Showing papers on "Routing (electronic design automation) published in 2017"

The electric vehicle routing problem with nonlinear charging function

Abstract: Electric vehicle routing problems (E-VRPs) extend classical routing problems to consider the limited driving range of electric vehicles. In general, this limitation is overcome by introducing planned detours to battery charging stations. Most existing E-VRP models assume that the battery-charge level is a linear function of the charging time, but in reality the function is nonlinear. In this paper we extend current E-VRP models to consider nonlinear charging functions. We propose a hybrid metaheuristic that combines simple components from the literature and components specifically designed for this problem. To assess the importance of nonlinear charging functions, we present a computational study comparing our assumptions with those commonly made in the literature. Our results suggest that neglecting nonlinear charging may lead to infeasible or overly expensive solutions. Furthermore, to test our hybrid metaheuristic we propose a new 120-instance testbed. The results show that our method performs well on these instances.

Survey of Photonic Switching Architectures and Technologies In Support Of Spatially and Spectrally Flexible Optical Networking {[}Invited]

Abstract: As traffic volumes carried by optical networks continue to grow by tens of percent year over year, we are rapidly approaching the capacity limit of the conventional communication band within a single-mode fiber. New measures such as elastic optical networking, spectral extension to multi-bands, and spatial expansion to additional fiber overlays or new fiber types are all being considered as potential solutions, whether near term or far. In this tutorial paper, we survey the photonic switching hardware solutions in support of evolving optical networking solutions enabling capacity expansion based on the proposed approaches. We also suggest how reconfigurable add/drop multiplexing nodes will evolve under these scenarios and gauge their properties and relative cost scalings. We identify that the switching technologies continue to evolve and offer network operators the required flexibility in routing information channels in both the spectral and spatial domains. New wavelength-selective switch designs can now support greater resolution, increased functionality and packing density, as well as operation with multiple input and output ports. Various switching constraints can be applied, such as routing of complete spatial superchannels, in an effort to reduce the network cost and simplify the routing protocols and managed pathway count. However, such constraints also reduce the transport efficiency when the network is only partially loaded, and may incur fragmentation. System tradeoffs between switching granularity and implementation complexity and cost will have to be carefully considered for future high-capacity SDM–WDM optical networks. In this work, we present the first cost comparisons, to our knowledge, of the different approaches in an effort to quantify such tradeoffs.

Learning to Route

Abstract: Recently, much attention has been devoted to the question of whether/when traditional network protocol design, which relies on the application of algorithmic insights by human experts, can be replaced by a data-driven (i.e., machine learning) approach. We explore this question in the context of the arguably most fundamental networking task: routing. Can ideas and techniques from machine learning (ML) be leveraged to automatically generate "good" routing configurations? We focus on the classical setting of intradomain traffic engineering. We observe that this context poses significant challenges for data-driven protocol design. Our preliminary results regarding the power of data-driven routing suggest that applying ML (specifically, deep reinforcement learning) to this context yields high performance and is a promising direction for further research. We outline a research agenda for ML-guided routing.

A simulated annealing heuristic for the hybrid vehicle routing problem

Abstract: Display Omitted This research proposes the hybrid vehicle routing problem (HVRP), which is an extension of the green vehicle routing problem.A simulated annealing (SA) heuristic is proposed to solve HVRP.Computational results show that the proposed SA effectively solves HVRP.Sensitivity analysis has been conducted to understand the effect of hybrid vehicles and charging stations on the travel cost. This study proposes the Hybrid Vehicle Routing Problem (HVRP), which is an extension of the Green Vehicle Routing Problem (G-VRP). We focus on vehicles that use a hybrid power source, known as the Plug-in Hybrid Electric Vehicle (PHEV) and generate a mathematical model to minimize the total cost of travel by driving PHEV. Moreover, the model considers the utilization of electric and fuel power depending on the availability of either electric charging or fuel stations.We develop simulated annealing with a restart strategy (SA_RS) to solve this problem, and it consists of two versions. The first version determines the acceptance probability of a worse solution using the Boltzmann function, denoted as SA_RSBF. The second version employs the Cauchy function to determine the acceptance probability of a worse solution, denoted as SA_RSCF. The proposed SA algorithm is first verified with benchmark data of the capacitated vehicle routing problem (CVRP), with the result showing that it performs well and confirms its efficiency in solving CVRP. Further analysis show that SA_RSCF is preferable compared to SA_RSBF and that SA with a restart strategy performs better than without a restart strategy. We next utilize the SA_RSCF method to solve HVRP. The numerical experiment presents that vehicle type and the number of electric charging stations have an impact on the total travel cost.

Flying ad-hoc networks (FANETs): A review of communication architectures, and routing protocols

Abstract: With recent technological progress in the field of electronics, sensors and communication systems, the production of small UAVs (Unmanned Air Vehicles) became possible, which can be used for several military, commercial and civilian applications However, the capability of a single and small UAV is inadequate Multiple-UAVs can make a system that is beyond the limitations of a single small UAV A Flying Ad hoc Networks (FANETs) is such kind of network that consists of a group of small UAVs connected in ad-hoc manner, which are integrated into a team to achieve high level goals Mobility, lack of central control, self-organizing and ad-hoc nature between the UAVs are the main features of FANETs, which could expand the connectivity and extend the communication range at infrastructure-less area On one hand, in case of catastrophic situations when ordinary communication infrastructure is not available, FANETs can be used to provide a rapidly deployable, flexible, self-configurable and relatively small operating expenses network; the other hand connecting multiple UAVs in ad-hoc network is a big challenge This level of coordination requires an appropriate communication architecture and routing protocols that can be set up on highly dynamic flying nodes in order to establish a reliable and robust communication The main contribution of this paper include the introduction of suitable communication architecture, and an overview of different routing protocols for FANETs The open research issues of existing routing protocols are also investigated in this paper

Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Abstract: Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers’ pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow-based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.

Optimal Routing for Quantum Networks

Abstract: To fully unleash the potentials of quantum computing, several new challenges and open problems need to be addressed. From a routing perspective, the optimal routing problem , i.e., the problem of jointly designing a routing protocol and a route metric assuring the discovery of the route providing the highest quantum communication opportunities between an arbitrary couple of quantum devices, is crucial. In this paper, the optimal routing problem is addressed for generic quantum network architectures composed by repeaters operating through single atoms in optical cavities. Specifically, we first model the entanglement generation through a stochastic framework that allows us to jointly account for the key physical-mechanisms affecting the end-to-end entanglement rate, such as decoherence time, atom–photon and photon–photon entanglement generation, entanglement swapping, and imperfect Bell-state measurement. Then, we derive the closed-form expression of the end-to-end entanglement rate for an arbitrary path and we design an efficient algorithm for entanglement rate computation. Finally, we design a routing protocol and we prove its optimality when used in conjunction with the entanglement rate as routing metric.

Energy harvesting and battery power based routing in wireless sensor networks

Abstract: Wireless sensor networks (WSNs) are a collection of several small and inexpensive battery-powered nodes, commonly used to monitor regions of interests and to collect data from the environment. Several issues exist in routing data packets through WSN, but the most crucial problem is energy. There are a number of routing approaches in WSNs that address the issue of energy by the use of different energy-efficient methods. This paper, presents a brief summary of routing and related issues in WSNs. The most recent energy-efficient data routing approaches are reviewed and categorized based on their aims and methodologies. The traditional battery based energy sources for sensor nodes and the conventional energy harvesting mechanisms that are widely used to in energy replenishment in WSN are reviewed. Then a new emerging energy harvesting technology that uses piezoelectric nanogenerators to supply power to nanosensor; the type of sensors that cannot be charged by conventional energy harvesters are explained. The energy consumption reduction routing strategies in WSN are also discussed. Furthermore, comparisons of the variety of energy harvesting mechanisms and battery power routing protocols that have been discussed are presented, eliciting their advantages, disadvantages and their specific feature. Finally, a highlight of the challenges and future works in this research domain is presented.

A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications

Abstract: The fact that electric vehicles (EVs) are characterized by relatively short driving range not only signifies the importance of routing applications to compute energy efficient or optimal paths, but also underlines the necessity for realistic simulation models to estimate the energy consumption of EVs. To this end, the present paper introduces an accurate yet computationally efficient energy consumption model for EVs, based on generic high-level specifications and technical characteristics. The proposed model employs a dynamic approach to simulate the energy recuperation capability of the EV and takes into account motor overload conditions to represent the vehicle performance over highly demanding route sections. To validate the simulation model developed in this work, its output over nine typical driving cycles is compared to that of the Future Automotive Systems Technology Simulator (FASTSim), which is a simulation tool tested on the basis of real-world data from existing vehicles. The validation results show that the mean absolute error (MAE) of cumulative energy consumption is less than 45 W h on average, while the computation time to perform each driving cycle is of the order of tens of milliseconds, indicating that the developed model strikes a reasonable balance between efficacy of representation and computational efficiency. Comprehensive simulation results are presented in order to exemplify the key features of the model and analyze its output under specific highly aggressive driving cycles for road gradients ranging from −6% to 6%, in support of its usability as a practical solution for estimating the energy consumption in EV routing applications.

Hierarchical Address Event Routing for Reconfigurable Large-Scale Neuromorphic Systems

Abstract: We present a hierarchical address-event routing (HiAER) architecture for scalable communication of neural and synaptic spike events between neuromorphic processors, implemented with five Xilinx Spartan-6 field-programmable gate arrays and four custom analog neuromophic integrated circuits serving 262k neurons and 262M synapses. The architecture extends the single-bus address-event representation protocol to a hierarchy of multiple nested buses, routing events across increasing scales of spatial distance. The HiAER protocol provides individually programmable axonal delay in addition to strength for each synapse, lending itself toward biologically plausible neural network architectures, and scales across a range of hierarchies suitable for multichip and multiboard systems in reconfigurable large-scale neuromorphic systems. We show approximately linear scaling of net global synaptic event throughput with number of routing nodes in the network, at $3.6\times 10^{7}$ synaptic events per second per 16k-neuron node in the hierarchy.

An Exact Algorithm for the Green Vehicle Routing Problem

Abstract: We propose an exact algorithm for solving the green vehicle routing problem (G-VRP). The G-VRP models the optimal routing of an alternative fuel vehicle fleet to serve a set of geographically scattered customers. Vehicles’ fuel autonomy and possible refueling stops en route are explicitly modeled and maximum duration constraints are imposed on each vehicle route. We model the G-VRP as a set partitioning problem in which columns represent feasible routes corresponding to simple circuits in a multigraph. Each node in the multigraph represents one customer and each arc between two customers represents a nondominated path through a set of refueling stations visited by a vehicle when traveling directly between the two customers. We strengthen the set partitioning formulation by adding valid inequalities including k-path cuts and describe a method for separating them. We provide computational results on benchmark instances showing that the algorithm can optimally solve instances with up to ∼110 customers. The o...

Incremental Deployment and Throughput Maximization Routing for a Hybrid SDN

Abstract: To explore the advantages of software defined network (SDN), while preserving the legacy networking systems, a natural deployment strategy is to deploy a hybrid SDN incrementally to improve the network performance. In this paper, we address two technical challenges: an incremental deployment strategy and a throughput-maximization routing, for deploying a hybrid network incrementally. For incremental deployment, we propose a heuristic algorithm for deploying a hybrid SDN under the budget constraint, and prove the approximate factor of $ 1- \frac {1}{e} $ . For throughput-maximization routing, we apply a depth-first-search method and a randomized rounding mechanism to solve the multi-commodity $h$ -splittable flow routing problem in a hybrid SDN, where $h\ge 1$ . We also prove that our method has approximation ratio $O\left({\frac {1}{\log N}}\right)$ , where $ N $ is the number of links in a hybrid SDN. We then show, by both analysis and simulations, that our algorithms can obtain significant performance gains and perform better than the theoretical worst-case bound. For example, our incremental deployment scheme helps to enhance the throughout about 40% compared with the previous deployment scheme by deploying a small number of SDN devices, and the proposed routing algorithm can improve the throughput about 31% compared with ECMP in hybrid networks.

Improve Service Chaining Performance with Optimized Middlebox Placement

Abstract: Previous works have proposed various approaches to implement service chaining by routing traffic through the desired middleboxes according to pre-defined policies. However, no matter what routing scheme is used, the performance of service chaining depends on where these middleboxes are placed. Thus, in this paper, we study middlebox placement problem, i.e., given network information and policy specifications, we attempt to determine the optimal locations to place the middleboxes so that the performance is optimized. The performance metrics studied in this paper include the end-to-end delay and the bandwidth consumption, which cover both users’ and network providers’ interests. We first formulate it as 0-1 programming problem, and prove it is NP-hard. We then propose two heuristic algorithms to obtain the sub-optimal solutions. The first algorithm is a greedy algorithm, and the second algorithm is based on simulated annealing. Through extensive simulations, we show that in comparison with a baseline algorithm, the proposed algorithms can reduce 22 percent end-to-end delay and save 38 percent bandwidth consumption on average. The formulation and proposed algorithms have no special assumption on network topology or policy specifications, therefore, they have broad range of applications in various types of networks such as enterprise, data center and broadband access networks.

Distributed Service Function Chaining

Abstract: A service-function chain, or simply a chain, is an ordered sequence of service functions, e.g., firewalls and load balancers, composing a service. A chain deployment involves selecting and instantiating a number of virtual network functions (VNFs), i.e., softwarized service functions, placing VNF instances, and routing traffic through them. In the current optimization-models of a chain deployment, the instances of the same function are assumed to be identical, while typical service providers offer VNFs with heterogeneous throughput and resource configurations. The VNF instances of the same function are installed in a single physical machine, which limits a chain to the throughput of a few instances that can be installed in one physical machine. Furthermore, the selection , placement , and routing problems are solved in isolation. We present distributed service function chaining that coordinates these operations, places VNF-instances of the same function distributedly , and selects appropriate instances from typical VNF offerings. Such a deployment uses network resources more efficiently and decouples a chain’s throughput from that of physical machines. We formulate this deployment as a mixed integer programming (MIP) model, prove its NP-Hardness, and develop a local search heuristic called Kariz. Extensive experiments demonstrate that Kariz achieves a competitive acceptance-ratio of 76%–100% with an extra cost of less than 24% compared with the MIP model.

Joint optimisation of order batching and picker routing in the online retailer’s warehouse in China

Abstract: Order picking is the core of warehouse operations and considerable researches have been conducted on improving its efficiency. In this paper, we aim at the joint optimisation of order batching and picker routing based on a famous and typical online retailer of China, which mainly focuses on fast-moving consumer goods. An integer programming is formulated to minimise the total travelling distance involving with order batching and picker routing. In the stage of order batching, an effective batching procedure based on similarity coefficient which is measured by overlapping channels between orders is proposed. In the stage of picker routing, an improved ant colony optimisation algorithm with local search is proposed. Based on those simulated orders generated by actual transaction data, numerical experiments are conducted to verify the performance of the algorithm we proposed. Results show that the proposed joint optimisation algorithm has potential advantages under various order sizes and order structures, w...

Orchestrating Bulk Data Transfers across Geo-Distributed Datacenters

Abstract: As it has become the norm for cloud providers to host multiple datacenters around the globe, significant demands exist for inter-datacenter data transfers in large volumes, e.g., migration of big data. A challenge arises on how to schedule the bulk data transfers at different urgency levels, in order to fully utilize the available inter-datacenter bandwidth. The Software Defined Networking (SDN) paradigm has emerged recently which decouples the control plane from the data paths, enabling potential global optimization of data routing in a network. This paper aims to design a dynamic, highly efficient bulk data transfer service in a geo-distributed datacenter system, and engineer its design and solution algorithms closely within an SDN architecture. We model data transfer demands as delay tolerant migration requests with different finishing deadlines. Thanks to the flexibility provided by SDN, we enable dynamic, optimal routing of distinct chunks within each bulk data transfer (instead of treating each transfer as an infinite flow), which can be temporarily stored at intermediate datacenters to mitigate bandwidth contention with more urgent transfers. An optimal chunk routing optimization model is formulated to solve for the best chunk transfer schedules over time. To derive the optimal schedules in an online fashion, three algorithms are discussed, namely a bandwidth-reserving algorithm, a dynamically-adjusting algorithm, and a future-demand-friendly algorithm, targeting at different levels of optimality and scalability. We build an SDN system based on the Beacon platform and OpenFlow APIs, and carefully engineer our bulk data transfer algorithms in the system. Extensive real-world experiments are carried out to compare the three algorithms as well as those from the existing literature, in terms of routing optimality, computational delay and overhead.

An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations

Abstract: Efficiency is a key success factor in complex supply chain networks. It is imperative to ensure proper flow of goods and services in humanitarian supply chains in response to a disaster. To this end, we propose a multi-echelon humanitarian logistic network that considers the location of central warehouses, managing the inventory of perishable products in the pre-disaster phase, and routing the relief vehicles in the post-disaster phase. An epsilon-constraint method, a non-dominated sorting genetic algorithm (NSGA-II), and a modified NSGA-II called reference point based non-dominated sorting genetic algorithm-II (RPBNSGA-II) are proposed to solve this mixed integer linear programming (MILP) problem. The analysis of variance (ANOVA) is used to analyze the results showing that NSGA-II performs better than the other algorithms with small size problems while RPBNSGA-II outperforms the other algorithms with large size problems.

Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

Abstract: The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.

A branch-and-price algorithm for production routing problems with carbon cap-and-trade

Abstract: This study presents a model for a pollution production-routing problem under carbon cap-and-trade. The aim is to incorporate carbon emissions into production inventory and routing decisions. The model is characterized by an additional flow-related cost structure, which generalizes models for pollution-routing problems and production inventory and routing problems. Correspondingly, we develop a branch-and-price heuristic by incorporating a column-generation formulation based on the Dantzig–Wolfe decomposition. In addition, we design an ad hoc label-setting algorithm to deal with time-slice networks in pricing subproblems. Computational results allow us to provide managerial insights concerning reduction of carbon emissions in supply chains. We prove that the model has the potential to reduce emission levels of carbon dioxide and operational costs.

Time-dependent green vehicle routing problem with stochastic vehicle speeds: An approximate dynamic programming algorithm

Abstract: This paper addresses a Time Dependent Capacitated Vehicle Routing Problem with stochastic vehicle speeds and environmental concerns The problem has been formulated as a Markovian Decision Process As distinct from the traditional attempts on the problem, while estimating the amount of fuel consumption and emissions, the model takes time-dependency and stochasticity of the vehicle speeds into account The Time Dependent Capacitated Vehicle Routing Problem is known to be NP-Hard for even deterministic settings Incorporating uncertainty to the problem increases complexity, which renders classical optimization methods infeasible Therefore, we propose an Approximate Dynamic Programming based heuristic as a decision aid tool for the problem The proposed Markovian Decision Model and Approximate Dynamic Programming based heuristic are flexible in terms that more environmentally friendly solutions can be obtained by changing the objective function from cost minimization to emissions minimization The added values of the proposed decision support tools have been shown through computational analyses on several instances The computational analyses show that incorporating vehicle speed stochasticity into decision support models has potential to improve the performance of resulting routes in terms of travel duration, emissions and travel cost In addition, the proposed heuristic provides promising results within relatively short computation times