Showing papers on "Routing (electronic design automation) published in 2023"

Ant based routing algorithm for balanced the load and optimized the AMNET lifetime

Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation H. Azath, A. K. Velmurugan, K. Padmanaban, A. M. Senthil Kumar, Murugan Subbiah; Ant based routing algorithm for balanced the load and optimized the AMNET lifetime. AIP Conference Proceedings 30 January 2023; 2523 (1): 020073. https://doi.org/10.1063/5.0110676 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Heuristic approaches to address vehicle routing problem in the Iot-based waste management system

Abstract: Nowadays, population growth and urban development lead to having an efficient waste management system (WMS) based on recent advances and trends. Alongside all functions and procedures in these systems, the waste collection plays a significant role. This study proposes a two-echelon WMS to minimize operational costs and environmental impact by utilizing the industry 4.0 concept. Both models utilize modern traceability Internet of Thing-based devices to compare real-time information of waste level in bins and separation centers with the threshold waste level (TWL) parameter. The first model optimizes the operational cost and CO2 emission of collecting waste from bins to the separation center by considering the time windows. A capacitated vehicle routing problem is designed as a later model-based to minimize the cost of waste transferring to recycling centers. In addition, to find the optimal solution, recent meta-heuristic algorithms are employed, and several novel heuristics based on the problem's specifications are developed. Furthermore, the developed heuristics methods are utilized to generate the initial feasible solutions in meta-heuristics and compared with random ones. The performance of the proposed algorithms is probed, and Best Worst Method (BWM) is applied to rank the algorithms based on relative percentage deviation, relative deviation index and hitting time.

A Proposal Algorithm to Solve Delay Constraint Least Cost Optimization Problem

Abstract: Traditionally, path selection within routing is formulated as a shortest path optimization problem. The objective function for optimization could be any one variety of parameters such as number of hops, delay, cost...etc. The problem of least cost delay constraint routing is studied in this paper since delay constraint is very common requirement of many multimedia applications and cost minimization captures the need todistribute the network. So an iterative algorithm is proposed in this paper to solve this problem. It is appeared from the results of applying this algorithm that it gave the optimal path (optimal solution) from among multiple feasible paths (feasible solutions).

Two-Stage Genetic Algorithm for Scheduling Stochastic Unrelated Parallel Machines in a Just-in-Time Manufacturing Context

Abstract: This paper considers a stochastic parallel machine scheduling problem in a just-in-time manufacturing context, in which its processing time can be described by a gamma or log-normal distribution. In order to obtain a high-performance schedule in a reasonable time, this work proposes a two-stage genetic algorithm with optimal computing budget allocation (OCBA) and improved Monte-Carlo Policy Evaluation (MCPE). In it, a genetic algorithm is selected as a main optimizer. An OCBA-based approach is developed to improve search efficiency, which is designed for two scenarios in a just-in-time manufacturing context. Different from most prior OCBA studies, this work considers that the stochastic processing time of jobs does not obey normal distribution. It extends the application area of OCBA by laying a theoretical foundation. A parameter control scheme based on MCPE is proposed, which aims to balance the global and local search in GA. To further enhance the efficiency and effectiveness of the proposed method, a two-stage framework is constructed. In the first stage, the performance is estimated roughly aiming at locating satisfactory solution regions. In the second stage, OCBA is incorporated to provide the reliable evaluation of excellent individuals. The theoretic interpretation of the proposed OCBA, and the convergence analysis results of the proposed method are presented. Various simulation results with benchmark and randomly generated cases validate that the proposed algorithm is more efficient and effective than several existing optimization algorithms. Note to Practitioners—A parallel machine scheduling problem under stochastic processing time is usually solved via meta-heuristic algorithms. However, their computational efficiency requires substantial improvement, especially for a stochastic optimization case that requires Monte Carlo sampling to estimate the actual objective function values in a precise manner. Most of them are parameter-sensitive, and choosing their proper parameters is highly challenging. For the first thorny issue, we develop an OCBA-based approach for determining the optimal numbers of simulations according to both prior knowledge and simulation results. In order to select proper control parameters of the proposed algorithm iteratively, we introduce a parameter control scheme based on MCPE. The combination of a meta-heuristic algorithm, OCBA and MCPE makes it possible to find high-quality solutions for the concerned scheduling problems in a short time. Theoretic analysis and numerical simulation results suggest that the proposed framework is valid and efficient. Hence, it can be readily applicable to practical systems, e.g., semiconductor manufacturing.

A PTAS for Capacitated Vehicle Routing on Trees

Abstract: We give a polynomial time approximation scheme (PTAS) for the unit demand capacitated vehicle routing problem (CVRP) on trees, for the entire range of the tour capacity. The result extends to the splittable CVRP.

Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

Abstract: Recently, owing to the high mobility and low cost of drones, drone-based delivery systems have shown considerable potential for ensuring flexible and reliable parcel delivery. Several crucial design issues must be considered to design such systems, including route planning, payload weight consideration, distance measurement, and customer location. In this paper, we present a survey of emerging drone routing algorithms for drone-based delivery systems, emphasizing three major drone routing aspects: trajectory planning, charging, and security. We focus on practical design considerations to ensure efficient, flexible, and reliable parcel delivery. We first discuss the potential issues arising when designing such systems. Next, we present a novel taxonomy based on the above-mentioned three aspects. We extensively review each algorithm for drone routing in terms of key features and operational characteristics. Furthermore, we compare the algorithms in terms of their main idea, advantages, limitations, and performance aspects. Finally, we present open research challenges to motivate further research in this field. In particular, we focus on the major aspects that researchers and engineers need to consider in order to design effective and reliable drone routing algorithms for drone-based delivery systems.

MCDM-Based Routing for IoT-Enabled Smart Water Distribution Network

Abstract: The work consists of two subapproaches. In the first approach, an analytical model is developed using trapezium fuzzy numbers in decision-making problems for an Internet of Things-based water distribution network. The second phase explains the integration of the previous phase with the MCDM-based location routing protocol (M-LRP). The water distribution network has three components static water source, the utility center (UC) which can be located in the proper position, and the consumer. The objective of this work is to select an optimal route between the UC and the consumer by considering multiple criteria. The simulation result shows that the proposed multicriterion-based decision-making (MCDM)-based routing protocol outperforms both existing MCDM-based and non-MCDM-based routing schemes. The proposed model outperforms the existing models like non-MCDM-based and MCDM-based routing protocols by 51% and 11%, respectively.

The vehicle routing problem with simultaneous pickup and delivery and occasional drivers

Abstract: This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total compensation paid to employed occasional drivers. We cast the problem into a mixed integer linear programming model and propose a simulated annealing (SA) heuristic with a mathematical programming-based construction heuristic to solve newly generated VRPSPDOD benchmark instances. The proposed SA incorporates a set of neighborhood operators specifically designed to address the existence of regular vehicles and occasional drivers. Extensive computational experiments show that the proposed SA obtains comparable results with the state-of-the-art algorithms for solving VRPSPD benchmark instances – i.e., the special case of VRPSPDOD – and outperforms the off-the-shelf exact solver – i.e., CPLEX – in terms of solution quality and computational time for solving VRPSPDOD benchmark instances. Lastly, sensitivity analyses are presented to understand the impact of various OD parameters on the objective value of VRPSPDOD and to derive insightful managerial insights.

Observability of lensing of gravitational waves from massive black hole binaries with LISA

Abstract: The gravitational waves emitted by massive black hole binaries in the Laser Interferometer Space Antenna (LISA) band can be lensed. Wave-optics effects in the lensed signal are crucial when the Schwarzschild radius of the lens is smaller than the wavelength of the radiation. These frequency-dependent effects can enable us to infer the lens parameters, possibly with a single detection alone. In this work, we assess the observability of wave-optics effects with LISA by performing an information-matrix analysis using analytical solutions for both point-mass and singular isothermal sphere lenses. We use gravitational-waveform models that include the merger, ringdown, higher harmonics, and aligned spins to study how waveform models and source parameters affect the measurement errors in the lens parameters. We find that previous work underestimated the observability of wave-optics effects and that LISA can detect lensed signals with higher impact parameters and lower lens masses.

Reducing Waiting Times at Charging Stations With Adaptive Electric Vehicle Route Planning

Abstract: Electric vehicles are becoming more popular all over the world. With increasing battery capacities and a growing fast-charging infrastructure, they are becoming suitable for long distance travel. However, queues at charging stations could lead to long waiting times, making efficient route planning even more important. In general, optimal multi-objective route planning is extremely computationally expensive. We propose an adaptive charging and routing strategy, which considers driving, waiting, and charging time. For this, we developed a multi-criterion shortest-path search algorithm using contraction hierarchies. To further reduce the computational effort, we precompute shortest-path trees between the known locations of the charging stations. We propose a central charging station database (CSDB) that helps estimating waiting times at charging stations ahead of time. This enables our adaptive charging and routing strategy to reduce these waiting times. In an extensive set of simulation experiments, we demonstrate the advantages of our concept, which reduces average waiting times at charging stations by up to 97 %. Even if only a subset of the cars uses the CSDB approach, we can substantially reduce waiting times and thereby the total travel time of electric vehicles.

Pole-Aware Analog Layout Synthesis Considering Monotonic Current Flows and Wire Crossings

Abstract: This article presents a new paradigm for analog placement, which further incorporates poles in addition to the considerations of symmetry island and monotonic current flow while minimizing wire crossings. The nodes along the signal paths in an analog circuit contribute to the poles, and the parasitics on these dominant poles can significantly limit the circuit performance. Although the monotonic placement methods introduced in the previous works can generate simpler routing topologies, the unawareness of poles, especially the dominant and the first non-dominant poles and wire crossings among critical nets, may increase wire load and performance degradation. This article proposes a pole-aware analog layout synthesis methodology to minimize the total wire load and wire crossings while satisfying different symmetry and topological routing constraints. Using a strong-connectivity approach to the group Steiner problem, the presented method for automatic selection of port locations can help reduce total wirelength, increase routing flexibility, and minimize total wire crossings. Experimental results show that the proposed approach results in better solution quality in circuit performance compared with other recent works.

Topological natures of the Gauss-Bonnet black hole in AdS space

Abstract: In the recent proposal [Phys. Rev. Lett. 129, 191101 (2022)], the black holes were viewed as topological thermodynamic defects by using the generalized off-shell free energy. In this paper, we follow such proposal to study the local and global topological natures of the Gauss-Bonnet black holes in AdS space. The local topological natures are reflected by the winding numbers, where the positive and negative winding numbers correspond to the stable and unstable black hole branches. The global topological natures are reflected by the topological numbers, which are defined as the sum of the winding numbers for all black hole branches and can be used to classify the black holes into different classes. When the charge is present, we find that the topological number is independent on the values of the parameters, and the charged Gauss-Bonnet AdS black holes can be divided into the same class of the RNAdS black holes with the same topological number 1. However, when the charge is absent, we find that the topological number has certain dimensional dependence. This is different from the previous studies, where the topological number is found to be a universal number independent of the black hole parameters. Furthermore, the asymptotic behaviors of curve {\tau}(r_h) in small and large radii limit can be a simple criterion to distinguish the different topological number. We find a new asymptotic behavior as {\tau}(r_h \to 0) = 0 and {\tau}(r_h \to \infty) = 0 in the black hole system, which shows topological equivalency with the asymptotic behaviors {\tau}(r_h \to 0)=\infty and {\tau}(r_h \to \infty)=\infty. We also give an intuitional proof of why there are only three topological classes in the black hole system under the condition (\partial_{r_h} S)P > 0.

Cascading Failure Analysis of Cyber–Physical Power Systems Considering Routing Strategy

Abstract: In this brief, we develop a novel model to study the cascading failure in cyber-physical power systems. We use a discrete packet traffic model that follows a specific routing strategy to describe the dynamic data transmission in the information network. Moreover, the dynamic load flow model that takes into account the power-frequency characteristics of loads and generators is applied to describe the dynamic flow process of the power network. Our proposed model allows to consider the impacts of data packet transmission failures and voltage-related failures in the cascading process. Furthermore, we analyze the effects of routing strategy and information network topology on the severity of cascading failure. Simulation results verify the applicability of the proposed model and reveal the way in which routing strategy affects the cascading failure of cyber-coupled systems. In addition, we show that when the main hub in the information network is used as a dispatching center, a spreadout degree distribution of the information network reduces the severity of cascading failure in the cyber-coupled system.

$Q$-Learning Aided Intelligent Routing With Maximum Utility in Cognitive UAV Swarm for Emergency Communications

Abstract: This article studies the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which employs the cognitive radio into a swarm of UAVs within a three-layer hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a $Q$ -learning framework to achieve the intelligent routing to maximize the utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric jointly captures the achievable rate and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for UAV to optimize the candidate UAV selection. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update $Q$ -values during the training process for achieving a fast convergence speed. Simulation results are provided to assess the potential of the $Q$ -learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm converges in a few number of iterations. Furthermore, the proposed algorithm can increase the accumulated rewards, and achieve significant performance gains, as compared to the benchmark schemes.

A queueing based analysis of a packing line

Abstract: Recticel Bedding Hulshout is a mattress manufacturer. The company intends to build a new packing line as a final step in their production lay-out redesign project. The objective of this study consists in answering the question whether, given the production volumes, the mix of mattresses (without packing, once wrapped or double wrapped) is operationally feasible within different scenarios. The new lay-out of the packing line can be modelled as a typical multi product job shop with general arrivals and general processing. The products (units of different modes of mattresses, a mode being a typical packing form of a mattress) enter the system individually and are processed as such at all manipulation steps. The mode depends on the upstream production line and is characterised by a deterministic routing. A general open queueing network and a parallel simulation model have been developed. We are able to show that the new lay-out is feasible, taking into account the different scenarios. Methodologically, we reveal that both an approximated queueing network and simulation are suitable approaches to give an answer to the managerial questions. However, the queueing model has the advantage of being a much more flexible, in terms of conducting 'what-if' scenarios and computational efficient approach.

Clarke and Wright Savings Algorithm as Solutions Vehicle Routing Problem with Simultaneous Pickup Delivery (VRPSPD)

Abstract: This study aims to establish the mathematical model Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) on 3 kg of LPG gas distribution and its solution using the method of Clarke and Wright Savings. Data used include a list of areas of consumers, service delivery company, the amount of consumer demand, vehicle type and vehicle capacity. The data is then processed to be modeled as hereinafter Vehicle Routing Problem with Simultaneous Pickup Delivery (VRPSPD) problems solved by the method of Clarke and Wright Savings[1]. Based on the calculation, the result for the total mileage of the vehicle is 160 km while the total mileage of the vehicle this company is 201km. Thus Clarke and Wright savings algorithm capable of providing mileage savings with a percentage of 20.03%.

An Enhanced Trust-Based Secure Route Protocol for Malicious Node Detection

Abstract: The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority. The intensity of the harm largely depends on the attacker’s intentions during hostile assaults. As a result, the loss of Information, power, or capacity may occur. The authors propose an Enhanced Trust-Based Secure Route Protocol (ETBSRP) using features extraction. First, the primary and secondary trust characteristics are retrieved and achieved routing using a calculation. The complete trust characteristic obtains by integrating all logical and physical trust from every node. To assure intermediate node trustworthiness, we designed an ETBSRP, and it calculates and certifies each mobile node's reputation and sends packets based on that trust. Connection, honesty, power, and capacity are the four trust characteristics used to calculate node reputation. We categorize Nodes as trustworthy or untrustworthy according to their reputation values. Fool nodes are detached from the routing pathway and cannot communicate. Then, we use the cryptographic functions to ensure more secure data transmission. Finally, we eliminate the untrustworthy nodes from the routing process, and the datagram from the origin are securely sent to the target, increasing throughput by 93.4% and minimizing delay.

Based on Improved NSGA-II Algorithm for Solving Time-Dependent Green Vehicle Routing Problem of Urban Waste Removal with the Consideration of Traffic Congestion: A Case Study in China

Abstract: The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in time, high collection and transportation costs and high carbon emissions, this paper studies the vehicle routing problem of municipal solid waste removal under the influence of time-dependent travel time, traffic congestion and carbon emissions. In this paper, a dual objective model with the lowest total economic cost and the highest garbage removal efficiency is established, and a DCD-DE-NSGAII algorithm based on Dynamic Crowding Distance and Differential Evolution is designed to improve the search ability, improve the convergence speed and increase the diversity of the optimal solution set. The results show that: according to the actual situation of garbage collection and transportation, the method can scientifically plan the garbage collection and transportation route, give a reasonable garbage collection scheme and departure time, and effectively avoid traffic congestion time; Through algorithm comparison, the algorithm and model proposed in this paper can reduce collection and transportation costs, improve transportation efficiency and reduce environmental pollution.

Development of a robust multi-objective model for green capacitated location-routing under crisis conditions

Abstract: Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental impacts of transportation over the past years, the use of green logistics to mitigate these impacts has become increasingly important. To compensate for a gap in the literature, this paper presents a robust bi-objective mixed-integer linear programming (MILP) model for the green capacitated location-routing problem (G-CLRP) with demand uncertainty and the possibility of failure in depots and routes. The final result of this Robust Multi-Objective Model is to set up the depots and select the routes that offer the highest reliability (Maximizing network service) while imposing the lowest cost and environmental pollution. A Nondominated Sorting Genetic Algorithm (NSGA-II) is used to solve the large-sized instances of the modeled problem. The paper also provides a numerical analysis and a sensitivity analysis of the solutions of the model.

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

Abstract: Routing strategies and security issues are the greatest challenges in Wireless Sensor Network (WSN). Cluster-based routing Low Energy adaptive Clustering Hierarchy (LEACH) decreases power consumption and increases network lifetime considerably. Securing WSN is a challenging issue faced by researchers. Trust systems are very helpful in detecting interfering nodes in WSN. Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem. The metaheuristic Elephant Herding Optimizations (EHO) algorithm is formulated based on elephant herding in their clans. EHO considers two herding behaviors to solve and enhance optimization problem. Based on Elephant Herd Optimization, a trust-based security method is built in this work. The proposed routing selects routes to destination based on the trust values, thus, finding optimal secure routes for transmitting data. Experimental results have demonstrated the effectiveness of the proposed EHO based routing. The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%, by 1.45%, and by 31.94% than LEACH, Elephant Herd Optimization, and Trust LEACH, respectively at Number of Nodes 3000. As the proposed routing is efficient in selecting secure routes, the average packet loss rate is significantly reduced, improving the network’s performance. It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.

Algorithms and Complexity Results for the Single-Cut Routing Problem in a Rail Yard

Abstract: Rail yards are facilities that play a critical role in the freight rail transportation system. A number of essential rail yard functions require moving connected “cuts” of rail cars through the rail yard from one position to another. In a congested rail yard, it is therefore of interest to identify a shortest route for such a move. With this motivation, we contribute theory and algorithms for the Single-Cut Routing Problem (SCRP) in a rail yard. Two key features distinguish SCRP from a traditional shortest path problem: (i) the entity occupies space on the network; and (ii) track geometry further restricts route selection. To establish the difficulty of solving SCRP in general, we prove NP-completeness of a related problem that seeks to determine whether there is space in the rail yard network to position the entity in a given direction relative to a given anchor node. However, we then demonstrate this problem becomes polynomially solvable—and therefore, SCRP becomes polynomially solvable, too—for “Bounded Cycle Length” (BCL) yard networks. We formalize the resulting two-stage algorithm for BCL yard networks and validate our algorithm on a rail yard data set provided by the class I railroad CSX Transportation.