Showing papers on "Routing (electronic design automation) published in 2015"
TL;DR: In this article, the authors proposed an Adaptive Large Neighborhood Search algorithm that is enhanced by a local search for intensification to optimize the routing of a mixed fleet of electric commercial vehicles and conventional internal combustion commercial vehicles (ICCVs).
372 citations
TL;DR: The purpose of the paper is to provide a comprehensive and relevant taxonomy for the RVRP literature and to propose an elaborate definition of RVRPs.
243 citations
TL;DR: This article provides a comprehensive review of various solution techniques that have been proposed to solve the production routing problem and attempts to provide an in-depth summary and discussion of different formulation schemes and of algorithmic and computational issues.
203 citations
TL;DR: A parallel simulated annealing algorithm that includes a Residual Capacity and Radial Surcharge insertion-based heuristic is developed and applied to solve a variant of the vehicle routing problem in which customers require simultaneous pickup and delivery of goods during specific individual time windows.
181 citations
TL;DR: In this article, a branch-and-cut algorithm is proposed to solve the production routing problem with demand uncertainty in two-stage and multistage decision processes, where the decisions in the first stage include production setups and customer visit schedules, while the production and delivery quantities are determined in the subsequent stages.
Abstract: The production routing problem (PRP) is a generalization of the inventory routing problem and concerns the production and distribution of a single product from a production plant to multiple customers using capacitated vehicles in a discrete- and finite-time horizon. In this study, we consider the stochastic PRP with demand uncertainty in two-stage and multistage decision processes. The decisions in the first stage include production setups and customer visit schedules, while the production and delivery quantities are determined in the subsequent stages. We introduce formulations for the two problems, which can be solved by a branch-and-cut algorithm. To handle a large number of scenarios, we propose a Benders decomposition approach, which is implemented in a single branch-and-bound tree and enhanced through lower-bound lifting inequalities, scenario group cuts, and Pareto-optimal cuts. For the multistage problem, we also use a warm start procedure that relies on the solution of the simpler two-stage prob...
154 citations
TL;DR: The proposed distributed clustering and routing algorithms jointly referred as DFCR is shown to be energy efficient and fault tolerant and compared with the existing algorithms to demonstrate the strength of the algorithm in terms of various performance metrics.
151 citations
01 Jun 2015
TL;DR: This work proposes the Expected Lifetime metric, denoting the residual time of a node (time until the node will run out of energy) and applies this metric to RPL, the de facto routing standard in low-power and lossy networks.
Abstract: Energy is a very scarce resource in Wireless Sensor Networks. While most of the current proposals focus on minimizing the global energy consumption, we aim here at designing an energy-balancing routing protocol that maximizes the lifetime of the most constraint nodes. To improve the network lifetime, each node should consume the same (minimal) quantity of energy. We propose the Expected Lifetime metric, denoting the residual time of a node (time until the node will run out of energy). We design mechanisms to detect energy-bottleneck nodes and to spread the traffic load uniformly among them. Moreover, we apply this metric to RPL, the de facto routing standard in low-power and lossy networks. In order to avoid instabilities in the network and problems of convergence, we propose here a multipath approach. We exploit the Directed Acyclic Graph (DAG) structure of the routing topology to probabilistically forward the traffic to several parents. Simulations highlight that we improve both the routing reliability and the network lifetime, while reducing the number of DAG reconfigurations.
148 citations
TL;DR: This paper proposes an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed by the collectivity as a whole, to reduce the overall traffic in a smart city.
Abstract: Human mobility in a city represents a fascinating complex system that combines social interactions, daily constraints and random explorations. New collections of data that capture human mobility not only help us to understand their underlying patterns but also to design intelligent systems. Bringing us the opportunity to reduce traffic and to develop other applications that make cities more adaptable to human needs. In this paper, we propose an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed by the collectivity as a whole. Using big data sets recently released during the Telecom Italia Big Data Challenge, we show that our algorithm allows us to reduce the overall traffic in a smart city thanks to synergetic effects, with the participation of individuals in the system, playing a crucial role.
147 citations
TL;DR: An adaptive variable neighborhood search (AVNS) is developed to solve the vehicle routing problem with intermediate stops (VRPIS), which considers stopping requirements at intermediate facilities.
Abstract: There are numerous practical vehicle routing applications in which vehicles have to stop at certain facilities along their routes to be able to continue their service. At these stops, the vehicles replenish or unload their cargo or they stop to refuel. In this paper, we study the vehicle routing problem with intermediate stops (VRPIS), which considers stopping requirements at intermediate facilities. Service times occur at these stops and may depend on the load level or fuel level on arrival. This is incorporated into the routing model to respect route duration constraints. We develop an adaptive variable neighborhood search (AVNS) to solve the VRPIS. The adaptive mechanism guides the shaking step of the AVNS by favoring the route and vertex selection methods according to their success within the search. The performance of the AVNS is demonstrated on test instances for VRPIS variants available in the literature. Furthermore, we conduct tests on newly generated instances of the electric vehicle routing problem with recharging facilities, which can also be modeled as VRPIS variant. In this problem, battery electric vehicles need to recharge their battery en route at respective recharging facilities.
123 citations
TL;DR: This paper investigates the integrated optimization of production, distribution, and inventory decisions related to supplying multiple retailers from a central production facility through a two-phase iterative method that iteratively focuses on lot-sizing and distribution decisions.
Abstract: This paper investigates the integrated optimization of production, distribution, and inventory decisions related to supplying multiple retailers from a central production facility. A single-item capacitated lot-sizing problem is defined for optimizing production decisions and inventory management. The optimization of daily distribution is modeled as a traveling salesman problem or a vehicle routing problem depending on the number of vehicles. A two-phase iterative method, from which several heuristics are derived, is proposed that iteratively focuses on lot-sizing and distribution decisions. Computational results show that our best heuristic outperforms existing methods.
115 citations
29 Mar 2015
TL;DR: The ISPD~2015 placement-contest benchmarks include all the detailed pin, cell, and wire geometry constraints from the 2014 release, plus added fence regions and placement blockages and specified upper limits on local cell-area density.
Abstract: The ISPD~2015 placement-contest benchmarks include all the detailed pin, cell, and wire geometry constraints from the 2014 release, plus(a) added fence regions and placement blockages,(b) altered netlists including fixed macro blocks,(c) reduced standard cell area utilization via larger floorplan outlines, and(d)] specified upper limits on local cell-area density.Compared to the 2014 release, these new constraints add realism and increase the difficulty of producing detail-routable wirelength-driven placements.
22 Apr 2015
TL;DR: This paper forms the problem of network function placement and routing as a mixed integer linear programming problem, and develops heuristics to solve the problem incrementally, allowing it to support a large number of flows and to solving the problem for incoming flows without impacting existing flows.
Abstract: The integration of network function virtualization (NFV) and software defined networks (SDN) seeks to create a more flexible and dynamic software-based network environment. The line between entities involved in forwarding and those involved in more complex middle box functionality in the network is blurred by the use of high-performance virtualized platforms capable of performing these functions. A key problem is how and where network functions should be placed in the network and how traffic is routed through them. An efficient placement and appropriate routing increases system capacity while also minimizing the delay seen by flows. In this paper, we formulate the problem of network function placement and routing as a mixed integer linear programming problem. This formulation not only determines the placement of services and routing of the flows, but also seeks to minimize the resource utilization. We develop heuristicsto solve the problem incrementally, allowing us to support a large number of flows and to solve the problem for incoming flows without impacting existing flows.
TL;DR: This paper incorporates fuel cost, carbon emission cost, and vehicle usage cost into the traditional VRP problem and establishes a low-carbon routing problem model based on the route splitting method, and develops an improved tabu search algorithm named RS-TS for solving the model.
01 Jan 2015
TL;DR: This document presents a security threat analysis for the Routing Protocol for Low-Power and Lossy Networks (RPLs) that builds upon previous work on routing security and adapts the assessments to the issues and constraints specific to low-power and lossy networks.
Abstract: This document presents a security threat analysis for the Routing
Protocol for Low-Power and Lossy Networks (RPLs). The development
builds upon previous work on routing security and adapts the
assessments to the issues and constraints specific to low-power and
lossy networks. A systematic approach is used in defining and
evaluating the security threats. Applicable countermeasures are
application specific and are addressed in relevant applicability
statements.
Book•
13 Nov 2015
TL;DR: This paper presents a meta-modelling framework for estimating the resilience of wireless mesh networks to disruption-tolerant routing in Vehicular Ad-hoc Networks and some of the mechanisms that control their resilience.
Abstract: Introduction Principles of Communication Networks Resilience Resilience of Future Internet Communications Resilience of Wireless Mesh Networks Disruption-tolerant Routing in Vehicular Ad-hoc Networks
TL;DR: An algorithm integrating hybrid-coded genetic algorithm and ant colony optimization is developed to efficiently tackle the proposed nonlinear IOBSRP model and results show that the proposed hybrid algorithm has more advantage in the light of solution quality as compared with multiple-GA and due-date first approach.
Patent•
01 May 2015
TL;DR: In this paper, an intelligent routing system is proposed for routing callers to agents in a contact center, along with a routing system that is graded on achieving an optimal interaction, such as increasing revenue, decreasing cost, or increasing customer satisfaction.
Abstract: Methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. One or more agents are graded on achieving an optimal interaction, such as increasing revenue, decreasing cost, or increasing customer satisfaction. Callers are then preferentially routed to a graded agent to obtain an increased chance at obtaining a chosen optimal interaction. In a more advanced embodiment, caller and agent demographic and psychographic characteristics can also be determined and used in a pattern matching algorithm to preferentially route a caller with certain characteristics to an agent with certain characteristics to increase the chance of an optimal interaction.
TL;DR: The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new, and combines two state-of-the-art metaheuristic concepts.
TL;DR: The results obtained show that the proposed scheme performs better than the benchmark chosen in this study, as there is a 30% reduction in network delay and a 20% increase in packet delivery ratio.
Abstract: In vehicular sensor networks (VSNs), an increase in the density of the vehicles on road and route jamming in the network causes delay in receiving the emergency alerts, which results in overall system performance degradation. In order to address this issue in VSNs deployed in dense urban regions, in this paper, we propose collaborative learning automata-based routing algorithm for sending information to the intended destination with minimum delay and maximum throughput. The learning automata (LA) stationed at the nearest access points (APs) in the network learn from their past experience and make routing decisions quickly. The proposed strategy consists of dividing the whole region into different clusters, based on which an optimized path is selected using collaborative LA having input parameters as vehicle density, distance from the nearest service unit, and delay. A theoretical expression for density estimation is derived, which is used for the selection of the “best” path by LA. The performance of the proposed scheme is evaluated with respect to metrics such as packet delivery delay (network delay), packet delivery ratio with varying node (vehicle) speed, transmission range, density of vehicle, and number of road side units/APs). The results obtained show that the proposed scheme performs better than the benchmark chosen in this study, as there is a 30% reduction in network delay and a 20% increase in packet delivery ratio.
TL;DR: In this article, a new modeling approach for integrating speed optimization in the planning of shipping routes, as well as a rolling horizon heuristic for solving the combined problem is proposed, which yields good solutions to the integrated problem within reasonable time.
TL;DR: This paper proposes a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm that enables to integrate more tightly route and speed decisions.
Posted Content•
01 Jan 2015
TL;DR: An Adaptive Large Neighborhood Search algorithm that is enhanced by a local search for intensification is developed to optimize the routing of a mixed fleet of electric commercial vehicles (ECVs) and conventional internal combustioncommercial vehicles (ICCVs).
Abstract: In this paper, we propose the Electric Vehicle Routing Problem with Time Windows and Mixed Fleet (E-VRPTWMF) to optimize the routing of a mixed fleet of electric commercial vehicles (ECVs) and conventional internal combustion commercial vehicles (ICCVs). Contrary to existing routing models for ECVs, which assume energy consumption to be a linear function of traveled distance, we utilize a realistic energy consumption model that incorporates speed, gradient and cargo load distribution. This is highly relevant in the context of ECVs because energy consumption determines the maximal driving range of ECVs and the recharging times at stations. To address the problem, we develop an Adaptive Large Neighborhood Search algorithm that is enhanced by a local search for intensification. In numerical studies on newly designed E-VRPTWMF test instances, we investigate the effect of considering the actual load distribution on the structure and quality of the generated solutions. Moreover, we study the influence of different objective functions on solution attributes and on the contribution of ECVs to the overall routing costs. Finally, we demonstrate the performance of the developed algorithm on benchmark instances of the related problems VRPTW and E-VRPTW.
TL;DR: An effective algorithm based on particle swarm optimization is presented to construct a multilayer obstacle-avoiding X-architecture SMT (ML-OAXSMT), which is the first work to address this problem and can offer the theory supports for chip design based on non-Manhattan architecture.
Abstract: As the basic model for very large scale integration routing, the Steiner minimal tree (SMT) can be used in various practical problems, such as wire length optimization, congestion, and time delay estimation. In this paper, an effective algorithm based on particle swarm optimization is presented to construct a multilayer obstacle-avoiding X-architecture SMT (ML-OAXSMT). First, a pretreatment strategy is presented to reduce the total number of judgments for the routing conditions around obstacles and vias. Second, an edge transformation strategy is employed to make the particles have the ability to bypass the obstacles while the union-find partition is used to prevent invalid solutions. Third, according to the feature of ML-OAXSMT problem, we design an edge-vertex encoding strategy, which has the advantage of simple and effective. Moreover, a penalty mechanism is proposed to help the particle bypass the obstacles, and reduce the generation of via at the same time. Experimental results show that our algorithm from a global perspective of multilayer structure can achieve the best solution quality among the existing algorithms. Finally, to our best knowledge, we redefine the edge cost and then construct the obstacle-avoiding preferred direction X-architecture Steiner tree, which is the first work to address this problem and can offer the theory supports for chip design based on non-Manhattan architecture.
TL;DR: This paper considers how to compute the start-service time and arrival time distributions for each customer to create a feasibility check that can be “plugged” into any algorithm for the SVRPTW and thus be used to solve large problems fairly quickly.
TL;DR: A classification framework based on the following elements: production, inventory and routing aspects, modelling aspects of the objective function structure and solution approach will provide researchers and practitioners a starting point for optimization models in the production and routing area at the tactical level.
TL;DR: The results show that the multi-channel quantum routing of single photons can be well achieved in the proposed system and offers a scheme for the experimental realization of general quantum routingof single photons.
Abstract: The routing capability is a requisite in quantum network. Although the quantum routing of signals has been investigated in various systems both in theory and experiment, the general form of quantum routing with many output terminals still needs to be explored. Here we propose a scheme to achieve the multi-channel quantum routing of the single photons in a waveguide-emitter system. The channels are composed by the waveguides and are connected by intermediate two-level emitters. By adjusting the intermediate emitters, the output channels of the input single photons can be controlled. This is demonstrated in the cases of one output channel, two output channels and the generic N output channels. The results show that the multi-channel quantum routing of single photons can be well achieved in the proposed system. This offers a scheme for the experimental realization of general quantum routing of single photons.
TL;DR: The proposed algorithm is based on a flexible large neighborhood search that is applied to the entire solution of the consistent vehicle routing problem ConVRP and outperforms template-based approaches in terms of travel cost and time consistency.
Abstract: The consistent vehicle routing problem ConVRP takes customer satisfaction into account by assigning one driver to a customer and by bounding the variation in the arrival times over a given planning horizon. These requirements may be too restrictive in some applications. In the generalized ConVRP GenConVRP, each customer is visited by a limited number of drivers and the variation in the arrival times is penalized in the objective function. The vehicle departure times may be adjusted to obtain stable arrival times. Additionally, customers are associated with AM/PM time windows. In contrast to previous work on the ConVRP, we do not use the template concept to generate routing plans. Our approach is based on a flexible large neighborhood search that is applied to the entire solution. Several destroy and repair heuristics have been designed to remove customers from the routes and to reinsert them at better positions. Arrival time consistency is improved by a simple 2-opt operator that reverses parts of particular routes.
A computational study is performed on ConVRP benchmark instances and on new instances generated for the generalized problem. The proposed algorithm performs well on different variants of the ConVRP. It outperforms template-based approaches in terms of travel cost and time consistency. For the GenConVRP, we experiment with different input parameters and examine the trade-off between travel cost and customer satisfaction. Remarkable cost savings can be obtained by allowing more than one driver per customer.
01 May 2015
TL;DR: Experimental results indicate that the proposed MOPSO is worthy of being studied in the field of multi-objective optimization problems, and the proposed algorithm has a better tradeoff between the wire length and radius of the routing tree and has achieved a better delay value.
Abstract: Constructing a timing-driven Steiner tree is very important in VLSI performance-driven routing stage. Meanwhile, non-Manhattan architecture is supported by several manufacturing technologies and now well appreciated in the chip manufacturing circle. However, limited progress has been reported on the non-Manhattan performance-driven routing problem. In this paper, an efficient algorithm, namely, TOST_BR_MOPSO, is presented to construct the minimum-cost spanning tree with a minimum radius for performance-driven routing in Octilinear architecture (one type of the non-Manhattan architecture) based on multi-objective particle swarm optimization (MOPSO) and Elmore delay model. Edge transformation is employed in our algorithm to make the particles have the ability to achieve the optimal solution while Union-Find partition is used to prevent the generation of invalid solution. For the purpose of reducing the number of bends which is one of the key factors of chip manufacturability, we also present an edge-vertex encoding strategy combined with edge transformation. To our best knowledge, no approach has been proposed to optimize the number of bends in the process of constructing the non-Manhattan timing-driven Steiner tree. Moreover, the theorem of Markov chain is used to prove the global convergence of our proposed algorithm. Experimental results indicate that the proposed MOPSO is worthy of being studied in the field of multi-objective optimization problems, and our algorithm has a better tradeoff between the wire length and radius of the routing tree and has achieved a better delay value. Meanwhile, combining edge transformation with the encoding strategy, the proposed algorithm can significantly reduce nearly 20 % in the number of bends.
TL;DR: A variable neighborhood search is proposed to address the routing aspect, and a skyline heuristic is adapted to examine the loading constraints, which outperforms all existing methods and improves or matches the majority of best known solutions for both problem versions.
TL;DR: A formulation of the TWAVRP is proposed and two variants of a column generation algorithm are developed to solve the LP relaxation of this formulation and these algorithms provide us with very tight LP-bounds to instances of moderate size in reasonable computation time.
Abstract: In this paper we introduce the time window assignment vehicle routing problem TWAVRP. In this problem, time windows have to be assigned before demand is known. Next, a realization of demand is revealed, and a vehicle routing schedule is made that satisfies the assigned time windows. The objective is to minimize the expected traveling costs. We propose a branch-price-and-cut algorithm to solve the TWAVRP to optimality. We provide results of computational experiments performed using this algorithm. Finally, we offer insight on the value of an exact approach for the TWAVRP by comparing the optimal solution to the solution found by assigning time windows based on solving a vehicle routing problem with time windows with average demand.