Routing Operations Research Articles

Research on the security of communication addressing and reporting system of civil aircraft

ACARS is a digital data link system that transmits short messages by radio or satellite between aircraft and ground stations. ACARS control centre is connected with each ACARS ground station and each airline information centre through the ground communication network. Exchange data and information with airlines in both directions through code addressing. However, because ACARS messages use plaintext transmission, anyone can monitor and decode ACARS messages with a low-cost receiver and obtain details such as aircraft type, location, planned route, passenger status and aircraft operation state, etc., thus posing a major threat to flight safety. The following security problems exist in the current ACARS system.

Analysis of electric vehicles in home health care routing problem

The road transportation is one of the largest contributors to greenhouse gas emissions globally, and rapid urbanisation increases the environmental and economic challenges. Electric vehicles support green supply chain and clean routing operations when compared with the traditional fossil fuel-powered vehicles. This paper analyses a variant of the home health care routing problem in which a group of health care workers performs a requested number of jobs by using electric vehicles. The problem considers multi-depot, heterogeneous fleet, time windows, preferences, competencies, connected activities, the range of electric vehicles, charging status, and charge strategies. We develop a hybrid metaheuristic which successfully combines genetic algorithm and a variable neighbourhood descent, and offer several algorithmic procedures tailored to handle the rich constraints of the problem. Extensive computational experiments on small, medium and large-scale instances have shown that the hybrid metaheuristic is effective on the problem.

Ice gouging on the arctic shelf of Russia

The results of recent geological and geophysical expeditions indicate the activation of hazardous natural phenomena associated with ice gouging and represent geohazard for almost all activities, including operation of the Northern Sea Route. Within the Barents Sea and the western part of the Kara Sea, the modern ice gouging is mainly associated with icebergs which are formed as a result of the destruction of the glaciers of Novaya Zemlya, the Spitsbergen archipelago and Franz Josef Land, while on the eastern shelf it is caused by the destruction of seasonal or perennial ice fields. Fixed furrows can be divided into modern coastal gouges or deep water ploughmarks. All deep water gouges within the periglacial and glacial shelf are of paleogeographical origin, but with different mechanisms of action on the seabed. These furrows were formed by floating ice on the periglacial shelf. On the glacial shelf deep water ploughmarks were formed by large icebergs, which could carry out the gouging even on the continental slope and deep-sea ridges of the Arctic Ocean.

Ice Gouging on Russia’s Arctic Shelf

Abstract—The results of recent geological and geophysical expeditions indicate the activation of hazardous natural phenomena associated with ice gouging and representing a geohazard for almost all activities, including operation of the Northern Sea Route. The underwater parts of ice formations plow up the seabed. Within the Barents Sea and the western part of the Kara Sea, modern ice gouging is mainly associated with icebergs formed as a result of the breakup of glaciers of Novaya Zemlya and the Svalbard and Franz Josef Land archipelagos, while on the eastern shelf it is caused by the destruction of seasonal or perennial ice fields. Fixed furrows can be divided into modern coastal gouges or deep water plowmarks. All deep water gouges within the periglacial and glacial shelf are of paleogeographical origin, but with different mechanisms of action on the seabed. These furrows were formed by floating ice on the periglacial shelf; on the glacial shelf, deep-water plowmarks were formed by large icebergs, which were able to cause gouging even on the continental slope and deep-sea ridges of the Arctic Ocean.

A hybrid VNS/Tabu search algorithm for solving the vehicle routing problem with drones and en route operations

With the goal of integrating drones in last-mile delivery, the Vehicle Routing Problem with Drones (VRPD) uses a fleet of vehicles, each of them equipped with a set of drones, for serving a set of customers with minimal makespan. In this paper, we propose an extension of the VRPD that we call the Vehicle Routing Problem with Drones and En Route Operations (VRPDERO). Here, in contrast to the VRPD, drones may not only be launched and retrieved at vertices but also on some discrete points that are located on each arc. We formulate the problem as a Mixed Integer Linear Program (MILP) and introduce some valid inequalities that enhance the performance of the MILP solvers. Furthermore, due to limited performance of the solvers in addressing large-scale instances, we propose an algorithm based on the concepts of Variable Neighborhood Search (VNS) and Tabu Search (TS). In order to evaluate the performance of the introduced algorithm as well as the solver in solving the VRPDERO instances, we carried out extensive computational experiments. According to the numerical results, the proposed valid inequalities and the heuristic have a significant contribution in solving the VRPDERO effectively. In addition, the consideration of en route operations can increase the utilization of drones and lead to an improved makespan.

SDN-Based Routing for Backhauling in Ultra-Dense Networks

Ultra-Dense Network (UDN) deployment is considered a key element to achieve the requested capacity in future fifth-generation (5G) mobile networks. Backhaul networks in UDNs are formed by heterogeneous links with multi-hop connections and must handle massive traffic. Backhauling in future 5G networks may represent the capacity bottleneck. Therefore, there is the need for efficient and flexible routing schemes able to handle the dynamism of the traffic load in capacity-limited networks. Toward this goal, the emerging Software-Defined Network (SDN) paradigm provides an efficient solution, transferring the routing operation from the data plane switches to a central controller, thus achieving more flexibility, efficiency, and faster convergence time in comparison to conventional networks. This paper proposes and investigates an SDN-approach for an efficient routing in a capacity-limited backhaul network that carries data and control traffic of a heterogeneous UDN. The routing algorithm is centralized in the SDN controller and two different types of traffic flow are considered: data and control plane coordination traffic. The goal is to reduce or even to avoid the amount of traffic that the backhaul network is not able to support, distributing in a fair way the eventual lack of bandwidth among different access points. Simulation results show that with the considered approach the performance significantly improves, especially when there is an excess of traffic load in the network. Moreover, thanks to the SDN-based design, the network can reconfigure the traffic routing depending on the changing conditions.

A Complete Routing Simulator for Digital Microfluidic Biochip

Microfluidic technology, as well as digital microfluidic biochips (DMFBs), have had a very significant contribution in the field of medical bio-chemistry such as point-of-care diagnostics, environmental monitoring, DNA sequencing, etc. DMFBs are low cost, highly flexible and reconfigurable device that makes it so advantageous in clinical applications. The routing of a nano-liter volume of droplets is very important and is a critical operation in DMFB. Complex heuristic algorithms have been developed for droplet routing. Proper simulation is very essential to measure the effectiveness of these algorithms. In this work, the authors develop a complete routing simulator for both homogeneous and heterogeneous type droplets. The proposed routing simulator graphically represents the routing operation of droplets in DMFB and display the measurement of all parameters of routing algorithms such as latest arrival time, average arrival time, number of cells used, and the total number of contaminations. The proposed simulator was executed on suit I and III benchmark testbenches.

Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Routing protocols for vehicular ad hoc networks resort to clustering in order to optimize network performance. Concerning the optimized link state routing protocol and the plethora of its derivatives, the multipoint relaying (MPR) technique has proven its efficiency as an accurate clustering scheme over the last two decades. However, it has been emphasized recently that the MPR technique, which was originally designed for open areas, does not benefit from the particular configuration of road sections, which are intrinsically spatially constrained. A clustering scheme exploiting this particularity, namely chain-branch-leaf (CBL), has been introduced in order to enhance the flooding of broadcast traffic, including that related to routing operations. In this paper, both MPR and CBL are evaluated through MATLAB simulation over several scenarios based on realistic road configurations and traffic generated with SUMO simulator. The results show that CBL actually reduces the number of nodes acting as relays (cluster-heads) in the network, thus decreasing the routing traffic related to creation and retransmission of topology control messages. Also, they show that, with CBL, the nodes chosen as relays remain longer in this role, thus favoring the overall network stability, and that most of the nodes remain attached longer to the same relay than with the MPR technique.

Experimental and numerical investigation of the performance of self-sensing concrete sleepers

Prestressed concrete sleepers with built-in fibre optic–based sensing systems have recently been developed to capture performance data within railway networks and to provide critical decision-support information to route managers and operators. To better understand how self-sensing sleepers can be fully utilized within the rail network, a study of their comprehensive performance under controlled conditions must be undertaken. This article presents the results of the full-scale laboratory testing of a self-sensing sleeper supported on ballast. A primary focus of this study was to investigate whether a self-sensing sleeper could also be used to estimate rail seat load, detect cracking, and identify differential ballast settlement. The ultimate capacity and resilience of the embedded fiber Bragg grating sensing system was tested by applying load up until concrete cracking followed by several cyclic load cycles. Through inference of the load versus strain response, the ability of the self-sensing sleeper to detect damage (concrete cracking and loss of ballast support) was evaluated. The experimental results revealed the effectiveness and robustness of the embedded sensing system to continue to provide reliable dynamic strain measurements well beyond the ultimate loading capacity of the prestressed sleeper. Cracking of the top surface of the sleeper was effectively detected by the fiber Bragg grating strain sensors at the mid-span section. After cracking, subsequent load cycles were carried out. During this period, the bottom fiber Bragg grating measurements captured the effects of differential ballast settlement under the rail seats. A three-dimensional nonlinear finite element model was developed to simulate the experimental test setup and to investigate the relation between fiber Bragg grating sensor measurements and rail track response. The combined experimental and numerical results suggest that a self-sensing sleeper may be deployed on an operational railway to provide reliable and long-term measurements of rail axle load and ballast pressure.

CoSiM-RPO: Cooperative Routing with Sink Mobility for Reliable and Persistent Operation in Underwater Acoustic Wireless Sensor Networks.

An efficient algorithm for the persistence operation of data routing is crucial due to the uniqueness and challenges of the aqueous medium of the underwater acoustic wireless sensor networks (UA-WSNs). The existing multi-hop algorithms have a high energy cost, data loss, and less stability due to many forwarders for a single-packet delivery. In order to tackle these constraints and limitations, two algorithms using sink mobility and cooperative technique for UA-WSNs are devised. The first one is sink mobility for reliable and persistence operation (SiM-RPO) in UA-WSNs, and the second is the enhanced version of the SiM-RPO named CoSiM-RPO, which utilizes the cooperative technique for better exchanging of the information and minimizes data loss probability. To cover all of the network through mobile sinks (MSs), the division of the network into small portions is accomplished. The path pattern is determined for MSs in a manner to receive data even from a single node in the network. The MSs pick the data directly from the nodes and check them for the errors. When erroneous data are received at the MS, then the relay cooperates to receive correct data. The proposed algorithm boosts the network lifespan, throughput, delay, and stability more than the existing counterpart schemes.

A column generation algorithm for vehicle scheduling and routing problems

In this paper, we consider a variant of a truckload open vehicle routing problem with time windows, which is suitable for modeling vehicle routing operations during a humanitarian crisis. We present two integer linear programming models to formulate the problem. The first one is an arc-based mixed integer linear programming model that can be solved using a general purpose solver. For the second formulation, we first devise a task adjacency graph, that we use to develop a path-based integer linear program. We then propose a column generation framework to solve large-scale instances. Finally, we perform numerical experiments to compare the performance of the two models. Our computational results show that the path-based formulation solved using our proposed column generation framework outperforms the arc-based mixed integer linear program in solution time, without sacrificing solution quality.

A Hybrid Adaptive Large Neighborhood Heuristic for a Real-Life Dial-a-Ride Problem

The transportation of elderly and impaired people is commonly solved as a Dial-A-Ride Problem (DARP). The DARP aims to design pick-up and delivery vehicle routing schedules. Its main objective is to accommodate as many users as possible with a minimum operation cost. It adds realistic precedence and transit time constraints on the pairing of vehicles and customers. This paper tackles the DARP with time windows (DARPTW) from a new and innovative angle as it combines hybridization techniques with an adaptive large neighborhood search heuristic algorithm. The main objective is to improve the overall real-life performance of vehicle routing operations. Real-life data are refined and fed to a hybrid adaptive large neighborhood search (Hybrid-ALNS) algorithm which provides a near-optimal routing solution. The computational results on real-life instances, in the Canadian city of Vancouver and its region, and DARPTW benchmark instances show the potential improvements achieved by the proposed heuristic and its adaptability.

Formal constraint-based compilation for noisy intermediate-scale quantum systems

Noisy, intermediate-scale quantum (NISQ) systems are expected to have a few hundred qubits, minimal or no error correction, limited connectivity and limits on the number of gates that can be performed within the short coherence window of the machine. The past decade’s research on quantum programming languages and compilers is directed towards large systems with thousands of qubits. For near term quantum systems, it is crucial to design tool flows which make efficient use of the hardware resources without sacrificing the ease and portability of a high-level programming environment. In this paper, we present a compiler for the Scaffold quantum programming language in which aggressive optimization specifically targets NISQ machines with hundreds of qubits. Our compiler extracts gates from a Scaffold program, and formulates a constrained optimization problem which considers both program characteristics and machine constraints. Using the Z3 SMT solver, the compiler maps program qubits to hardware qubits, schedules gates, and inserts CNOT routing operations while optimizing the overall execution time. The output of the optimization is used to produce target code in the OpenQASM language, which can be executed on existing quantum hardware such as the 16-qubit IBM machine. Using real and synthetic benchmarks, we show that it is feasible to synthesize near-optimal compiled code for current and small NISQ systems. For large programs and machine sizes, the SMT optimization approach can be used to synthesize compiled code that is guaranteed to finish within the coherence window of the machine.

A study on multi-ASC scheduling method of automated container terminals based on graph theory

Compared with traditional terminals, two automated stacking cranes (ASCs) are configured for each container block of automated container terminal (ACT), which interact with automated guided vehicles (AGVs) and container trucks at the two ends of a container block individually. To increase the capacity, container yards with multiple rows of blocks perpendicular to the terminal’s shoreline are considered. To utilize the yard spaces, twin ASCs are set to share transport tracks installed at the two sides of a block, while interferences between the ASCs causes the routing and sequencing operations. In order to control the scheduling of twin ASCs, the interference model is established by analyzing the time overlap between tasks. Considering the influence of AGV transportation time, the model are then established to sequence the container handling tasks under the minimization of waiting time and makespan. A particle swarm optimization algorithm (PSO) based on graph theory model is design to solve the problem. Numerical experiments show that the algorithm is more competitive than traditional algorithm. Based on the model and experimental result, the practical significance of applying the algorithm to the actual situation is discussed.

Effects of Weather Routing on Maximum Vertical Bending Moment in a Ship Taking Account of Wave-Induced Vibrations

In this paper, the effect of weather routing and ship operations on the extreme vertical bending moment (VBM) in a 6000TEU class large container ship which is operated in North Atlantic Ocean is addressed. A direct time-domain nonlinear response simulation method taking account of the wave-induced vibrations is combined with a voyage simulation based on 10 years of meteorological data in the area. The probability distribution of the ship's operational parameters conditional upon the meteorological conditions is considered. It is clarified that the most severe wave condition with the significant wave height over 16 m in the area may not be encountered by the ship due to the weather routing and the extreme value is determined mostly by the wave condition much milder than the most severe in the area. It is also found out that the ship speed assumed in the most contributing sea state strongly affects the extreme value of the total VBM. It is explained by the fact that the wave-induced vibrations in the ship tend to be excited at faster speed.

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

In the era of big data, data‐driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time‐frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time‐frequency graphs are obtained by wavelet time‐frequency analysis. Then the time‐frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time‐frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

On Learning and Learned Data Representation by Capsule Networks

In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks. Our investigation yielded the results some of which have been mentioned in the original paper of CapsNet, they are: 1) the routing operation determines the certainty with which a layer of capsules pass information to the layer above and the appropriate level of certainty is related to the model fitness; 2) in a designed experiment using data with a known 2D structure, capsule representations enable a more meaningful 2D manifold embedding than neurons do in a standard convolutional neural network (CNN), and; 3) compared with neurons of the standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.

Improving Network Formation in 6TiSCH Networks

The industrial Internet of Things (IIoT) is expected to revolutionize the current industry. The capillary introduction of sensors and actuators for real-time monitoring and remote control and their seamless integration into existing information systems will represent a technological breakthrough that will help to reshape the industrial processes. To this aim, the definition of wireless communication standards will play a crucial role in reducing deployment costs and minimizing the time for installation. In this context, the new IPv6 over the TSCH mode of IEEE 802.15.4e communication stack, 6TiSCH, represents the current leading standardization effort that aims at achieving both reliable and timed wireless communication and integration within IPv6 communication networks for industrial systems. In this paper, the network formation dynamics of 6TiSCH networks are assessed, considering the current guidelines for the so-called minimal configuration, a static initial configuration pre-configured to guarantee control communication during network bootstrap. It is shown that the minimal configuration might lead to long network formation and suboptimal performance of the routing algorithm which may result into a disconnected network. In order to overcome this issue, a dynamic resource management algorithm to be executed during network bootstrap is proposed. Simulation and experimental results show that the proposed solution allows to minimize the network formation time and also helps in optimizing routing operations leading to the discovery of better routes.

Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network

The recent advancement in information technology and evolving of the (IoT) shifted the traditional medical approach to patient-oriented approach (e.g., Telemedicine/Telemonitoring). IoT permits several services including sensing, processing and communicating information with physical and bio-medical constraints. Wireless Body Area Network (WBAN) handles the issues pertaining to the medical purposes in the form of sensor nodes and connected network. The WBAN takes human physiological data as an input to subsequently monitor the patient conditions that are transferred to other IoT components for analysis. Such monitoring and analysis demand a cohesive routing approach to ensure the safe and in-time transfer of data. The temperature rise of bio-medical sensor nodes makes the entire routing operation very crucial because the temperature of implanted nodes rises and ultimately damages body tissues. This needs dispersion in data transmission among different nodes by opting various available routes while avoiding temperature rise. In this paper, we present Adaptive Thermal-Aware Routing algorithm for WBAN. The ATAR is designed to overcome the temperature rise issue of implanted bio-medical sensors nodes. The new protocol is based on Multi-Ring Routing approach to find an alternative route in the case of increasing temperature. The simulation results indicate that proposed protocol is more efficient in terms of temperature rise and throughput than existing approaches.

Comparative Analysis of the Long-term Management Plan of Natural Parks - A Focus on Jeollabuk-do Provincial Parks -

The purpose of the present study was to compare the long-term management objectives, policy objectives, implementation plans, and detailed plans of four provincial parks established in 2013 using various analysis frameworks. Through this, the study aimed to identify the characteristics of the management plan and extract the implementation plan important to each park by analyzing the priorities in the detailed plan. The long-term management objectives of most parks were to maintain recreational functions by utilizing all the resources of the park, rather than merely preserving the ecosystem. From an ecological perspective, information on major plants and legal protection species are included for most parks, and geological and topographic features considered a major resource. For sustainable forests, it is important to induce the dispersion of concentrated use space. Efficient management systems are distinctively differentiated among the parks. For regional revitalization, the management of programs to link provincial parks and their surrounding areas has become most important. From an ecological perspective, priorities differed for the parks according to their ecological characteristics. For sustainable forests, Moaksan Provincial Park prioritized the visitor reservation system related to the overall park, while the other three parks prioritized alleviating the concentrated use of specific areas. From an efficient management perspective, Daedunsan Provincial Park prioritized aspects related to the cableway. Maisan Provincial Park prioritized inducing the linkage between the use of natural and cultural resources, and Seonunsan Provincial Park prioritized establishing an ecological and cultural commentary operating system. For the base-type leisure activity space, Moaksan Provincial Park prioritized the systematic management and operation of the pilgrimage route, Maisan Provincial Park the connection between the Maisan geography and residents’ income, and Seonunsan Provincial Park providing a space for ecological education.