Route Selection Problem Research Articles

Self-Driving Tour Travel Route Planning Model Construction Using Recurrent Neural Network

With the continuing increase of people’s living standards, tourism has now become the primary mode of holiday and leisure activity. Scientific and reasonable route planning can best meet people’s essential needs, provide detailed planning in terms of travel time and cost, resolve the impact of various external factors, and realize the complementary benefits of scenic tourism while ensuring that each factor configuration is optimized. External factors such as trip time, cost, travel date, and transport mode all have an impact on route planning. In order to develop a mathematical model and gather other information, it is frequently essential to determine two or more of these influencing elements. The ideal plan for the influencing elements is to first develop an optimal self-driving trip route that meets all of the requirements. This strategy is becoming increasingly popular among the general public, and it focuses on how to spend the least amount of money in order to visit more tourist attractions, construct the most reasonable routes, and provide circumstances for tourists to save time and money. The purpose of this study is to examine the topic of self-driving travel route planning and to suggest the creation of a route planning model. In this paper, a self-driving journey route planning model is built using a recurrent neural network model. The driving route is designed with a high-speed priority strategy, and the travel route selection problem takes into account the consumption condition as well as the quantity of scenic sites. The developed model is used to optimize self-driving trip routes, and the results are promising.

Behavior analysis of travel route selection with consideration of risk aversion

In order to study the effect of travelers' attitudes on route selection behavior when risk aversion is considered in intelligent transportation system, this study provides a methodology about multi-attribute route selection for travelers with different characteristics. A multi-objective decision-making model of route selection with consideration of risk aversion is established by integrating the cumulative prospect theory to describe the traveler's risk aversion attitude and the solution algorithm based on the assignment model. A case in practice verifies the rationality of the model. The result shows that traveler's risk aversion has a significant impact on the route selection result. The result of path selection varies with the traveler's different composite impedance and comprehensive influence weight. The target sensitivity with consideration of risk aversion is stronger than that without consideration of risk aversion, and the consideration of risk aversion is closer to the actual situation of route selection. The proposed travel route selection decision-making model which considers risk avoidance can be used to study the problem of multi-type travelers and multi-attribute route selection in intelligent transportation system, also can be used as the basis for the study of traffic problems such as traffic flow allocation and network design.

Research on Zoning, Optimization, Stability, and Nonlinear Control of Wireless Network in Power Grid Communication

To explore the partition optimization and stability of telecommunication network under nonlinear control, a routing optimization algorithm for power control of distribution communication network is proposed, which is based on the Ad-hoc On-demand Distance Vector (AODV) routing optimization algorithm, based on the energy consumption model of wireless sensor network, the optimal routing mechanism for power control is constructed, and the optimal routing mechanism is used to select the route with low energy consumption to solve the problem of energy limitation of wireless sensor nodes. This paper aims at the network routing selection problem in the terminal information collection of the distribution network and designs the routing optimization algorithm of the wireless sensor network. Experimental results show that the model accuracy of the optimized model is 99.8%, the average residual error of the model is about 5.5[Formula: see text]nm, the model accuracy meets the requirements, and the method is effective. According to the confidence interval prediction results of wireless communication link reliability, a node power control system structure of wireless sensor network is proposed, and an optimal control algorithm of communication link quality and reliability of microgrid wireless sensor network based on fuzzy control is studied.

Selective routing problem with synchronization

We introduce a new optimization problem arising in the management of a multi-object spectrograph in a telescope. The field of view of the spectrograph is divided in 55 contiguous and parallel spatial bands. Each band is associated with two opposite sliding bars that can be positioned to observe an astronomical object. The observation of an object requires the synchronized configuration of the bars in several contiguous bands to form a slitlet in the position of the object. A multi-slit pattern for the bars permits the simultaneous observation of several objects requiring bars of different bands. Each astronomer using this spectrograph propose a list of objects to observe. Since the instrument is quite demanded, the manager of the telescope imposes a time limitation to the astronomers, so they need to select a subset of objects from their lists. This paper describes and solves the selection problem. The problem must also find a sequence of configurations for the bars in each band to maximize the total priority of the selection, subject to some synchronization issues and the time limitation. We show three mathematical formulations. One of them is a set-partitioning model where a master problem manages the synchronization constraints and a subproblem generates columns satisfying the time limitation. We propose two branch-and-price-and-cut algorithms for solving the set-partitioning model, and discuss an extensive computational experiment showing the performance of the algorithms on two families of instances.

Spatiotemporal representation learning for rescue route selection: An optimized regularization based method

Abstract Emergency medical services (EMS) are emergency services that provide urgent pre-hospital treatment for serious illness and injuries. However, in most countries, EMS is faced with the problem of untimely emergency response. In this paper, we develop an Optimized Regularization based framework (OpRe-RRS) by optimizing the Rescue Route Selection problem to increase the rescue speed. Specifically, through the analysis of spatio-temporal data, we predict the ranking of road priority and select the rescue route for ambulances to lift speed. Along this line, we match the GPS data of ambulances to the correct road section through a map matching algorithm. Then, we extract different features from three perspectives: (i) basic features, (ii) POI features and (iii) traffic features. To effectively exploit the roads similarity, we develop a loss function with regularization to solve this prediction problem. Finally, experiments on real-world data demonstrate that our method can effectively reduce rescue time.

Integrated optimization of bus bridging routes and timetables for rail disruptions

A rail disruption may lead to rapid degradation of the public transportation system. In response to rail disruptions, this study focuses on bus bridging service design considering a routing strategy that combines express and short-turn routes. An integrated optimization framework is proposed to jointly optimize the bus bridging routes and timetables under time-varying demand. A brute-force search method is developed to identify all candidate bus bridging routes. A mixed integer linear programming model is then formulated to solve the route selection, bus deployment, and bus timetabling problem simultaneously. To handle the arising computational challenges in solving this large-scale problem, a tabu search-based heuristic with LP rounding is developed. Numerical studies based on Cranbourne Line, Melbourne, demonstrates the applicability of the proposed approach in practice. The results indicate that the proposed framework can generate high-quality bus bridging solutions in a reasonable time, which allows quick response to rail line disruptions. The bus bridging strategy with multiple routes outperforms the standard bridging route in terms of bus line capacity and passenger travel time.

Train routing selection problem: Ant colony optimization versus integer linear programming

The real-time Railway Traffic Management Problem (rtRTMP) is the problem of detecting and solving time-overlapping conflicting request done by multiple trains on the same track resources. It typically consists in taking retiming, reordering or rerouting train actions in such a way that the propagation of disturbances in the railway network is minimized. The rtRTMP is an NP-Hard problem and finding good strategy to simplifying its solution process is paramount to obtain good quality solutions in a short computation. Solving the Train Routing Selection Problem (TRSP) aims to do so, by limiting the number of routing variables through a pre-processing that selects the most promising routing alternatives among the available ones for each train in order to reduce the size of rtRTMP instances. This paper studies the performance of an Ant Colony Optimization (ACO) algorithm for the same problem. An integer linear programming formulation for the TRSP is presented and solved using a commercial software, and it is considered as a benchmark. Computational experiments are performed on two practical case studies of the French railway infrastructure: the line near the city of Rouen and the Lille terminal station area. ACO and the commercial solver perform comparably only on small instances and both are able to find optimal solutions. However, on larger instances, the ACO algorithm outperforms the commercial software, both in terms of computation time and solution quality.

A firefly algorithm for the environmental prize-collecting vehicle routing problem

In the present research, a new variant of the Vehicle Routing Problem (VRP), the Environmental Prize-Collecting Vehicle Routing Problem (E-PCVRP), is introduced. The E-PCVRP is a selective routing problem that focuses on the maximization of the aggregated prize values collected from the visited nodes while minimizing the fixed and variable cost of the formed routes. In terms of variable cost, the CO2 emissions of the vehicles performing the routes are considered as a load-distance function. The presented solution approach is based on the Firefly Algorithm (FA). The FA is an optimization algorithm, designed for the solution of continuous problems, while the proposed E-PCVRP, requires a discrete solution approach. Addressing the above discrepancy, the Firefly Algorithm based on Coordinates (FAC) is introduced, which incorporates the proposed “Coordinates Related” (CR) encoding/decoding process in the original FA scheme. The CR is a novel process that allows for algorithms designed for continuous optimization to by employed in the solution of discrete problems, such as the VRP. Specifically, the CR utilizes auxiliary vectors for solution representation, containing the Cartesian coordinates of each node, that allows for the original movement equation of the FA to be applied directly. The effectiveness of the FAC algorithm is showed over computational experiments and statistical analysis, in comparison to the performance of other bio-inspired algorithms and a mathematical solver. • The Environmental Prize-Collecting Vehicle Routing Problem is presented. • The objective function considers prize maximization and CO2 emissions minimization. • As the basis of the solution approach, the Firefly Algorithm has been chosen. • The novel CR encoding/decoding process is proposed and incorporated into FA's scheme. • Statistical analysis proves the significance of results obtained by the proposed FAC.

A throughput-aware joint vehicle route and access network selection approach based on SMDP

In intelligent transportation system (ITS), the interworking of vehicular networks (VN) and cellular networks (CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading (MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process (SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio. Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

PDAU: a new exploitation of routing strategy in adhoc network

Generally, routing is the process of selecting a path for traffic in a network, or across multiple networks. The routing strategy usually struck with the issue of shortest path routing (SPR). A number of routing models is already in progress but, still the challenges rise with some real-world applications. This routing strategy impresses to focus on solving these issues by considering delay, energy and total fitness. This paper introduces a new routing algorithm for solving the issues faced in route establishment and route recovery as well. This paper also uses neural network (NN) model to predict the node movements in adhoc network. More importantly, the major optimal route selection problem is solved here by introducing a new hybrid algorithm. The proposed model, probability-based dragonfly (DA) with artificial bee colony (ABC) update (PDAU) compares its performance over other conventional methods in terms of delay, energy and total fitness.

SCAROS: A Scalable and Robust Self-Backhauling Solution for Highly Dynamic Millimeter-Wave Networks

Millimeter-wave (mmWave) backhauling is key to ultra-dense deployments in beyond-5G networks because providing every base station with a dedicated fiber-optic backhaul link to the core network is technically too complicated and economically too costly. Self-backhauling allows the operators to provide fiber connectivity only to a small subset of base stations (Fiber-BSs), whereas the rest of the base stations reach the core network via a (multi-hop) wireless link towards the Fiber-BS. Although a very attractive architecture, self-backhauling is proven to be an NP-hard route selection and resource allocation problem. The existing self-backhauling solutions lack practicality because: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> they require solving a fairly complex combinatorial problem every time there is a change in the network (e.g., channel fluctuations), or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> they ignore the impact of network dynamics which are inherent to mobile networks. In this article, we propose SCAROS which is a semi-distributed learning algorithm that aims at minimizing the end-to-end latency as well as enhancing the robustness against network dynamics including load imbalance, channel variations, and link failures. We benchmark SCAROS against state-of-the-art approaches under a real-world deployment scenario in Manhattan and using realistic beam patterns obtained from off-the-shelf mmWave devices. The evaluation demonstrates that SCAROS achieves the lowest latency, at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> higher throughput, and the highest flexibility against variability or link failures in the system.

The selective minimum latency problem under travel time variability: An application to post-disaster assessment operations

In this paper, we consider a new selective routing problem, where a subset of customers should be serviced by a limited fleet of vehicles with the aim of minimizing the total latency. A service level constraint is added to guarantee that a minimum system performance is achieved. Assuming that the travel times are uncertain, we address the problem through a mean-risk approach. The inclusion of risk in the objective function makes the problem computationally challenging. To solve it, we propose an efficient heuristic, relying on a variable neighbourhood search mechanism, able to strike the balance between service level and latency. A detailed discussion of the model, which includes simulation tests and a sensitivity analysis, is carried out to illustrate the applicability of our approach in a post-disaster scenario, taking as a case study the Haiti earthquake in 2010. Additional computational experiments show that the proposed heuristic is effective for this difficult problem and often matches optimal solutions for small and medium-scale benchmark instances.

A fuzzy evidential reasoning based approach for submarine power cable routing selection for offshore wind farms

Offshore wind farms have developed fast as an environmentally friendly source of energy. The submarine power cable of the offshore wind farm, used for connecting power generation devices to onshore equipment, may have a significant impact on navigation safety and is prone to being damaged (e.g. caused by fishing or emergency anchoring by ships) when adopting unfavorable routing. This paper proposes a fuzzy evidential reasoning method for submarine power cable routing selection of the offshore wind farm by comprehensively considering the conditions for cable laying and its influence on maritime safety. The kernel of this approach is to establish a three-layer decision-making framework after fuzzification of the input variables, to derive the belief rule base, and to obtain the optimal routing from the submarine power cable candidates using evidential reasoning and index value. The proposed approach is applied to a real routing selection problem of a submarine power cable for an offshore wind farm in Zhejiang Province of China. The resulting choice corresponds to the discussions in a workshop unanimously.

Optimizing Multimodal Transportation Routes Considering Container Use

This study focuses on the route selection problem of multimodal transportation: When facing a shortage of containers, a transport plan must be designed for freight forwarders that realizes the optimal balance between transportation time and transportation cost. This problem is complicated by two important characteristics: (1) The use of containers is related to transport routes, and they interact with each other; and (2) Different types of containers should be used in different time ranges for different modes of transportation. To solve this problem, we establish a multi-objective optimization model for minimizing the total transportation time, transportation cost and container usage cost. To solve the multi-objective programming model, the normalized normal constraint method (NNCM) is used to obtain Pareto solutions. We conducted a case study considering the transportation of iron ore in Panzhihua City, Sichuan Province. The results demonstrate that using railway containers and railway transportation as much as possible in route selection can effectively solve the problem of container shortage and balance transportation time and transportation cost.

Multi-Armed Bandit On-Time Arrival Algorithms for Sequential Reliable Route Selection under Uncertainty

Traditionally vehicles act only as servers in transporting passengers and goods. With increasing sensor equipment in vehicles, including automated vehicles, there is a need to test algorithms that consider the dual role of vehicles as both servers and sensors. The paper formulates a sequential route selection problem as a shortest path problem with on-time arrival reliability under a multi-armed bandit setting, a type of reinforcement learning model. A decision-maker has to make a finite set of decisions sequentially on departure time and path between a fixed origin-destination pair such that on-time reliability is maximized while travel time is minimized. The upper confidence bound algorithm is extended to handle this problem. Several tests are conducted. First, simulated data successfully verifies the method, then a real-data scenario is constructed of a hotel shuttle service from midtown Manhattan in New York City providing hourly access to John F. Kennedy International Airport. Results suggest that route selection with multi-armed bandit learning algorithms can be effective but neglecting passenger scheduling constraints can have negative effects on on-time arrival reliability by as much as 4.8% and combined reliability and travel time by 66.1%.

Designing Robust Air Transportation Networks via Minimizing Total Effective Resistance

Designing a robust air transportation network is an ongoing research effort that seeks to improve the extent of a network being connected against failures and attacks. The total effective resistance can be a promising measure for network robustness as demonstrated by case studies conducted in this paper. To enhance the robustness of air transportation networks, we consider to solve a flight route selection problem in which a set of routes is chosen from a candidate route set to minimize the utility function defined by the total effective resistance. Since it is an integer nonlinear programming problem, to balance the tradeoff between optimality performance and computational efficiency, two methods that implement the total effective resistance are developed to suit different network scales. For small/medium-scale networks, we develop an interior-point method based on convex relaxation and duality gap, which achieves a near optimality performance within acceptable time. For large-scale networks, we develop an accelerated greedy algorithm based on proved monotone submodularity, which can substantially reduce the computation time in deriving a good solution with a guaranteed optimality gap. Their optimality performance and computational efficiency are verified and compared through numerical results. Moreover, three case studies from real-world examples are also performed to demonstrate the application of the proposed methods for different network scales.

Optimal ice routing of a ship with icebreaker assistance

Offshore development and growing prospects of commercial shipping in the Arctic pose the challenge of optimal ship routing in ice. Route selection in spatially distributed ice conditions significantly affects the voyage time and determines the efficiency of shipping. Most of the applied methods of ice routing solve the problem of a single vessel route selection without considering icebreaker support. At the same time, the real practice of ice navigation is closely connected with icebreaker assistance. It allows reducing the voyage time and fuel consumption, while having additional costs for icebreaker services. Such opposite trends set an optimization task that has not been studied in detail before. In this article, we presented the formulation of a Single Vessel and Icebreaker Assisted Ice Routing optimization problem in non-stationary ice conditions. We considered the icebreaker assistance as an integral part of the overall route optimization problem, and used the economic criterion to optimize both ship route and amount of icebreaker involvement. The article contains the adapted mathematical formulations of classical graph-based and wave-based routing problems in order to consider icebreaker assistance. To prove the practical applicability of these formulations, we developed special subject-oriented research software and implemented there both graph-based (Dijkstra, A*) and the wave-based ice routing methods. Using this developments, we conducted several case studies and made the analysis of strengthens and weaknesses of the alternative routing methods in case of ice operation. The results of the study may serve an additional step to the practical implementation of ice routing technologies and planning of icebreaker resources.

Auction-based approach for a flexible job-shop scheduling problem with multiple process plans

ABSTRACTThis study addresses the flexible job-shop scheduling problem with multiple process plans with the objective of minimizing the overall makespan. A nonlinear programming model is formulated to allocate machines and schedule jobs. An auction-based approach is proposed to address the integrated production route selection and resource allocation problem and focus on improving resource utilization and productive efficiency to reduce the makespan. The approach consists of an auction for process plans and an auction for machines. The auctions are evaluated to select a more suitable route for production and allocate resources to a more desirable job. Numerical experiments are conducted by testing new large benchmark instances. A comparison of Lingo and other existing algorithms demonstrates the effectiveness and stability of the proposed auction-based approach. Furthermore, SPSS is used to prove that the proposed method exhibits an absolute advantage, particularly for medium-scale or large-scale instances.

Different Forms of Triangular Neutrosophic Numbers, De-Neutrosophication Techniques, and their Applications

In this paper, we introduce the concept of neutrosophic number from different viewpoints. We define different types of linear and non-linear generalized triangular neutrosophic numbers which are very important for uncertainty theory. We introduced the de-neutrosophication concept for neutrosophic number for triangular neutrosophic numbers. This concept helps us to convert a neutrosophic number into a crisp number. The concepts are followed by two application, namely in imprecise project evaluation review technique and route selection problem.

Near Optimal Route Association With Shannon Model in Multi-Drone WSNs

In this paper, we develop a wireless data gathering model for a multi-drone system in traditional wireless sensor networks (WSNs), where each drone serves as a data collector in the extremely large and densely deployed area. Previous solutions usually consider how to schedule the traveling route among nodes, but fail to optimize the data transmission time. We focus on extending the data collection issue over Shannon model which involves the factors such as the transmission bandwidth and the SNR between a drone and a sensor node. A novel and typical system model is formulated and we investigate the corresponding Route Selection and Communication Association (RSCA) problem, that is, given a set of candidate flight routes and a fixed number of deployed nodes, we determine which routes should be selected for traveling and which nodes should be associated such that the overall energy consumption for data gathering could be minimized . We prove the RSCA problem is NP-hard by reduction from the Vertex Cover problem and then devise an efficient and accessible $O(\log \log n)$ approximation algorithm within the time complexity bound by $O(p^{2}\log n\log \log n)$ , where $n$ is the number of sensor nodes and $p$ is the number of routes in WSNs. Extensive simulations are carried out to investigate the performance of our designed algorithm by comparing with the brute-force and random methods. The proposed algorithm achieves 54% more energy consumption at most and 45% more on average comparing with the optimal solution. Furthermore, real-world trace-driven evaluations have been conducted to show that our obtained solution would hold 34% more at most and 25% more on average comparing with the optimal energy consumption and eventually validate our algorithm.