing, and facilitate accesscontrol.
Rate-Diversity And Resource-Aware Broadcast And Multicast In Multi-Rate Wireless Mesh Networks, Bao Hua Liu, Chun Tung Chou, Archan Misra, Sanjay Jha
Rate-Diversity And Resource-Aware Broadcast And Multicast In Multi-Rate Wireless Mesh Networks, Bao Hua Liu, Chun Tung Chou, Archan Misra, Sanjay Jha
Research Collection School Of Computing and Information Systems
This paper focuses on the problem of increasing the traffic capacity (volume of admissible traffic) of broadcast and multicast flows in a wireless mesh network (WMN). We study and suggest routing strategies where the process of constructing the forwarding tree considers three distinct features: (a) the ability of individual mesh nodes to perform link-layer broadcasts at multiple rates, (b) the wireless broadcast advantage, whereby a single broadcast transmission covers multiple neighboring receivers and (c) the residual transmission capacity at a WMN node, subject to intereference-based constraints from existing traffic flows in its neighborhood. Our metric of interest is the total …
Minimum Energy Reliable Paths Using Unreliable Wireless Links, Qunfeng Dong, Suman Banerjee, Micah Adler, Archan Misra
Minimum Energy Reliable Paths Using Unreliable Wireless Links, Qunfeng Dong, Suman Banerjee, Micah Adler, Archan Misra
Research Collection School Of Computing and Information Systems
We address the problem of energy-efficient reliable wireless communication in the presence of unreliable or lossy wireless link layers in multi-hop wireless networks. Prior work [1] has provided an optimal energy efficient solution to this problem for the case where link layers implement perfect reliability. However, a more common scenario --- a link layer that is not perfectly reliable, was left as an open problem. In this paper we first present two centralized algorithms, BAMER and GAMER, that optimally solve the minimum energy reliable communication problem in presence of unreliable links. Subsequently we present a distributed algorithm, DAMER, that approximates …
Minimum Energy Paths For Reliable Communication In Multi-Hop Wireless Networks, Suman Banerjee, Archan Misra
Minimum Energy Paths For Reliable Communication In Multi-Hop Wireless Networks, Suman Banerjee, Archan Misra
Research Collection School Of Computing and Information Systems
Current algorithms for minimum-energy routing in wireless networks typically select minimum-cost multi-hop paths. In scenarios where the transmission power is fixed, each link has the same cost and the minimum-hop path is selected. In situations where the transmission power can be varied with the distance of the link, the link cost is higher for longer hops; the energy-aware routing algorithms select a path with a large number of small-distance hops. In this paper, we argue that such a formulation based solely on the energy spent in a single transmission is misleading --- the proper metric should include the total energy …