Set Of Routes Research Articles

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with Routing Planning for 6G-enabled UAV communication (EADCRP-6G) technique. The goal of the proposed EADCRP-6G technique is to conduct energy-efficient cluster-based data collection and optimal route planning for 6G-enabled UAV networks. EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) and organize these clusters. Besides, Artificial Fish Swarm-based Route Planning (AFSRP) technique is applied to choose an optimum set of routes for UAV communication in 6G networks. In order to validated whether the proposed EADCRP-6G technique enhances the performance, a series of simulations was performed and the outcomes were investigated under different dimensions. The experimental results showcase that the proposed model outperformed all other existing models under different evaluation parameters.

Decomposition of the vehicle routing problem with time windows on the time dimension

The Vehicle Routing Problem with Time Windows (VRPTW) asks for the optimal set of routes to be performed by a fleet of vehicles to serve a set of customers within their assigned time windows. In this work, we propose a matheuristic for the VRPTW which utilizes the sub-problem constituted by optimizing only a selected time window of the VRPTW whereas the tours outside of this time window are regarded as fixed. We call this problem the Single Time Window Vehicle Routing Problem (STWVRP). For applying the STWVRP, we must assume that several customers are assigned to the same time window, i.e., the number of time windows is much smaller than the number of customers. An exact problem description of the STWVRP is given in the form of a Mixed-Integer Linear Programming formulation. We apply this exact formulation within a matheuristic for the VRPTW. The paper concludes with extensive computational experiments.

Various Routing Protocols of Vehicular Ad hoc Network: A Survey

Abstract: A vehicular ad hoc network is a type of network divided by an area where car nodes can join or leave the network. Due to the flexibility of the network environment active contractual routes are used in the development of the route from the source to the destination. Various active and active protocols are updated in this paper. Active routing protocols are those that use current network information in the development of the route from the source to the destination. Active routing protocols are those that use the pre-defined network information in the route setting. Active route agreements use route tables in route development. In this review paper, a literature survey was conducted on VANET route protocols. It is analyzed that an effective route protocol offers higher performance compared to active router protocols. Keywords: VANET, Reactive, Proactive Routing

SWEEP ALGORITHM IN CVRP TO OPTIMIZE DELIVERY AUTOMOTIVE SPARE PART

The CVRP to solve many optimization and application problems distribution especially of the automotive spare parts industry, West Java, Indonesia is presented has 120 customers who aim to optimize and maximize the capacity and cost of vehicles owned, namely 10 colts 6 diesel type vehicles. Vehicle routes are compared against the existing daily distribution routes showing that significant savings can be gained on the daily cost of transportation-related expenses. The objective of this research is to find a set of optimal routes that minimize the number of vehicles required and total distance traveled for all vehicles to serve customers with a result from 10 to 9 using vehicles with a total distance increase from the Sweep method of 253.8347 km and cost benefits or cost savings with a total usage cost of Rp 17.800.000 to be Rp 17,709,503 in cost efficiency of 4.47% or Rp 90,496.58 a day.

Strong cuts from compatibility relations for the Dial-a-Ride Problem

In this paper we study the Dial-a-Ride Problem, which is a variant of the well-studied Vehicle Routing Problem, where a fleet of vehicles has to satisfy a set of transportation requests between given pickup and delivery locations, and the solution is a set of routes satisfying several constraints, and minimizing the transportation costs. Our main goal is to investigate the classes of valid inequalities that can be obtained from the compatibility relation on the arcs. The compatibility relations are derived from the constraints of the problem, such as precedence relations, time windows and the capacity of the vehicles, and their main use is to exclude infeasible vehicle paths. We strengthen the lifting for an existing class of cuts, introduce a new family of valid inequalities, and devise a new exact separation algorithm for all these cuts. We also establish a connection between these cuts and the edge-polytope of a bipartite graph, which gives new insight into the strength of known classes of inequalities as well. Finally, we evaluate the various classes of cuts on benchmark instances from the literature.

Personalized Route Planning System Based on Driver Preference.

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

A column generation and Combinatorial Benders Decomposition algorithm for the Selective Dial-A-Ride-Problem

In Pickup-and-Delivery Problems (PDP), one must design a set of vehicle routes that visit matching pickup and delivery locations. The Selective Dial-A-Ride Problem (SDARP) is a PDP which aims to serve as many pickup–delivery pairs as possible, where each pair has a time window and a maximum ride time. This paper introduces Extended Fragments to solve the SDARP. Extended Fragments stem from a subclass of vehicle routes which can be directly enumerated. An Extended Fragment formulation for the SDARP is proposed and solved using Combinatorial Benders Decomposition, augmented by a time discretisation and a novel variable-fixing technique. This algorithm, along with other recent ‘fragment’-based methods for PDP, is an a priori column generation method, combined with Combinatorial Benders Decomposition. The new method solves the existing benchmark for the SDARP, including 11 previously-unsolved instances.

Prognostics-aware multi-robot route planning to extend the lifetime

Transformation to Industry 4.0, manufacturing systems need more intelligent devices with capable of self-awareness. Prognostic-aware robotic systems are one of key components for the self-awareness in manufacturing. The prognostics-aware route planning is one of the key components for the success of the multi-robot team during the long-term and uninterrupted operations with also extending lifetime and reducing maintenances costs. In this study, a Prognostics-aware Multi-Robot Route Planning (P-MRRP) algorithm is proposed for extending lifetime of the robot team. In the P-MRRP algorithm, firstly routes are obtained from route set construction algorithm and most reliable route set is selected by calculating Probability of Route Completion (PoRC) according to reliability of the robot team. The proposed algorithm also considers effect of load during the route of robots. In this study, the reliability of the robot is updated considering both the travelled distances with route of robot and the load of robot between pickup and/or delivery nodes. The results of P-MRRP algorithm are compared with the results of classical MRRP. The performance of the algorithm shows that the lifetime of mobile robot team can be extended by using the P-MRRP algorithm.

Optimization of resource flows across the whole supply chain. Application to a case study in the dairy industry

This paper deals with the problem of optimally planning the flows of raw materials and products across a linear multiproduct supply chain. Given a known series of products demands along a planning period, the problem consists of optimizing procurement, inventory, production, and distribution decisions for each time-period of the planning horizon. A decomposition procedure is developed to solve the optimization problem in reasonable computational times. The proposal first generates both a set of pickup routes for raw material collection and a set of delivery routes for product distribution through the Column Generation method. Then, these routes are feed into a Mixed-Integer Linear Program modelling the integrated problem. The scope of the optimization approach covers the entire supply chain from sources of raw materials to the end-customers, providing a useful computational tool for assessment of linear supply chains. The aim is to maximize the profit of the company that fabricates and distributes the products. The usefulness and effectiveness of the proposed solution strategy is demonstrated by solving an extensive set of realistic instances for a dairy industry.

A metaheuristic for crew scheduling in a pickup‐and‐delivery problem with time windows

AbstractA simultaneous vehicle routing and crew scheduling problem (SVRCSP) consists of planning routes for a fleet of vehicles and scheduling their crews, with the particularity that the vehicle–crew correspondence is not fixed through time. This allows a greater planning flexibility and a more efficient use of the fleet, but in counterpart it requires high synchronization. In this work, an SVRCSP is presented, where long‐distance pickup‐and‐delivery requests must be fulfilled over a multiday planning horizon, subject to several constraints such as multiple time windows, hour of services regulation, among others. Crews can be composed of one or two drivers and any of them can be relieved in a given set of locations. Also, they are allowed to travel between locations with noncompany shuttles. The objective is to minimize the cost of travel in company and noncompany vehicles, which depends on the distance, and the penalization for completing requests with delay. A two‐stage sequential approach is applied: a set of truck routes is computed in the first stage and a set of driver routes consistent with the previous routes is obtained in the second stage. An algorithm based on the GRASP × ILS metaheuristic, embedded with a repair heuristic to facilitate the construction of initial solutions, is proposed and evaluated for the latter stage. High‐quality solutions were found for instances generated with up to 3000 requests and a planning horizon of one to four weeks spread over 15 Argentine cities in less than an hour. Additionally, the possibility of carrying an additional driver reduced the cost of external shuttles by 2.25 times on average compared to individual crews and, in some cases, removed this cost completely.

On the Implications of Routing Models on Network Optimization

In network optimization problems, from traffic engineering to network monitoring, the routing model is typically considered as something given and frozen. This paper is motivated by the fundamental question how the ability to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">change</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimize</i> the routing model itself influences the efficiency at which communication networks can be operated. To this end, we identify two main dimensions of a routing model: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistency</i> (of a single route) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coherence</i> (of sets of routes). We present analytical results on the impact of the routing model on the achievable route diversity as well as on the runtime of solving optimization problems underlying different case studies. We also uncover that it can sometimes be beneficial to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">artificially</i> restrict the routing model, to significantly reduce the computational complexity without negatively affecting the route diversity much.

Fuzzy Stochastic Capacitated Vehicle Routing Problem and Its Applications

This paper considers Capacitated Vehicle Routing Problem (CVRP) in an imprecise and random environment. The deterministic version of the problem deals with finding a set of routes in such a way that the demand of all the customers present in the network are satisfied and the cost incurred in performing these operations comes out to be a minimum. In practical life situations, problems are not always defined in crisp form. Phenomena like randomness and impreciseness are quite natural to arise in real life. This work presents CVRP in such a mixed environment, such type of CVRP may be called as Fuzzy Stochastic Capacitated Vehicle Routing Problem. In this work, the demands of the customers are assumed to be stochastic and are revealed only when a vehicle arrives at the customer location. Moreover, the edge weights represent time required to traverse the edge and hence are both imprecise and random in nature. Factors like traffic conditions, weather conditions, are responsible for the random nature of the edge weights and the varying speed of the vehicle is responsible for impreciseness. Thus, the work presents CVRP with stochastic demands and stochastic and imprecise travel times. In this paper, an expectation-based approach has been used to deal with the randomness of edge weights. A two-stage model is used to solve the problem where the first stage corresponds to finding an optimal tour and recourse actions are planned in the second stage. A procedure based on Branch and Bound algorithm has been used to find minimum cost route. A small numerical example is presented to explain the working of the method proposed and the proposed solution approach is further tested on modified fuzzy versions of some benchmarks datasets.

Consistent iterative algorithm for stochastic dynamic traffic assignment with a stable route set

AbstractIn stochastic (dynamic) traffic assignment (DTA), route choice (RC) formulations with implicit route set require less memory than their explicit counterparts. To compute turn probabilities, however, a topological ordering of the nodes is needed, which negatively impacts the stability of convergence and consistency of equilibrium. This paper formulates a general framework for the RC module of the efficient stochastic DTA (ESDTA) without topological ordering; we demonstrate how its full fixed route yields smooth and consistent convergence. The theoretical framework requires a series of computational modifications to ensure that numerical errors remain limited and to make the formulation more efficient for use in practice. The resulting ESDTA's computation time to reach equilibrium scales linearly with respect to all relevant complexity dimensions (network size, demand zones, time resolution, and congestion level).

Travel Time Reliability-Based Rescue Resource Scheduling for Accidents Concerning Transport of Dangerous Goods by Rail

The characteristics of railway dangerous goods accidents are very complex. The rescue of railway dangerous goods accidents should consider the timeliness of rescue, the uncertainty of traffic environment and the diversity of rescue resources. Thus, the purpose of this paper is to confront the rescue resources scheduling problem of railway dangerous goods accident by considering factors such as rescue capacity, rescue demand and response time. Based on the analysis of travel time and reliability for rescue route, a multi-objective scheduling model of rescue resources based on travel time reliability is constructed in order to minimize the total arrival time of rescue resources and to maximize total reliability. The proposed model is more reliable than the traditional model due to the consideration of travel time reliability of rescue routes. Moreover, a two-stage algorithm is designed to solve this problem. A multi-path algorithm with bound constraints is used to obtain the set of feasible rescue routes in the first stage, and the NSGA-II algorithm is used to determine the scheduling of rescue resources for each rescue center. Finally, the two-stage algorithm is tested on a regional road network, and the results show that the designed two-stage algorithm is valid for solving the rescue resource scheduling problem of dangerous goods accidents and is able to obtain the rescue resource scheduling scheme in a short period of time.

Genotoxicity Assessment of Metal-Based Nanocomposites Applied in Drug Delivery.

Nanocomposites as drug delivery systems (e.g., metal nanoparticles) are being exploited for several applications in the biomedical field, from therapeutics to diagnostics. Green nanocomposites stand for nanoparticles of biocompatible, biodegradable and non-toxic profiles. When using metal nanoparticles for drug delivery, the question of how hazardous these “virus-sized particles” can be is posed, due to their nanometer size range with enhanced reactivity compared to their respective bulk counterparts. These structures exhibit a high risk of being internalized by cells and interacting with the genetic material, with the possibility of inducing DNA damage. The Comet Assay, or Single-Cell Gel Electrophoresis (SCGE), stands out for its capacity to detect DNA strand breaks in eukaryotic cells. It has huge potential in the genotoxicity assessment of nanoparticles and respective cells’ interactions. In this review, the Comet assay is described, discussing several examples of its application in the genotoxicity evaluation of nanoparticles commonly administered in a set of routes (oral, skin, inhaled, ocular and parenteral administration). In the nanoparticles boom era, where guidelines for their evaluation are still very limited, it is urgent to ensure their safety, alongside their quality and efficacy. Comet assay or SCGE can be considered an essential tool and a reliable source to achieve a better nanotoxicology assessment of metal nanoparticles used in drug delivery.

Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model’s objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers’ facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time.

Using Neural Networks for Bicycle Route Planning

This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to design the routes on the novel area. We created two NNs capable of choosing the best route among the given road network by training them on two different areas. The bicycle routes produced by NNs were the same at best and had 75% overlap at the worst compared to those produced by human experts. Furthermore, the mean square error for all of our NN models varied from 0.015 and 0.081. We compared this new approach to the traditional multicriteria GIS (geographic information system) analysis (MA) that requires the human expert to define the bicycle route selection criteria. The benefit of using NN over the MA was that the NN directly transfers the human expertise to a model. In contrast, the MA needs the expert to select multiple criteria and adjust their weights carefully.

Optimization Approach to Data-Driven Air Traffic Flow Management

An integer programming model for data-driven air traffic flow management that extends the state-of-the-art approach of Bertsimas and Patterson was developed. The major contribution of our model is that the structure of the route set is modified to improve the flexibility of rerouting operations. To improve the computational performance, we employed reformulation-linearization techniques to turn the model into a linear integer programming model, and we propose two sets of valid inequalities to speed up the computation process. The computational results indicated that the optimal strategies for flights can be efficiently obtained for large-scale instances.

Preservation of shared backup path ink‐node content based elastic optical datacenter networks

AbstractEfficient path selection and content availability in the optical datacenter networks are essential for business continuity. To measure quantitatively the connectivity of content and give protection to diverse type of contents, the idea ofk‐node content connectivity is becoming popular. However, the assignment of dedicated spectrum for each end‐to‐end content path consumes too many resources. In order to enhance the resource efficiency, the spectrum can be shared between end‐to‐content paths of various requests. The spectrum sharing minimizes the backup spectrum requirement and has been a topic of research. In this study, an innovative model, namely, end‐to‐end content connectivity based quasi‐bipartite graphs (E‐QBG) for the possible path prediction and maximum matching by Hungarian path prediction (MM‐HPP) are implemented for the spectrum efficiency and the disaster resilient datacenter networks. The objective is to minimize the entire backup spectrum resource and operations path. The quasi‐bipartite graph is used for the end‐to‐end content backup route evaluation including spectrum sharing on various links, while the Hungarian algorithm is utilized for accurate maximum matching prediction between various end‐to‐end route sets. We introduce the concept of maximum dissortativity path based on the weight factor. The proposed E‐QBG and MM‐HPP reduces spectrum consumption which guarantees survivability against multi‐failure and natural disasters. This method leads to an approach for more effective utilization of the resources for end‐to‐end content connected elastic optical datacenter networks as compared with the existing techniques.

A Multi-Objective Meta-Heuristic Approach to Improve the Bus Transit Network: A Case Study of Fargo-Moorhead Area

In this study, the Transit Network Design Problem (TNDP) is studied to determine the set of routes and frequency on each route for public transportation systems. To ensure the important concerns of planners like route length, route configuration, demand satisfaction, and attractiveness of the transit routes, the TNDP is solved to generate a set of routes by proposing an initial route set generation (IRSG) procedure embedded into the NSGA-II algorithm. The proposed IRSG algorithm aims to produce high-quality initial route set solutions to reach better optimization procedures. Moreover, the Multi-Objective Mixed-Integer Non-Linear Programming (MOMINLP) model is proposed to formulate the frequency setting problem on each route by minimizing the total travel time of passengers (user costs) and operator costs simultaneously, while maximizing the service coverage area near all the bus stops. The MOMINLP model is solved by applying the NSGA-II algorithm to produce a Pareto front between the first and the second objective functions. The model was applied to the Fargo-Moorhead Area (FMA), a small urban area. Results were compared with the existing transit network to measure the efficiency of the NSGA-II solution methodology. The proposed algorithm was found to considerably decrease the total travel time of passengers.