Routing Model Research Articles

APPLICATION OF TRANSPORTATION MODEL TO SOLVE TANKERS’ – ROUTING PROBLEM

The problem of selecting minimum cost routes for tankers in distributing petroleum products and satisfying customers’ requirement without scarcity in Nigeria remains a huge challenge to major marketers in the oil industries. The cost of transporting petroleum products from sources to destinations matters a lot to oil marketers because of the direct impact it has on their profits. The means of distributing petroleum products from refineries to depots or filling stations are tankers’ routing and pipelines. In this research, we extended some existing tankers’-routing models in literature which use a discrete integer programming approach to determine efficient and effective distribution of petroleum products. Consequently, we developed a new transportation linear programming algorithm to determine minimum cost routes in the delivery of petroleum product from their supply centers (refinery) to demand centers (filling stations). The significance of the application we adopted in this research lies in the modified distribution approach to tackle the complexity involved when transportation problems are formulated as linear programming problem having several variables and constraints. In this research, we formulate a new version of transportation model of tankers’ routing with the aim of reducing the cost of petroleum products delivery. The proposed transportation linear programming model was applied to a numerical example alongside other existing transportation algorithms. It is observed that, the new algorithm produced approximately the same total cost obtained by using other existing algorithms

Integrated Optimisation Model of Daily Freight Train Scheduling and Dynamic Railcar Routing Based on a Two-Layer Space-Time Network

This paper focuses on daily freight train scheduling and dynamic railcar routing problems for rail freight transportation at the operational level. Two mixed integer linear programming models that adopted different strategies were formulated based on a continuous two-layer time-space network. We simultaneously considered the benefits of railroad company and service quality when setting the objective function. By solving the models, we can distribute the dynamic railcar flows to the train paths in the basic train timetable to obtain the daily train operation plan over a short time horizon (e.g. a day), which will be helpful for dispatchers to make decisions such as the empty railcar distribution and car routing (trip planning). Finally, we compared two models on a part of the Chinese railroad network. The results show that the second model can effectively improve the efficiency of railroad freight transportation.

Determinants Influencing Korean Agri- Food Companies’ Modes of Overseas Market Entry

Purpose - Faced with the constraints of a limited domestic market, the Korean agri-food industry necessitates regional diversification through the establishment of offshoring production subsidiaries. This study tests the validity of Root’s model of the determinants of offshoring and sequential entry to overseas markets on Korean agri-food companies’ offshoring of production subsidiaries. Design/Methodology – This study employs the convergent mixed-methods research design, wherein qualitative and quantitative data collection and analysis are conducted, then compared, before interpreting the data affecting Korean agri-food outward direct investment (ODI). Methodologically, based on Korean agri-food manufacturing company ODI data from the KOTRA, a survey was conducted to quantitatively analyze the factors influencing their selection of production subsidiaries for ODI. Findings – Though Korean agri-food companies also consider Root’s external factors when selecting overseas entry methods, the sequential entry route and selection model, factors theorized to influence Root’s overseas market entry method, were not found to be statistically significant. Instead, they tailor their market entry approach based on the type of investment and the operating objectives of the local subsidiary. This is attributed to Korean agri-food companies’ deviation from sequential entry routes when entering markets through ODI, as well as the determination of the form and type of investment of these overseas subsidiaries according to the purpose of their operations. Originality/value - This study distinguishes itself by employing a convergent mixed-methods design, which combines quantitative and qualitative analyses to identify the determinants of ODI market entry by Korean agri-food companies. Furthermore, it evaluates Root’s market entry model from the perspective of Korean agri-food companies.

AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences

Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant; however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising; however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to the EV driver’s preference, AFARM checks the set of candidate paths at the source point and selects the appropriate path for the vehicle to drive based on its current status. Consequently, AFARM provides an anxiety-free navigation model that allows for a reliable and environmentally friendly driving experience, promoting this alternative mode of transportation.

Accounting for passenger loss in bus bunching reduction: a robust real-time speed control method

ABSTRACT Bus bunching is a common phenomenon resulting from the inherent instability influenced by factors such as traffic and passenger behaviour. However, the issue of passenger loss caused by extended waiting time, is often overlooked despite being a significant concern for passengers. This paper addresses this problem by modelling passenger arrival and loss as a Markov birth–death process. We present a transit route model that incorporates passenger loss while considering additional factors, such as stochasticity, overtaking, passenger alighting, and bus capacity constraints. The primary objective of our model is to minimise passenger loss. To achieve this, we conduct an analysis about the characteristics of passenger loss. Under mild assumptions, we prove that passenger loss increases as the headways between buses exhibit greater variance. We also develop a robust optimisation (RO) method and corresponding algorithms to minimise passenger loss. To validate the effectiveness of our control method, we conduct numerical experiments using both computer-generated and real-world data. The results demonstrate that our method effectively reduces bus bunching, passenger loss, and waiting time compared to the existing literature.

A Triple Bottom Line optimization model for assignment and routing of on-demand home services

‘On-demand home services’ is a fast-growing industry where online platforms match independent service professionals with customers seeking aid for household tasks. In this paper, we study the assignment and routing of service professionals for serving customers of an on-demand home services platform considering the Triple Bottom Line (TBL) criteria for ensuring sustainability in operations. We characterize this as the Home Services Assignment and Routing Problem with the Triple Bottom Line (HSARP-TBL) and implement a Mixed Integer Linear Programming (MILP) model for solving it. We assign service professionals to customers based on their desired time slots and also transport modes for each customer visit by a professional, considering either combinations of public transport or a personal vehicle for each professional’s tour. The objective is to minimize costs due to time window violations and uncovered customers, catering to the economic pillar of the TBL. We incorporate additional constraints related to the TBL by improving customer satisfaction based on the ratings of assigned professionals to customers, with and without subscription (economic), controlling emissions due to transportation of professionals (environmental) and ensuring equity in service allocation and net earnings between professionals (social). For tackling large instances we implement a Hybrid Genetic Search (HGS) algorithm adapting it to our problem setting. We demonstrate that the HGS outperforms the MILP model systematically for large instances in terms of solution value and computational time. Finally, we observe that for some instances, without worsening the primary economic objective, all the TBL indicators can be improved.

Communication network robust routing optimization in an integrated energy cyber–physical system based on a random denial-of-service attack

The integration of power grids and communication networks in smart grids enhances system safety and reliability but also exposes vulnerabilities to network attacks, such as Denial-of-Service (DoS) attacks targeting communication networks. A multi-index evaluation approach is proposed to optimize routing modes in integrated energy cyber-physical systems (IECPS) considering potential failures from attacks. Security and economic service evaluation indexes are incorporated to quantify the significance of information flow routing. An optimization model for electric, heat, and gas routing in worst-case scenarios is formulated and solved using a column and constraint generation algorithm. The optimized routing method effectively circumvents specified attack areas, reducing the correlation degree of communication links within the attack area. Comparison with single-service optimization methods demonstrates the superiority of the proposed approach in mitigating the impact of network attacks on IECPS. The study highlights the importance of considering security and economic factors in optimizing routing modes to enhance the resilience of integrated energy cyber-physical systems against network attacks, particularly DoS attacks on communication networks. The evaluation index approach presented in this study provides a comprehensive method for assessing the importance of communication links in IECPS and optimizing routing modes to improve system robustness and reliability in the face of network attacks.

Trust Enabled Cluster Aware Routing Protocol Design with Energy Considerations using Fuzzy Strategies

In this modern era, wireless communication technologies are getting high impact over network industries. Due to this drastic development of communication technologies, a network needs to be more precise with certain metric such as: energy consumption, throughput, trust management, performance and lifetime. These are all the primary concern while handling the transactions with wireless communication systems. In this paper, a new approach of network routing protocol is introduced, which will replace the issues found in existing routing protocols and provide good support while communication between entities. The proposed paper introduces a new routing protocol called Modified Adhoc On-Demand Distance Vector (MAODV), in which the classic AODV protocol supports mobile nodes with several impacts such as mobility, failure prevention, loss management and so on. However, the issues in industry still remains, so, that a new approach or routing protocol is required to overcome such issues. This is the concern to develop a new routing protocol for establishing communication between mobile nodes or wireless devices. The drastic development of communication devices requires some latest technologies to provide range free communication model with the assistance of internet support. A term, 'Internet of Things (IoT)', which rules the world now-a-days through its power of connectivity and establishing the bridge between communication devices around the globe without any range restrictions. In this paper, a cluster based routing strategy is designed with the help of fuzzy assistance. The cluster based routing protocols divides the range of communication into clusters and electing the cluster head based on the communication needs and provides a range free communication model with the hands of IoT support. This cluster based routing model provides support for both inter and intra-cluster communications, which all will be brief in further sections. The proposed model of wireless communication assures the following things such as Improved Energy Efficiency, reduced packet loss and delay, improved throughput ratio and lifetime over resulting scenario with the help of a new algorithm called Fuzzy Assisted Cluster enabled Routing Model (FACeRM) with the association of new routing protocol MAODV.

Taylor-Spotted Cat Optimization (Taylor-SCO): An Energy-Efficient Cluster Head Selection Algorithm with Improved Trust Factor for Data Routing in WSN

Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps.

Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA

In order to tackle the issue of carbon emissions in logistics and distribution, a vehicle routing model was proposed with the aim of minimizing the overall cost, which includes the vehicle’s fixed cost, transportation costs, and carbon emission costs. An enhanced genetic algorithm, based on a modified Nearest Neighbor Construction (NNC) method, was developed to solve this model. A comparative analysis was conducted using the Solomon dataset to study the impact of carbon emissions on vehicle routing optimization, comparing scenarios with and without considering carbon emission costs. The research findings revealed that the Improved NNC (INNC) method exhibited faster convergence compared with the random generation and random insertion methods. Despite a slight increase of 0.5127% in transportation cost when factoring in carbon emission costs, there was a decrease of 4.6914% in carbon emission costs and 0.3578% in total cost. These results offer theoretical insights and empirical evidence to inform the development of models for the logistics industry in the context of a low-carbon economy.

ROBUST OPTIMIZATION MODEL OF CONTAINER LINER ROUTES IN FEEDER LINE NETWORK

The universal application of the hub-and-spoke maritime network makes feeder line network key to restricting the quality and efficiency of maritime transportation. However, container liner routes in feeder line network are susceptible to the changes in shipment demand and international fuel prices. Therefore, based on the hub-and-spoke maritime network, this paper constructs a robust optimization model of container liner routes in feeder line network. Under the capacity and time constraints, routes optimization and ship equipment under uncertain environment are analysed. An improved tabu search algorithm was designed based on the characteristics of the model. The example analysis proves that the model can still ensure the robustness of routes under uncertain environment, which is more applicable than the deterministic model.

Modelling runoff components and hydrological processes in glaciated catchments of the inner Tien-Shan, Kyrgyzstan

Accurately quantifying runoff sources and understanding hydrological processes in glaciated mountain basins is essential for effective water resource management in the face of climate change. This study aimed to determine the contributions from various runoff sources in glaciated basins within the inner Tien-Shan mountains of Kyrgyzstan by utilizing integrated land surface, glacier energy-mass balance, and river routing models. To account for local topographic influences on solar radiation and cloud transmissivity processes, gridded meteorological forcing data were downscaled. The integrated models were then evaluated against observed discharge, glacier mass balance, and snow water equivalent, with a focus on the Kara-Batkak glacier reference site. Shortwave radiation correction was particularly important for improving the accuracy of model simulations. The results indicate that the peak glacier melt contribution occurs in July and August, with some basins reaching up to 54%. On an annual basis, the average contribution from glaciers across the basins was found to be 19%, while the ratios of snowmelt and rainfall were 58% and 23%, respectively. This study highlights the utility of integrated modelling approaches in understanding and quantifying runoff components in data-scarce high mountain regions. The incorporation of observed glacier data proved to be crucial for accurately representing hydrological processes under current climatic conditions. These findings underscore the significance of considering glacier dynamics and their influence on water resources to inform effective water management strategies in glaciated mountain basins.

Energy and delay aware routing model for smart crop monitoring application using internet of things

The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead.

A Framework for Provably Secure Onion Routing against a Global Adversary

Onion routing and mix networks are a central technology to enable anonymous communication on the Internet. As such, a large number of protocols and model variants have been explored in the field, which offer differing levels of privacy, exhibit vulnerabilities, or even supersede each other. These factors make discovering the appropriate formalization for new developments difficult, and some model variants have not been formalized at all. We address this issue by creating one parametrized framework that encompasses the onion routing and mix network models and functionalities with a global adversary in the related work. In doing so, we create a categorization of the variants of onion routing models in use in the related work and map common OR and mix network protocols to their variants. For each identified variant: Our framework offers i) an ideal functionality in the Universal Composability framework, and ii) game-based properties that imply realization of the ideal functionality when a protocol satisfies them. In effect, our framework both unifies and extends previous formalization efforts in the field.

A High‐Resolution, Daily Hindcast (1990–2021) of Alaskan River Discharge and Temperature From Coupled and Optimized Physical Models

AbstractWater quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate‐based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling‐Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non‐optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions.

生鲜农产品冷链集货车辆路径与货物配载联合优化研究

As a bridge connecting agricultural production and consumption, the circulation of agricultural products has the function of connecting supply and demand, guiding production and promoting consumption. However, the development of rural logistics in China is slow, and most logistics centers still rely on experience to plan the pick-up vehicle routings, resulting in long transport time and high cost. In order to improve the efficiency of pick-up and reduce transportation costs, a joint optimization model of cold-chain pick-up vehicle routing and cargo allocation for fresh agricultural products was proposed in this study. Soft time window constraint and three-dimensional loading constraints were considered, and the lowest pick-up cost was used as optimization goals in this model. In addition, adaptive large neighborhood search algorithm (ALNS) and heuristic depth-first search algorithm (HDFS) were combined to solve the model. A case study of Kunming International Flower Auction Center was conducted to compare the schemes of pick-up vehicle routing before and after optimization. Results demonstrate that the pick-up cost after optimization decreases by 9.6 %, the number of vehicles decreases by one, the total volume utilization rate of vehicles increases by 23 %, and the total load utilization rate of vehicles increases by 15 %. This study provides a model reference and solution method for enterprise operators to formulate schemes of pick-up vehicle routing quickly and reasonably.

Flood Routing and Dam Breach Parameter Calculation on Sepaku Semoi Dam

Flood Routing and Dam Breach Parameter Calculation on Sepaku Semoi Dam

Cluster-Based Cross Layer-Cross Domain Routing Model with DNN-Based Energy Prediction

In WSN, extending the network life is still a major problem that needs to be tackled. Cross-layer protocols are used to get around these problems. In this paper, a new cross-layer design routing model is presented using a clustering-based technique. The proposed model is proceeding with the optimal cluster-based routing model via a new algorithm. Initially during the network generation, the node’s energy is predicted by the deep learning model termed as DNN model on the basis of the distance between node and sink as the input. Subsequently, during the clustering process, the cluster head is optimally selected via a new optimisation algorithm named Self-Improved Shuffle Shepherd Optimisation (SISSO) Algorithm. The cluster head selection is done by considering the constraints including Link quality, Distance, Overhead, Energy and Delay. Finally, a Modified Kernel Least Mean Square (MKLMS)-based data aggregation process is to eliminate the redundant data transmission. The performance of the SISSO method is proven superior over other conventional approaches with regard to the alive node and network lifetime. In the alive node analysis of supernode, the proposed SISSO model achieves the maximal number of alive supernodes at 2,000 rounds (i.e. 0.67) than other conventional methods.

Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins

Abstract. A series of numerical experiments were conducted to test the connection between streamflow hydrographs at the outlet of large watersheds and the time series of hillslope-scale runoff yield. We used a distributed hydrological routing model that discretizes a large watershed (∼ 17 000 km2) into small hillslope units (∼ 0.1 km2) and applied distinct surface runoff time series to each unit that deliver the same volume of water into the river network. The numerical simulations show that distinct runoff delivery time series at the hillslope scale result in indistinguishable streamflow hydrographs at large scales. This limitation is imposed by space-time averaging of input flows into the river network that are draining the landscape. The results of the simulations presented in this paper show that, under very general conditions of streamflow routing (i.e., nonlinear variable velocities in space and time), the streamflow hydrographs at the outlet of basins with Horton–Strahler (H–S) order 5 or above (larger than 100 km2 in our setup) contain very little information about the temporal variability of runoff production at the hillslope scale and therefore the processes from which they originate. In addition, our results indicate that the rate of convergence to a common hydrograph shape at larger scales (above H–S order 5) is directly proportional to how different the input signals are to each other at the hillslope scale. We conclude that the ability of a hydrological model to replicate outlet hydrographs does not imply that a correct and meaningful description of small-scale rainfall–runoff processes has been provided. Furthermore, our results provide context for other studies that demonstrate how the physics of runoff generation cannot be inferred from output signals in commonly used hydrological models.

A comparative analysis on Relibale routing models in wireless sensor in smart applications

Researchers are drawn to Wireless Sensor Networks (WSNs) due to its versatility and possible use cases in a variety of fields, including target tracking and identification environmental sensing, industrial process tracking, and tactical systems. Recently, there has been a lot of focus on the particular issues that arise from implementing routing in sensor networks as opposed to the more conventional data routing in wireless connections. Due to the importance of energy conservation in the design of WSNs, numerous protocols for routing, power management, and data transmission have been developed for these networks. When it comes to environmental monitoring, traffic monitoring, etc., routing in WSNs is crucial. The primary objective of this study is to analyse routing issues and to analyse routing-related optimization issues. Features pertaining to routing issues like energy consumption, security, speed, and dependability are explored later. Attention has been paid to the routing protocols, which might vary by use case and network design. In this study, a comprehensive review is performed on the cutting-edge approaches to routing in WSNs. A brief introduction to WSNs and a discussion of the obstacles that routing protocols must overcome throughout their creation before moving on to a thorough examination of the various routing approaches now in use is considered. The comparative analysis is performed different routing techniques like Energy-Aware Routing for Software-Defined Multihop Wireless Sensor Networks (EAR-SDMWSN), DORA: A Destination-Oriented Routing Algorithm for Energy-Balanced Wireless Sensor Networks (DORA) and Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks (EP-LBR). The benefits and drawbacks of each routing method, with an emphasis on performance is also performed.