Chain Routing Research Articles

Routing and facility location optimization in a dairy products supply chain

Routing and facility location optimization is an important aspect of supply chain management. This is because the problem of determining which facilities within a supply chain echelon should cost-effectively supply products to facilities in the next echelon is usually encountered by supply chain managers and analysts. In this paper, a linear programming model for supply chain routing and facility location optimization has been proposed. The model can be solved using the PuLP optimization library implemented in Python programming language. The model has been applied to a small dairy products supply chain to determine the most cost-effective routes for products in the supply chain. The optimization results proffered the optimal contribution of each factory warehouse to each distribution warehouse in order to satisfy distribution warehouse demand while minimizing costs. The results of the study indicated that the model is efficient in solving the routing and facility location optimization problem usually encountered in supply chain management. Therefore, this study will aid supply chain analysts and managers in determining the most cost-effective route for distributing products between echelons or stages of supply chains under study.

Efficient routing method for IoT networks using bee colony and hierarchical chain clustering algorithm

Hierarchical chain communication is an efficient technique for saving sensor energy while maintaining load balance and scalability. However, the long delay caused by the formation of long chains is a significant research gap in this paradigm. In this article, a two-level hierarchical chain routing scheme based on the bee colony algorithm called PEG_ABC is proposed to reduce transmission delay and take advantage of the benefits of chain communication. Initially, the remaining sensor energy, Euclidean distance between sensors, and Euclidean distance between sensors and base station are simultaneously adjusted to the fitness function of the bee colony algorithm to select the most suitable chain leader at the first level. Also, in our proposed method at the second level, the collected data in the leader nodes of each level is transferred to the BS through end-to-end path formation between the leaders based on the bee colony algorithm. At this level, the data transmission delay and remaining energy in the leader nodes adjust the fitness function of the bee colony algorithm. Simulation results show the efficiency of the proposed PEG_ABC scheme compared to literature research in terms of energy consumption, network lifetime, number of packets sent to BS, and transmission delay. Also, this article presents a relationship based on extensive simulation results to determine the optimal number of clusters in hierarchical chain communication.

Cold Chain Routing for Product Freshness and Low Carbon Emissions: A Target-Oriented Robust Optimization Approach

Cold Chain Routing for Product Freshness and Low Carbon Emissions: A Target-Oriented Robust Optimization Approach

Sustainable Supply Chain Management of COVID-19 Vaccines for Vaccination Delivery based on Routing Algorithms

Covid-19 pandemic has changed daily life in the city of Athens where vaccines are exploited with supply chain technology potentiality. Vaccines are tracked at the city’s airport till their delivery to vaccination centers. Due to the sensitivity of vaccines to the warm climate inherent in the city, delivery is assigned to a fleet of trucks. Specifically, two use cases, i.e., UC-I and UC-II, are proposed, which are based on global and local routing algorithms to exploit trucks’ load COVID-19 vaccine delivery from the airport and transport it to vaccination centers. In this paper, we focus on the supply chain routing algorithm technology of collecting COVID-19 vaccines from the airport and delivering them to vaccination centers in the smart city of Athens, Greece. Concretely, the purpose and the objectives of the research effort are in the areas of: (1) describing in deep detail the proposed supply chain system, (2) exploiting the adopted architecture based on certain separate use cases for system experimentation, (3) adopting specific vaccination routing algorithms to support vaccination distribution, and (4) evaluating experimentally the proposed supply chain system architecture with regards to the adopted use cases’ routing algorithms.

Reliability Evaluation for Chain Routing Protocols in Wireless Sensor Networks Using Reliability Block Diagram

There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN’s reliability is essential during the design process and when evaluating WSNs’ performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance D, and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, Q. Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario.

Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning

ABSTRACT Biomass sources have the potential to mitigate carbon emissions as a renewable source while reducing waste and residues. Seasonality and disruption risks are some of the disadvantages of biomass resources requiring that biomass supply chains be managed such that to withstand disruptions. There has been very limited research on integrating predictions for smart management on supply or demand sides of biomass supply chains. In this study, a number of predictive models are investigated for building energy demand and biomass stock availability subject to forecasts of weather conditions. On that basis, an allocation algorithm is proposed for optimal collection and logistics of biomass from land to depots. Accordingly, Google Maps API will be used to identify the best distribution routes for delivering biomass from depots to end-users. A case study with real (supply and demand) data is considered. The proposed integrated data-driven approach aims at improving the accuracy of biomass supply and demand predictions and coordinating these predictions to enhance the resiliency of bioenergy supply chain routing decisions.

Research on Cold Chain Routing Optimization of Multi-distribution Center Considering Traffic Performance Index

In response to the practical situation where traffic congestion factors reduce the distribution efficiency and increase the cost of cold chain logistics in the actual transportation process, a mathematical model for optimizing the cold chain path of multiple distribution centers is established, with the goal of minimizing the comprehensive cost and incorporating a traffic congestion index. By analyzing routine factors such as customer requested time windows, loading and unloading time, and road congestion, an improved particle swarm algorithm that introduces exchange offspring is proposed. The proposed method is tested through experimental simulations and the results are compared with traditional methods. The results show that considering the traffic congestion index saves 7.12% of the comprehensive cost compared to not considering the traffic congestion index, and completes the delivery service with a shorter path length.

Cross-Domain Service Function Chain Routing: Multiagent Reinforcement Learning Approaches

The service function chain (SFC) routing has been increasingly associated with multi-domain networks. However, several multi-domain SFC routing issues, linked to cross-domain interactions and emphasized by the heterogeneity of substrate topologies and administrative authorities, present a challenge. We propose the Cooperative Multi-agent Reinforcement Learning (CMRL) based algorithm for multi-domain SFC routing problem. Simulations on the real-world network topology demonstrate that our proposal can provide more than two and four times reduction in terms of the run time and network resource cost, respectively.

A Two-level clustering chain energy heterogeneous routing protocol for WSN

In view of the disadvantages of high energy consumption, short life cycle, low throughput and no application correlation in heterogeneous WSN, a two-level clustering chain routing protocol is proposed under the heterogeneous WSN of intelligent water-saving irrigation system of ecological agriculture in Hexi Gobi. The irrigation subnets are divides into clusters according to the actual layout in the wireless network and the nodes in the cluster form the chain on the basis of the minimum distance. The two-level cluster headers in reserve are selected by rate of the remaining energy of terminal nodes in cluster vs. average energy in local region. Eventually the level clusters heads and the two-level cluster headers are chosen according to the largest ratio of the remaining energy to the distance between the terminal nodes and the base station. Results in simulation show that the proposed algorithm is superior to PEGASIS and SEP in energy consumption, life cycle and throughput.

Markov model based dynamic chain routing protocol for grid WSN

Markov model based dynamic chain routing protocol for grid WSN

An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

The Internet of Things grew rapidly, and many services, applications, sensor-embedded electronic devices, and related protocols were created and are still being developed. The Internet of Things (IoT) allows physically existing things to see, hear, think, and perform a significant task by allowing them to interact with one another and exchange valuable knowledge when making decisions and caring out their vital tasks. The fifth-generation (5G) communications require that the Internet of Things (IoT) is aided greatly by wireless sensor networks, which serve as a permanent layer for it. A wireless sensor network comprises a collection of sensor nodes to monitor and transmit data to the destination known as the sink. The sink (or base station) is the endpoint of data transmission in every round. The major concerns of IoT-based WSNs are improving the network lifetime and energy efficiency. In the proposed system, Optimal Cluster-Based Routing (Optimal-CBR), the energy efficiency, and network lifetime are improved using a hierarchical routing approach for applications on the IoT in the 5G environment and beyond. The Optimal-CBR protocol uses the k-means algorithm for clustering the nodes and the multihop approach for chain routing. The clustering phase is invoked until two-thirds of the nodes are dead and then the chaining phase is invoked for the rest of the data transmission. The nodes are clustered using the basic k-means algorithm during the cluster phase and the highest energy of the node nearest to the centroid is selected as the cluster head (CH). The CH collects the packets from its members and forwards them to the base station (BS). During the chaining phase, since two-thirds of the nodes are dead and the residual energy is insufficient for clustering, the remaining nodes perform multihop routing to create chaining until the data are transmitted to the BS. This enriches the energy efficiency and the network lifespan, as found in both the theoretical and simulation analyses.

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

Internet of vehicles (IoV) is an emerging area that gives support for vehicles via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation of more than one radio access network. The mobility of vehicles demands to make handover in such heterogeneous network. The vehicles at short range uses dedicated short range communication (DSRC), while it has to use better technology for long range and any type of traffic. Usually, the previous work will directly select the network for handover or it connects with available radio access. Due to this, the occurrence of handover takes place frequently. In this paper, the integration of DSRC, LTE and mmWave 5G on IoV is incorporated with novel handover decision making, network selection and routing. The handover decision is to ensure whether there is a need for vertical handover by using Dynamic Q-learning algorithm that uses entropy function for threshold prediction as per the current characteristics of the environment. Then the network selection is based on fuzzy-convolution neural network (F-CNN) that creates fuzzy rules from signal strength, distance, vehicle density, data type and line of sight. V2V chain routing is proposed to select V2V pairs using jellyfish optimization algorithm (JOA) that takes in account of channel, vehicle and transmission metrics. This system is developed in OMNeT++ simulator and the performances are evaluated in terms of success probability, handover failure, unnecessary handover, mean throughput, delay and packet loss.

RETRACTED ARTICLE: Denaturalized cluster organization based improving energy constraints using relay link chain routing protocol in wireless sensor network

Energy constraints is an important for routing standards in cooperative transmission to make a better communication on wireless medium. The WSN broadcast communication leads Loss of energy, broken link, non-relay network issues because of cooperative network does not make efficient routing standards. Most of the energy efficient schemas possess the network wide broadcast protocol (NWBP) to oppose the challenges in WSN. To improve the energy efficiency in WSN, we propose a denaturalized cluster organization to form the centralized cluster head (DNC-CCH) communication on route head to make the transmission without sleeping nodes on networks have the on state available node from source to destination. In Addition by improving energy constraints using relay link chain routing protocol (RLCRP) in wireless sensor network make an relay on routing to choose the non-sleeping cluster nodes relies the neighbor discourse supposed to the delay routing propagation. This select the closest relay links to make the continues link on really transmission with aggregate data from source to destination with improved energy. Also this consider the neighboring senders to improve the lifespan of the network nodes to make energy efficient routing standards and life time of WSN cooperative communication.

Game Theoretic Approach for a Service Function Chain Routing in NFV With Coupled Constraints

In this brief, the service function chain (SFC) routing in network function virtualization (NFV) with server capacity limitation and user minimum target rate constraint is investigated simultaneously. After relaxing the constraints by the addition of extra cost terms, an extended NFV game incorporating the coupled constraints is formulated. By using semi-tensor product of matrices, we prove that the formulated game is a potential game which possesses at least one Nash equilibrium. We then present the equilibrium point algorithm for solving the SFC routes problem. The numerical simulation of two cases with constraints and without constraints illustrates the influence of coupled limitations on the SFC routing.

Forest Fire Detection Using Wireless Multimedia Sensor Networks and Image Compression

Recently, the issue of multimedia sensors received considerable critical attention, that led to the apparition of Wireless Multimedia Sensor Networks (WMSNs) WMSN that different from wireless sensor networks (WSN) by using multimedia sensors that can process video, audio, image data besides scalar data and send it to station base (SB). Multimedia data have a big volume bigger than scalar data and need more resources and consumed more energy. The ideal solution to solve the problems of WMSN (big volume, energy consumption) is data compression. Forest plays a critical role in our daily life we can summarize the importance of forests in human life. Among the most dangerous events the forest fires that happen because of natural or Man-made. Many methods used to detect forest fires the newest are: wireless multimedia sensor networks. Our system of detecting forest fire has been developed using a wireless multimedia senor network with two types of sensors (scalar, images). In the first phase when the scalar sensors detected a high temperature its announced alarm to activate the image sensors. In the second phase for detecting fire the image sensors, we used image processing tools. When the zone of fire in the image captured was detected the phase of compression started using the down sampling method. the final phase is transmission data to the station base using the grid chain transmission protocol technique, which allows a critical optimization of energy consumption. So, maximizing network life. The competence of the proposed system is achieved by minimizing size of image transmitted with grid chain routing protocol.

DORA: A Destination-Oriented Routing Algorithm for Energy-Balanced Wireless Sensor Networks

This research presents a new multichain routing strategy named the destination-oriented routing algorithm (DORA). The proposed design generates a new multichain routing scheme to transmit packets for energy-balanced WSNs. Based on the multichain routing, the optimal transmission distance between any two nodes is derived by the mathematical analysis model. With the optimal distance, the design criterion is to select the furthest forwarding node within the communication range and the direction to the sink, thus forming the multichain structures by accurate distance, direction, and multiple paths. With the shorter chain routing, unnecessary energy loss can be reduced to prolong the global network lifespan. The simulation results demonstrate that the proposed DORA doubles the network lifespan, compared with that of conventional PEGASIS and improves 60% lifespan on the RPC protocol.

A Bi-Objective Natural Gas and Liquefied Natural Gas Supply Chain Location and Routing Problem by Using ε – Constraint Method

A Bi-Objective Natural Gas and Liquefied Natural Gas Supply Chain Location and Routing Problem by Using ε – Constraint Method

Dynamic Q-learning and Fuzzy CNN Based Vertical Handover Decision for Integration of DSRC, mmWave 5G and LTE in Internet of Vehicles (IoV)

Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such heterogeneous networks. Instead of using better technology for long ranges and other types of traffic, the vehicles are using devoted short range communications at short ranges. Commonly, networks for handovers were used to be selected directly or with the available radio access it used to connect automatically. With the help of this, the hand over occurrence now takes places frequently. This paper is based on the incorporation of DSRC, LTE as well as mm Wave on Internet of vehicles which is integrated with the Handover decision making algorithm, Network Selection and Routing. The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using Fuzzy Convolution Neural Network commonly known as FCNN which makes the fuzzy rules by considering the parameters such as strength of its signal, its distance, the density of the vehicle, the type of its data as well the Line of Sight (LoS). V2V chain routing is presented in such a manner that V2V pairs are also selected with the help of jellyfish optimization algorithm considering three metrics – Vehicle metrics, Channel metrics and Vehicle performance metrics. OMNET++ simulator is the software in which system is developed. The performance evaluation is done according to its Handover Success Probability, Handover Failure, Redundant Handover, Mean Throughput, delay and Packet Loss.

Performance evaluation of vertical handover in Internet of Vehicles

Abstract Internet of Vehicles (IoV) is developed by integrating the intelligent transportation system (ITS) and the Internet of Things (IoT). The goal of IoV is to allow vehicles to communicate with other vehicles, humans, pedestrians, roadside units, and other infrastructures. Two potential technologies of V2X communication are dedicated short-range communication (DSRC) and cellular network technologies. Each of these has its benefits and limitations. DSRC has low latency but it limits coverage area and lacks spectrum availability. Whereas 4G LTE offers high bandwidth, wider cell coverage range, but the drawback is its high transmission time intervals. 5G offers enormous benefits to the present wireless communication technology by providing higher data rates and very low latencies for transmissions but is prone to blockages because of its inability to penetrate through the objects. Hence, considering the above issues, single technology will not fully accommodate the V2X requirements which subsequently jeopardize the effectiveness of safety applications. Therefore, for efficient V2X communication, it is required to interwork with DSRC and cellular network technologies. One open research challenge that has gained the attention of the research community over the past few years is the appropriate selection of networks for handover in a heterogeneous IoV environment. Existing solutions have addressed the issues related to handover and network selection but they have failed to address the need for handover while selecting the network. Previous studies have only mentioned that the network is being selected directly for handover or it was connected to the available radio access. Due to this, the occurrence of handover had to take place frequently. Hence, in this research, the integration of DSRC, LTE, and mmWave 5G is incorporated with handover decision, network selection, and routing algorithms. The handover decision is to ensure whether there is a need for vertical handover by using a dynamic Q-learning algorithm. Then, the network selection is based on a fuzzy-convolution neural network that creates fuzzy rules from signal strength, distance, vehicle density, data type, and line of sight. V2V chain routing is proposed to select V2V pairs using a jellyfish optimization algorithm that takes into account the channel, vehicle characteristics, and transmission metrics. This system is developed in an OMNeT++ simulator and the performances are evaluated in terms of mean handover, handover failure, mean throughput, delay, and packet loss.

Congestion Minimization for Service Chain Routing Problems With Path Length Considerations

Network function virtualization (NFV), with its perceived potential to accelerate service deployment and to introduce flexibility in service provisioning, has drawn a growing interest from industry and academia alike over the past few years. One of the key challenges in realizing NFV is the service chain routing problem, whereby traffic must be routed so as to traverse the various components of a network service that have been mapped onto the underlying network. In this work, we consider the online service chain routing problem. We route the service chain with the goal of jointly minimizing the maximum network congestion and the number of hops from the source to the destination. To this end, we present a simple yet effective online algorithm in which the routing decision is irrevocably made without prior knowledge of future requests. We prove that our algorithm is O(log m)-competitive in terms of congestion minimization, where m is the number of edges of the underlying network topology, and we show that this ratio is asymptotically optimal.