Set Of Routes Research Articles

Design and Evaluation of Passenger Ferry Routes

This article introduces a planning framework for improving and best utilizing existing water and ferry resources. It presents an analysis framework and formulation for designing and evaluating passenger ferry routes. The motivation for this work emanates from problems encountered by the ferry service in Hong Kong, including the continuous loss in ferry patronage. This loss is believed to be caused by the ferry’s relatively poor level of service and intense competition from more attractive alternatives. The article reports on an evaluation and design methodology for the entire passenger ferry network, consisting of a framework with operational objective functions that takes into account passengers, operator, and community interests. This framework considers both the evaluation of existing ferry routes and the design of new ones. The methodology presented combines the philosophy of mathematical programming approaches and decision-making techniques, allowing the user to select an efficient solution from a number of alternatives. The evaluation procedure established provides practical and measurable criteria for evaluating the “goodness” of each route and for comparison between sets of routes at the network level.

Service Constraint NCBQ trust orient secure transmission with IoT devices for improved data security in cloud using blockchain

Problem of secure transmission in cloud systems has been well studied; there exist number of approaches discussed around the problem. Number of threats has been identified in cloud which targets the services and data behind them. To mitigate the threats, different approaches are discussed in literature. The most approaches uses behavior of user in measuring trust, and some of them uses data level features in measuring trust of user. Also, various data encryption schemes are discussed which suffer with poor performance in security achievement. To handle this issue, an efficient Service Constraint NCBQ (Network Condition and Behavior Quality) Trust orient secure transmission protocol (SCNCQB-TSTP) is presented in this article. The proposed model involves in secure transmission by analyzing various factors of network, user behavior and quality of service. The method discovers set of routes to reach the service point and fetches several information about the transmission route like traffic, transmission frequency, bandwidth, IoT devices, hop count, throughput, and bandwidth utilization. According to the above information fetched about different routes, the method analyzes the trust of each route in three ways like Network Support Analysis, Behavior Support Analysis and QoS-Support Analysis. Based on the result of different analysis, the method estimates the value of Secure Transmission Score (STS). According to the value of STS, an optimal route has been selected to perform data transmission. Similarly, the method adapts Service Constraint Blockchain Model (SCBM) towards data security, which classifies the services in various levels. According to the service claimed by the user, the service data has been encrypted with specific class of scheme and key. Further, encrypted data has been placed is adapted to block chain to perform data transmission. The proposed SCNCQB-TSTP model improves the performance of secure transmission and data security.

BIM 기반 건축기계설비 시공상세도 추출을 위한 템플릿 구성

The purpose of this study was to improve and enhance the constructability of BIM, by suggesting a configuration of BIM template to quickly produce comprehensible shop drawings for domestic MEP/FP construction. Shop drawings that describe how the building and system components will be fabricated and installed during the construction should graphically depict dimension, specification, location of an element, and spacing and joint method between elements as accurately and precisely as possible. Thus, geometry information of model element and topology information between model elements, should be clearly defined in a MEP/FP BIM template. Specifically, construction specification and client’s requirement should be quantitative defined in advance, and then material and routing settings of the template should be customized with them, rather than using system defaults. While some elements can be modeled at LOD 300, the suggested elements and devices shall be defined at LOD 350.

Plug-in hybrid electric refuse vehicle routing problem for waste collection

Commercial waste collection is an essential service requiring efficient and reliable provision for customers. At the operational level, one of the most challenging problems is to design a set of refuse vehicle routes to collect waste from a set of bins. To be used multiple times, these vehicles must be emptied regularly throughout the day. This paper investigates a waste collection problem with a homogeneous fleet of plug-in hybrid electric refuse vehicles powered by two different power sources, i.e., electricity and compressed natural gas (CNG). In addition, realistic fuel consumption functions are used to estimate total energy requirements for each type of fuel, including refueling and recharging, and the detailed energy consumption along the path between two nodes of interest. We propose a Hybrid Threshold Acceptance (HTA) algorithm for this problem and denote it as the Hybrid Waste Collection Problem (HWCP). Extensive computational experiments confirm that the proposed HTA algorithm provides good results against current state-of-the-art algorithms designed for the electric vehicle routing problem. Out detailed computational results demonstrate the performance of our method considering either full or partial recharging, as well as the effect of different battery/tank capacities. Compared to the standard CNG or electric vehicles, we also show the benefits of using a fleet of hybrid electric refuse vehicles in terms of operational costs and total distance traveled.

A two-stage stochastic programming model for collaborative asset protection routing problem enhanced with machine learning: a learning-based matheuristic algorithm

ABSTRACT In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameters, such as travel and service times, obtaining profit by protecting an asset, and upper bounds of time windows, are considered as stochastic parameters. Generating proper scenarios for uncertain parameters has a large impact on the accuracy of the obtained solutions. Therefore, artificial neural networks are employed to extract possible scenarios according to previous actual wildfire events. The problem cannot be solved by exact solvers for large instances, so two matheuristic algorithms are proposed in this study to solve the problem in a reasonable time. In the first algorithm, a set of feasible routes is generated based on a heuristic approach, then a route-based mathematical model is used to obtain the final solution. Also, another matheuristic algorithm based on adaptive large neighbourhood search (ALNS) is proposed. In this algorithm, routing decisions are determined using the ALNS algorithm while other decisions are achieved by solving an intermediate mathematical model. The numerical analysis confirms the efficiency of both proposed algorithms; however, the first algorithm performs more efficiently.

Masalah Vehicle Routing Problem Pada Pengiriman Barang di kota Bandung utara dengan menggunakan Kluster KMeans dan Algoritma Nearest Neighbor

The Vehiscle Routing Problem is an optimization problem in determining the route of packet distribution with limited vehicle capacity. Routing in the distribution of packets is important so that the shortest route will be sought so that the package arrives on time. One of the algorithms that discusses the search for vehicle routes with VRP problems is the Nearest Neighbor Algorithm, as for the stages in finding the best route by enumerating all possible sequences of existing routes and selecting the best set of routes in order to find the shortest route from one node to all other nodes. so it becomes a connected route.

Past Human Mobility Corridors and Least-Cost Path Models South of General Carrera Lake, Central West Patagonia (46° S, South America)

Understanding the use of natural corridors is critical for characterizing the past use of marginally occupied landscapes at the Andean fringes of western Patagonia by the hunter-gatherer groups who inhabited this region. In this paper, we combine least-cost path models and archaeological surveys and excavations to determine the possible movements along the southern margin of General Carrera Lake. The methodology includes defining uncertainties that allow for modeling a set of equiprobable routes, thereby avoiding problems with errors and biases from predictors, such as slope, land-use cover, and seasonality. The results identify mobility corridor bottlenecks, i.e., geographical areas with a high probability of travel routes with equiprobable routes that converge. In addition, we identify areas where travel routes are likely to diverge into multiple semi-parallel routes. The study of archaeological sites provides stratigraphic data to control for the chronology and characteristics of mobility along this transect. The correlation between archaeological sites and paths, specifically for the control and winter scenarios, shows the quality of these route predictions. These results indicate the repeated use of internodal spaces that were effectively incorporated into mobility during the last three millennia.

Multisource data integration for targeted bus exterior advertising

Bus exterior advertising plays a significant role in outdoor advertising, since it provides frequent exposure to a large number of residents. Traditional route selection methods are generally based on a rough estimation, for example, the number of total passengers of a bus route or the geographical features along the bus route. Targeted bus exterior advertising remains a challenge as little is known about the characteristics of the people along the bus route. In this study, we are aiming at determining a set of bus routes for a given ad category to maximize advertising effectiveness, by mining multiple data sources, including mobile phone data, bus GPS data, smart card data (SCD), and land use data. Specifically, we first estimated the distribution of potential target audiences using mobile phone data and land use data. Two optimization models are proposed considering different advertising requirements. For well-established brands that audiences are familiar with, a wide coverage-oriented bus route selection model is proposed to maximize the coverage of potential target audiences. For new brands that require a high level of exposure before they become recognizable, a deep coverage-oriented bus route selection model is proposed to maximize the total exposure times of the ads. Both models were demonstrated with a case study in Shenzhen, China to explicitly present the outcomes of the models and the differences between them. The calculation results show that the wide coverage-oriented model achieves an average of 84.8% improvement compared with baseline 1 which selects the bus routes with the most passengers, while an average of 9.2% improvement compared with baseline 2 which selects the bus route with the maximum coverage of the target area in reaching more potential target audiences. The exposure intensity of the deep coverage-oriented model is almost 3.7 times of the wide coverage-oriented model. The proposed models provide new options for advertisers to select a suitable advertising strategy according to their needs.

Enhanced Search-and-Rescue Optimization-Enabled Secure Route Planning Scheme for Internet of Drones Environment

The Internet of Drones (IoD) is greatly developed and promotes many civil applications. However, it can still be prone to several security problems which threaten public safety. The issue of security poses further problems upon linking the IoD to the Internet, as its data stream is exposed to attack. For secure communication between drones, an effective route planning scheme with a major intention of accomplishing security is needed. With this aim, this study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy, distance, and degree of trust. The experimental validation of the ESRO-SRP technique is carried out under several aspects, and the results demonstrated the enhancements of the ESRO-SRP model over recent approaches. The ESRO-SRP model has provided an increased packet delivery ratio (PDR) of 86%, whereas the BRUe-IoE, ORP-FANET, UAVe-WSN, and TR-UAV Swarm approaches have accomplished a minimal PDR of 79.60%, 73.60%, 67.60%, and 63.20%, respectively.

A demonstration of sustainable pipeline routing optimization using detailed financial and environmental assessment

The selection of optimal pipeline routes should consider the costs for the design, construction, operation and maintenance of all the routing alternatives. The cost analysis of each alternative route requires substantial geospatial information on slopes and elevations, land use, soil types and other features. In addition to financial costs it is necessary to consider the sustainability aspects, including public safety and environmental impacts, if the pipeline passes near populated areas or if it crosses with natural habitats. This work uses an available computer-aided design tool to automate the routing optimization process. The optimal search follows Simulated Annealing to obtain a set of near-optimal pipeline routes and designs. This optimizer is able to consider a large variety of static geospatial features, conduct detailed hydraulic and cost calculations, and accommodate models for the evaluation of environmental impacts. Cost calculations are in accordance to widely recognized procedures used in pipeline design practice. For the quantification of environmental impacts, this study defines a cost function and proposes a simple implementation using geospatial data from CDDA and the Natura. The above are herein applied on the routing optimization of a crude oil pipeline, to demonstrate how financial and environmental criteria can be considered jointly at the early design phases of a real scale project. The pipeline is over 110 km long. The considered terrain involves areas of different land use, protected areas, cities and other infrastructures, seismic faults and Seveso plants. The data needed for these calculations are obtained from many geospatial data layers. The paper describes in detail the fusion procedures to pretreat the data and facilitate calculations of financial costs and environmental impacts. Results are obtained over a range of control variables to depict the trade-offs between the two criteria and other design options. The obtained results compare favorably with the routing proposed by the pipeline company.

Evaluation of thermodynamic consistency of kinetic parameters in cyclic enzyme-catalyzed reaction networks

It is shown by analysis of a cyclic enzymatic network that the presence of zero net flux/current cycles impose strong thermodynamic constraints on the values of the kinetic parameters. Thermodynamic analysis is carried out for the four-node pyramidal reaction network involving the conversion of 7,8-­dihydrofolate (H2F) and NADPH in the presence of H+ to 5,6,7,8-­tetrahydrofolate (H4F) and NADP+ catalyzed by the universal enzyme dihydrofolate reductase (DHFR). The complete set of empty reaction routes in the DHFR pathway is identified, equations for the thermodynamic constraints imposed on the rate constants of the various elementary steps are written down, and the thermodynamic consistency of the experimentally determined rate constants is evaluated. The visual approach based on the theory of graphs can be employed to test the thermodynamic consistency of kinetic parameters of general cyclic enzyme-catalyzed reaction networks.

The real-time on-demand bus routing problem: The cost of dynamic requests

The real-time on-demand bus routing problem (ODBRP) supports the online routing of buses in a large-scale ride-sharing system. Given are a set of buses with fixed capacity, a set of bus stations and a set of transportation requests, only part of which are known before the planning horizon. A request consists of a set of possible departure and arrival stations, as well as an earliest departure and latest arrival time. The aim is to (1) assign each passenger to a departure and arrival bus station and (2) develop a set of bus routes to fulfill each request within its time window while minimizing the sum of the total waiting time and the total user ride time.Including the possibility for requests to be issued after the start of the planning horizon, i.e., when buses have already started servicing other requests, requires a dynamic re-optimization of a partially executed solution. Compared to the case in which all requests are known beforehand, the solution quality, expressed as the total waiting and user ride time, is expected to decline. This decline in objective function value can be seen as the “cost” of the dynamic requests. In this paper, we introduce the real-time ODBRP as an extension of its static problem variant and present a heuristic to deal with dynamic requests. In addition, an extensive set of experiments allows us to conclude that dynamic requests indeed lead to higher waiting and user ride times, especially for passengers who submit their request at the last minute. Passengers are therefore encouraged, if possible, to send their request well in advance, as this results in lower and more stable service promises, higher customer satisfaction, and higher revenues for the operating on-demand bus company.

Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy

The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic congestion is tolerated. It is an NP problem and is conventionally solved by metaheuristics such as evolutionary algorithms. For the MVRP in a distribution network, we propose an optimal distribution path optimization method that is composed of a distribution sequence search stage and a distribution path search stage that exploits a divide-and-conquer strategy, inspired by the idea of dynamic programming. Several optimization objectives subject to constraints are defined. The search for the optimal solution of the number of distribution vehicles, distribution sequence, and path is implemented by using an improved genetic algorithm (GA), which is characterized by an operation for preprocessing infeasible solutions, an elitist’s strategy, a sequence-related two-point crossover operator, and a reversion mutation operator. The improved GA outperforms the simple GA in terms of total cost, route topology, and route feasibility. The proposed method can help to reduce costs and increase efficiency for logistics and transportation enterprises and can also be used for flow-shop scheduling by manufacturing enterprises.

The morphological structure and influence factors analysis of China's domestic civil aviation freight transport network

Civil aviation freight transport (CAFT) in China plays an important role in social and economic development. However, research on the morphological structure of China's CAFT network and its influence factors remains rare. In this paper, we focus on this topic by using a new data set of China's domestic routes from 2015 to 2019. With the complex network theory, we make an empirical study on the morphological structure of China's domestic CAFT network, and try to explore the nexus between the attributes of the network and the influence factors. The results show that China's domestic CAFT network is in line with the small world network model, which has a low average path length and a high clustering coefficient. The centrality of the network presents a saddle distribution with dense in the east and west, while sparse in the central. The results also indicate that there are many localized networks dominated by a few core airports. A number of regional hub cities exist, which are mainly located in the west of China. The development of China's CAFT highly depends on the civil aviation passenger transport (CAPT) network, and is highly affected by GDP, the industrial structure and the geographical location of cities. Finally, we give several policy suggestions, including developing the freight dedicated airports, establishing independent freight transport network system, vigorously developing core and intermediate hub cities, as well as constructing the regional network.

Finding Risk-Expenses Pareto-Optimal Routes in Ice-Covered Waters

Multi-objective optimization of a vessel route is considered of key importance when creating automatic navigation systems to ensure independent navigation in ice conditions. This is explained by the need to take into account not only the time or fuel expenditures on the route but also the risks. Previously, only a few models for navigation in ice used the multi-objective approach when finding the set of Pareto-optimal solutions. This paper suggests the multi-objective model of ship routing optimization with usage of the ice chart and ship parameters. Risks for a vessel are related to values of ice thickness and ice concentration in regions to travel through, which are specified by the ice chart. In the model, we use the extended version of the wave algorithm to find a set of routes, from which we select solutions of the Pareto-front for the multi-objective problem. The model uses objective functions of route length, maximum ice thickness, and maximum ice concentration. In addition, the travel time calculations are used in the model. Kaj Riska’s model of ship performance in ice is used for calculating travel time; the speed of a vessel is evaluated in each of the graph edges. The computational example provided in the paper is based on the particular ice chart of the Gulf of Finland. The developed method can be easily implemented for assisting a particular ship in independent ice navigation with the presence of a relevant ice chart.

Characterization and optimization of a used cooking oils collection chain – Study case Bogotá, Colombia

To improve used cooking oil (UCO) reclaiming, this work focused on the characterization and optimization of an existing UCO collection chain in Bogotá, Colombia. First, based upon historical data from a major collector, production sites were identified, classified, and available volumes were quantified. Also, collection practices, routes, and frequencies were characterized, and inventory of resources consumed during the process were determined. Afterwards, a computational model of the existing collection chain was constructed and implemented in Python™. In this case, a vehicle routing problem approximation was used by mean of weighted Hamiltonian paths. Weights were defined according to economic and environmental indicators calculated as collection costs and equivalent CO2 emissions, respectively. Routes and traveling times were estimated by correlation of geometrical distances between locations and real-time distances from Google Maps™ routes, identifying a dilation factor of 1.33 and an average speed of 9.6 km/h. Finally, an optimization of the collection routes was made using a genetic algorithm inside an integer search. As a result, it was possible to identify the set of routes that minimize costs and CO2 emissions for the collection scheme. Average collection costs of 0.37 USD/kg UCO and average equivalent CO2 emissions of 117.2 g CO2/kg were obtained.

Optimization of New Energy Public Transportation Network Based on Ant Colony Algorithm and Low-Carbon Concept

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

Feeder routing for air-to-air refueling operations

With the ever increasing volume of air traffic comes an enormous environmental impact, specifically due to carbon emissions. A promising approach to reduce this impact is the introduction of en route air-to-air refueling, allowing for the design of aircraft with reduced weight and a decreased fuel burn. Aside from the design of aircraft, the key challenge lies in the planning of the refueling operation in order to minimize the fuel burn of the feeder fleet. The air-to-air refueling problem is a variant of a vehicle routing problem in which a fleet of feeders are to perform air-to-air refueling operations for a fixed set of cruisers. In this paper, we devise an IP-based method capable of solving this problem supported by an ODE model of the feeders’ fuel consumption. The algorithmic key to solve the air-to-air refueling problem lies in separating the problem of finding routes serving cruisers and assigning sets of routes to individual feeders. We demonstrate the effectiveness of our methods both on real-world and artificial instances, solving all problems to optimality and significantly outperforming state-of-the-art heuristic methods.

DFT modeling approach for operational risk assessment of railway infrastructure

Reliability engineering of railway infrastructure aims to understand failure processes and to improve the efficiency and effectiveness of investments and maintenance planning such that a high quality of service is achieved. While formal methods are widely used to verify the design specifications of safety-critical components in train control, quantitative methods to analyze the service reliability associated with specific system designs are only starting to emerge. In this paper, we strive to advance the use of formal fault-tree modeling for providing a quantitative assessment of the railway infrastructure’s service reliability in the design phase. While, individually, most subsystems required for route-setting and train control are well understood, the system’s reliability to globally provide its designated service capacity is less studied. To this end, we present a framework based on dynamic fault trees that allows to analyze train routability based on train paths projected in the interlocking system. We particularly focus on the dependency of train paths on track-based assets such as switches and crossings, which are particularly prone to failures due to their being subject to weather and heavy wear. By using probabilistic model checking to analyze and verify the reliability of feasible route sets for scheduled train lines, performance metrics for reliability analysis of the system as a whole as well as criticality analysis of individual (sub-)components become available. The approach, which has been previously discussed in our paper at FMICS 2019, is further refined, and additional algorithmic approaches, analysis settings and application scenarios in infrastructure and maintenance planning are discussed.

Modelling the costs of pre-hospital transport service for victims of road accidents in TDABC

The pre-hospital transport of victims of road accidents in Morocco by basic ambulances concerns the majority of the population of victims requiring urgent care. It also constitutes the common service that benefits the entire population of victims, in terms of pre-hospital care. The objective of this contribution is to develop a model for calculating the costs of pre-hospital transport of road accident victims using Time-Driven Activity-Based Costing (TDABC). This model could be effective in better understanding how consumption occurs and how resources are administered and distribut-ed within the pre-hospital care service portfolio. The qualitative research approach that we have adopted in the con-text of this study has enabled us to map and identify all the activities and tasks carried out in the process of pre-hospital transport of road accident victims. The model obtained is flexible enough to adapt to the various situations of pre-hospital transport of road accident victims by basic ambulances. The application of this model in the Moroccan context, delimited in a well-defined round trip (civil protection - accident site - hospital - civil protection) demonstrates that the said model is valid for cost calculation. The calculations made (217.47 MAD, 224.31 MAD, 225.45 MAD and 226.59 MAD) allow us to identify the possible cases of victims transported by basic ambulance. However, the TDABC, applied to the pre-hospital transport of road accident victims, has some limitations, in particular the estimation of time which is the main key to the allocation of the consumed resources. The results show that the TDABC promotes a better knowledge of all the processes related to the pre-hospital transport service. It allows making visible the value of the costs of the victims of road accidents in the selected patient population. Indeed, when the model is used in the set of routes (black points), decision makers are able to see, on the one hand, the variation of costs between similar pre-hospital transport services and different routes for the same transport services and, on the other hand, the value of the costs of each category of road accident victims' population.