Optimal Routing Strategy Research Articles

Minimizing network-wide emissions by optimal routing through inner-city gating

In this paper, we propose a control and optimization framework to reduce network-wide emissions in an urban traffic network. The framework is comprised of two layers. The first layer (optimal green routing) predicts the optimal splitting coefficients for all Origin–Destination (OD) pairs crossing the city center or bypassing. As implementing optimal routing strategies on the field is almost impossible as we do not have direct control over the user’s decisions, we choose to balance travel time through gating at the city perimeter so that the usual Dynamic User Equilibrium (DUE) discipline matches the optimal splitting coefficients. The second layer then adjusts inflows at the city gates to the DUE solution based on the instantaneous travel times corresponding to the optimal routing strategy for each OD pair. The accumulation-based Macroscopic Fundamental Diagram (MFD) model of a single reservoir city with seven arterial routes and six bypass alternatives is developed. A Linear programming problem is formulated to determine the optimal splitting coefficients and a Nonlinear Model Predictive Control (NMPC)-based gating control strategy is designed to track the optimal splitting coefficients. The network-wide emission control framework is compared to three other gating strategies aiming to (i) optimize traffic conditions or (ii) minimize emissions, in the inner-city only, and (iii) optimize traffic conditions in the whole network. A comprehensive analysis conducted on all four approaches is presented. We compare the results of the controlled case with respect to the uncontrolled case and with respect to each other. The comparison results show that: (i) the proposed network-wide emission control strategy significantly outperforms the other two simpler control strategies focusing on inner-city only in reducing the total emission, (ii) but also improves the in total time spent and mean speed at the network-level.

Exchange Fees and Overall Trading Costs

In the US market, two popular exchange fee structures are “maker-taker” and “inverted” (also known as “taker-maker”). Maker-taker exchanges charge fees for taking liquidity and give rebates for providing liquidity, while inverted exchanges give rebates for taking liquidity and charge fees for providing liquidity. Using live trade data from an institutional investor that applies a flexible trading approach and seeks to participate in the natural market liquidity, we find that net of fees and rebates, trading costs are statistically indistinguishable between maker-taker and inverted exchanges. This finding has important implications for venue routing decisions in trading algorithms that do not demand immediacy, suggesting that the optimal routing strategy in terms of the tradeoff between cost and volume per unit of time is a pro-rata allocation across venues. It is also relevant to the recent discussions on legislation to fix fees across all trading venues.

Important factors to daily vehicle routing cost of battery electric delivery trucks

This study investigates the effects of key technological and operating factors on daily routing of battery electric truck (BET) in urban goods delivery. The key factors are service area, number of customers, battery capacity, number of charging stations, vehicle capacity, and charging rate. A primary cost metric used is daily vehicle routing cost (DVRC), which is defined as the sum of driver salary associated with driving time and idling time for en-route battery recharging activities, and electricity (energy) cost. It is estimated with a proposed green EVRP (G-EVRP) model. Overall, BET works with the economies of scale. However, BET may be better suited for a market place with high number of customers (customer density) at a city or a county scale but often technologically or financially constrained at a regional (intercity) scale. For a given network size and number of customers, battery capacity must be carefully chosen to minimize the BET DVRC. Vehicle capacity affects the number of vehicles needed to dispatch and the optimal fleet size is a compromise between the level of consolidation and that of en-route recharging activity. Little effect is found in number of charging stations on BET daily routing in the study setting as optimal routing strategies typically result in either zero or very low en-route recharging activity level.

Route Optimization Scheme for Mobile Content Sources in Content Centric Networking

Route Optimization Scheme for Mobile Content Sources in Content Centric Networking

Secure and Opportunistic Routing in WSN using Associative Secure Optimized Routing Algorithm

Under wireless communication domain optimized routing for wireless networks is the trending scenario for research. Various older schemes faces performance issues such as consistency, alongside ensuring routing security which significantly causes performance degradation. Although the current mechanisms designed for routing are low-cost and precise positioning is also not essential but security still requires emphasis to be given during design. In this paper a secure and optimized routing technique is proposed for WSN mentioned as Associative Secure Optimized Routing (ASOR) Algorithm. Optimized data progression is the prime focus of ASOR alongside ensuring security to it. ASOR operates in two phases, in first phase it identifies and club the associative nodes based on the defined indulgent constant(IC) based on explicit trust, strength of link and link_quality parameters. Explicit trust and strength of link are directly measurable however to measure quality, survival time of link and the incurred delay are taken into consideration. Secondly the selection of optimized nodes using proposed ASOR is carried out through defined aptness function(AF) which is dependent on explicit_trust(ET), strength_of_link(SOL), node_quality(NQ) and distance. Proposed associative secure optimized routing scheme’s performance is evaluated based on metrics for adhoc sensor network of 100 dynamic nodes in the presence of worm_hole and black_hole attacks. Comparatively higher throughput and detection_rate whereas lower distance and delay are indicated by ASOR, which are relatively better than opposing methods. ASOR shows significantly high detection_rate and throughput as 55.7 and 44.1 respectively and comparatively less delay and distance as 13 and 168.2 which are significantly better than opposing methods. ASOR can be effectively used in WSN for real time application such as in agriculture, industries etc.

Assessment of the DRT System Based on an Optimal Routing Strategy

Demand responsive transport (DRT) is operated according to flexible routes, dispatch intervals, and dynamic demand, is attracting a lot of attention. The biggest characteristic of the DRT service is that the vehicle routes and schedules are operated optimally based on real-time travel requests of using passengers without fixed operating schedules. This study analyzed the feasibility of implementing the DRT service by analyzing the benefits for the users and cost of the operator from the effects of increasing public transportation use and providing personalized mobility service based on DRT implementation by the introduction of DRT using multi-agent transport simulation (MATSim). Through the simulation, the DRT is expected to provide convenient, fast, and cost-effective mobility services to customers; provide an optimal vehicle scale to providers; and, ultimately, achieve a safe and efficient transportation system.

A Stochastic Single Vehicle Routing Problem with a Predefined Sequence of Customers and Collection of Two Similar Materials

We suppose that a vehicle visits N ordered customers in order to collect from them two similar but not identical materials. The actual quantity and the actual type of material that each customer possesses become known only when the vehicle arrives at the customer’s location. It is assumed that the vehicle has two compartments. We name these compartments, Compartment 1 and Compartment 2. It is assumed that Compartment 1 is suitable for loading Material 1 and Compartment 2 is suitable for loading Material 2. However it is permitted to load items of Material 1 into Compartment 2 and items of Material 2 into Compartment 1. These actions cause extra costs that are due to extra labor. It is permissible for the vehicle to interrupt its route and go to the depot to unload the items of both materials. The costs for travelling from each customer to the next one and the costs for travelling from each customer to the depot are known. The objective is to find the routing strategy that minimizes the total expected cost among all possible strategies for servicing all customers. A dynamic programming algorithm is designed for the determination of the routing strategy that minimizes the total expected cost among all possible strategies. The structure of optimal routing strategy is characterized by a set of critical numbers for each customer.

A routing strategy with energy optimisation based on community in mobile social networks

The paper proposes an optimal energy efficiency routing strategy minimised network energy consumption with a given delay based on community. Firstly, the expected energy consumption and delay of message delivery are obtained through Markov chain. Then a cost function combined with energy consumption and delay for message deliver is designed. Thus we obtain the optimisation function for delivering message of relay to comprehensive cost. Further, the reward function of relay is given. Finally, the optimal expected reward of optimal relay is achieved using the optimal stopping theory for realising the optimal energy efficiency routing strategy. In simulations, the average energy consumption, the average delay and the average delivery ratio of routing optimisation strategy are compared with other routing strategies in related literatures. The results show the proposed strategy has smaller average energy consumption, smaller average delay and bigger average delivery ratio, and better energy consumption optimisation results can be achieved.

A routing strategy with energy optimisation based on community in mobile social networks

The paper proposes an optimal energy efficiency routing strategy minimised network energy consumption with a given delay based on community. Firstly, the expected energy consumption and delay of message delivery are obtained through Markov chain. Then a cost function combined with energy consumption and delay for message deliver is designed. Thus we obtain the optimisation function for delivering message of relay to comprehensive cost. Further, the reward function of relay is given. Finally, the optimal expected reward of optimal relay is achieved using the optimal stopping theory for realising the optimal energy efficiency routing strategy. In simulations, the average energy consumption, the average delay and the average delivery ratio of routing optimisation strategy are compared with other routing strategies in related literatures. The results show the proposed strategy has smaller average energy consumption, smaller average delay and bigger average delivery ratio, and better energy consumption optimisation results can be achieved.

Optimal power routing scheme between and within interlinking converters in unbalanced hybrid AC–DC microgrids

An optimal power routing (OPR) scheme between and within interlinking converters (ICs) in unbalanced hybrid AC–DC microgrids to minimise the power imbalance factor at the point of common coupling, active power losses, and voltage deviation indices for microgrids in grid-connected operating mode is proposed in this study. These goals are achieved through a multi-objective optimisation model by optimal distributing of mobile loads between available charging stations and at the same time, OPR within three phases of three-phase four-lag AC/DC converters. Numerical results obtained from implementing the proposed method on the modified IEEE 13-bus system, as an unbalanced hybrid microgrid, and IEEE 34-bus test system, as an unbalanced distribution system, demonstrate that proposed OPR algorithm is successful to satisfy the optimisation goals. For this purpose, four case studies are defined and studied to demonstrate the unique features of the proposed OPR comparing with other power routing schemes. In addition to simulation results, the OPR scheme between ICs is realistically implemented at Florida International University smart grid testbed to show the effect of the power routing on energy losses reduction.

A Novel Discounted Min-Consensus Algorithm for Optimal Electrical Power Trading in Grid-Connected DC Microgrids

Due to the private ownerships of various resource and equipment in a microgrid, it is essential to design a proper power trading strategy for a mutually satisfying partnership among suppliers and users. This paper proposes a novel discounted min-consensus algorithm to discover the optimal electrical power trading route with minimal power loss in dc microgrids. The proposed distributed algorithm only requires information exchange among neighbors based on a peer-to-peer communication protocol, which is robust to topology changes and enables parallel data processing to achieve fast decision-making. The convergence and optimality of the proposed discounted min-consensus algorithm are guaranteed by rigorous analysis. Simulation results of the 10-bus, 68-bus, and 1062-bus power systems demonstrate the effectiveness of the proposed distributed optimal routing strategy for electrical power trading. Moreover, the proposed novel distributed algorithm sheds light on applying distributed control and optimization approaches to solve problems arising in artificial intelligence.

Optimized and Secure Routing in WSN using Collaborative Optimized Routing Algorithm

Optimized routing in wireless network is the trending research area in ad-hoc wireless communication. Performance of outmoded schemes designed for security during the process of routing in sensor networks significantly affected by consistency and security issues. However the present routing schemes are inexpensive and need no rigorous positioning but security is still unavoidable apprehension. An optimized routing scheme is proposed for WSN in this paper which is named as Collaborative Optimized Routing Algorithm (CORA), which precisely focuses on optimized progression of data during routing along with security aspects. CORA functions in two phases, first phase is to identify and group the collaborative nodes through defined lenient constant based on direct_trust, link_strength and quality parameters. Direct_trust and link_strength are direct measures but to evaluate quality, link survival time and caused delay are considered. Whereas in second phase suit function is defined for choosing the optimized nodes using the proposed CORA, based on direct_trust, link_strength, quality and distance. Performance of proposed collaborative optimized routing scheme is assessed based on the measurement metrics with 50 dynamic nodes ad-hoc sensor network in presence of selective forwarding and denial-of-service attacks. CORA indicates the higher detection rate and throughput as 54.9 and 41.6 respectively and lower delay and distance as 15 and 158.8 respectively which are significantly better than competing schemes.

Linking granular computing, big data and decision making: a case study in urban path planning

Granular computing, an emerging information processing paradigm transforming complex data into information granules at different scales so that different features and regularities can be revealed, offers an essential linkage between big data and decision making. By using innovative technologies of granular computing that transforms big data collections into information granules, we would be at position of recognizing and exploiting the meaningful pieces of knowledge present in data, and produce sound, and practically supported decisions. In this study, we first summarize a general scheme of big data–granular computing–decision making and then present a case study where we detect the important traffic event information by collecting and analyzing social media data, and transform them into probabilistic information granules that can be used for urban routing navigation. We propose a robust fastest path optimization model to incorporate the impact of traffic events and generate the optimal routing strategy. Real-life experiments are carried out in regional Chaoyang District, Beijing, as well as the backbone roadway network of Beijing, which illustrate the effectiveness of our proposed big data-driven decision-making method. Our study provides new evidence demonstrating that big data can be efficiently used to enhance decisions and granular computing with this regard. The concept of the proposed scheme can be easily extended for decision-making modeling in other domains.

Multi-objective optimization of real-time customized bus routes based on two-stage method

Customized public transit, as a diversified passenger service mode proposed under the new situation, not only deals with the comfortable and convenient commuting needs of most commuters, but also improves the efficiency of road use effectively and alleviates the road traffic pressure in the morning and evening rush hours. Currently, various routing optimization methods based on static passenger requests have been applied to the optimization design of customized bus routing in major cities, but the static passenger requests cannot satisfy the dynamic passenger requests of random users at present Therefore, it is urgent and necessary to investigate real-time customized bus routing optimization methods based on static passenger requests and dynamic passenger requests. In this paper, our goal is to present an approach to tackle two important issues of real-time customized bus route optimization under stochastic user demand: one is that how to deal with dynamic travel requests in a specific road network to improve passengers’ satisfaction with customized bus services; the other is that how to employ specific methods to achieve real-time updating of customized bus routes to meet the random user’s travel requests. Starting from the whole operation process of customized bus, we firstly construct a multi-objective optimization model of customized bus route, and clearly depicts the four processes of customized bus operation in the form of mathematical expressions. Second, we design a two-stage method based on NSGA-II algorithm to solve the model, in which the first stage deals with static bus travel requests to obtain the initial customized bus route optimization scheme, and the second stage deals with dynamic bus travel requests to update the customized bus route in real time. Finally, a two-stage case is illustrated to verify the correctness and validity of the model and the method for solving the real-time customized bus route optimization problem under stochastic user demand.

Pricing and Routing Mechanisms for Differentiated Services in an Electric Vehicle Public Charging Station Network

We consider a Charging Network Operator (CNO) that owns a network of Electric Vehicle (EV) public charging stations and wishes to offer a menu of differentiated service options for access to its stations. This involves designing optimal pricing and routing schemes for the setting where users cannot directly choose which station they use. Instead, they choose their priority level and energy request amount from the differentiated service menu, and then the CNO directly assigns them to a station on their path. This allows higher priority users to experience lower wait times at stations, and allows the CNO to directly manage demand, exerting a higher level of control that can be used to manage the effect of EV on the grid and control station wait times. We consider the scenarios where the CNO is a social welfare-maximizing or a profit-maximizing entity, and in both cases, design pricing-routing policies that ensure users reveal their true parameters to the CNO.

Dynamic trucking equilibrium through a freight exchange

This paper proposes a new hyperpath-based dynamic trucking equilibrium (DTE) assignment model. Unlike existing freight assignment models, we focus on the interactions between individual truck operators that solely compete for loads advertised on an online freight exchange. The competitors are assumed to follow optimal bidding and routing strategies – represented using a hyperpath – to maximize their expected profit. The proposed DTE model (1) predicts system-wide truck flows (including empty truck flows), (2) identifies efficiency improvements gained by network-wide visibility, and (3) lays the foundation for building a system optimal model. We rewrite the DTE conditions as a variational inequality problem (VIP) and discuss the analytical properties of the formulation, including solution existence. A heuristic solution algorithm is developed to solve the VIP, which consists of three modules: a dynamic network loading procedure for mapping hyperpath flows onto the freight network, a column generation scheme for creating hyperpaths as needed, and a method of successive average for equilibrating profits on existing hyperpaths. The model and the solution algorithm are validated by numerical experiments constructed from empirical data collected in China. The results show that the DTE solutions outperform with wide margin the benchmark solutions that either ignore inter-truck interactions or operate trucks according to suboptimal routing and bidding decisions.

Optical Burst Routing by Balanced Wavelength Allocation Under Multi-objective Quality Metrics

The optical burst switching is promising optical network technique that intends to transmit data in the form of bursts with the ability of dynamic switching between sub-wave lengths. One among the crucial issues of the OBS networks is burst loss due to suboptimal and often impediment burst scheduling and wavelength allocation strategies. Hence, the major contributions of the contemporary research in regard to OBS networks entails to portray the optimal burst routing strategies. The contribution of this manuscript is balanced wavelength allocation using differential evolution method in short referred as BWADE, which selects an optimum multi-hop route that intends to uses multi-objective QoS aspects as a fitness scale for differential evolution algorithm. The experimental study concluding that the proposed BWADE outperforming the other contemporary routing approaches through simulation outcomes.

Application of a variable neighborhood search algorithm to a fleet size and mix vehicle routing problem with electric modular vehicles

The emergence of new promising technologies used in electric transportation offers opportunities to achieve sustainable urban freight deliveries. In particular fleets of electric vehicles are seen as an efficient ecologic and economic transportation alternative for operating last-mile goods distribution in cities. This paper deals with the use of electric modules which can be added or removed from a freight vehicle to help gain capacity or respect delays when delivering customers. Since the electric vehicles have a limited battery capacity, they have to stop and recharge them at dedicated stations during the delivery tours. However, with the modular feature, this problem of disturbing the whole distribution process for recharging the batteries can be reduced and sometimes overlooked. This paper objective is to design and find the optimal routing strategy for a fleet of electric modular vehicles operating the last-mile delivery, with the minimal cost including the acquisition, the travel and the recharging ones. The research problem arising from the structure of the fleet can be associated with both a truck and trailer problem and a fleet size and mix vehicle routing problem with time windows using electric vehicles. This problem is presented in the first part of the article with the proposed solving procedure. More precisely, for big instances of the problem which is NP-Hard, a Variable Neighborhood Search (VNS) is designed and adapted. Experimental studies carried out on some benchmark instances show that the VNS performs impressively well in comparison with the best solving approaches of the literature.

The Reinforcement Learning based Local Routing Optimization for Ad-hoc Network

A reinforcement learning based local routing optimization scheme with two different semiconductor chips – hub IC and node ICs is proposed for the ad-hoc network. The received signal strength indicator (RSSI) in IC generates voltage information for analyzing network quality between each node and collected RSSI data are used for input and reward function of the learning agent. The Qlearning method is utilized for reinforcement learning. The chip fabricated with a 0.18 μm CMOS process, which uses the standard supply voltage of 1.5 V, achieves the lowest power consumption of 274 μW at the supply voltage of 0.8 V. The proposed reinforcement learning based local routing optimization for the ad-hoc network reduce 64 % of total network power consumption compare to the conventional infrastructure based network.

Certification of the service of pediatric hematology-oncology in the subjects of the Russian Federation on the basis of infographic mapping

The purpose of the study was to create a certification of the service of pediatric hematology-oncology of the constituent entities of the Russian Federation (RF) using infographics and digitalization. Materials and methods. For the first time, infographic maps were used as a tool for certification, as a way of visualizing a large amount of data on the organization of pediatric hematology-oncology service in each subject of the RF. They allowed to show the specifics of the medical and organizational structure of the profile service in the subjects of the RF, their interaction with other subjects and federal centers. Processing of the cartographic image of the subject of the RF was carried out in the Adobe Illustrator graphic editor. A modifiable tool for creating infographic maps created in the MS Power Point program, with saving the results in PDF and PNG formats. Results. In the course of the study, a “Passport of Service” was prepared, containing infographic maps of 77 constituent entities of the RF. The maps in a graphic form provide information on resources available in the subject of the RF: a medical organization of the 3rd level with bed structure and the possibility of visualization, its personnel; the number of multicenter protocols and clinical guidelines used in the subject of the RF; logistics of patients, taking into account the territorial distance from the specialized beds, the presence in the region of the station/department of blood transfusion; the possibility of radiotherapy in the region; the presence of institutions where they provide palliative and rehabilitation assistance; the name of the federal and regional specialized centers, with whom the interaction is carried out, the well-established telemedicine consultation system with the Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology. The database of contacts of the main freelance children’s specialists of hematologists and oncologists of the RF was also updated. Conclusion.Thus, data were collected for visual display and to create specific conditions for developing optimal patient routing schemes and forming proposals for optimizing the service. The obtained “Passport of the Children’s Hematology-Oncology Service of the Russian Federation” can be used in the work of hospitals, scientific centers and professional communities.