Routing Scheduling Research Articles

Evolution of Perimeter Routing Through Genetic Optimization in Wireless Networks

The continuously advancements in WSN demands its more usage in a variety of applications. We proposed a genetic optimization of non-linear type to give better response in the area of energy consumption and network lifetime. The scenario which is shown in this paper have try on exhaustive improvement in the different working stages of a network such as node positioning, coverage area, clustered data and aggregation output among regions. Our aim is to apply genetic approach under perimeter routing on network simulator environment, to get a specified fitness function, optimized and to personalise the network at all working scenario. Our focus is also to built the updated routing schedule with consideration of energy saving and to balance the energy factor in the whole network. The aim is fulfilled by using the innovative technique of crossover and mutation operators. So, we analyze that by introducing the genetic approach to WSN network quality improves in terms of functional parameters of the network. This proposed dynamic genetic optimization approach enhances the flexibility criteria and improvement under sudden changing conditions in the environment. It will also help to minimize the ratio of dead nodes in a frequent manner. Keywords: wireless sensor networks, perimeter routing, optimization technique, genetic approach, energy consumption.

An Application on Mobile Devices with Android and IOS Operating Systems Using Google Maps APIs for the Traveling Salesman Problem

ABSTRACTNowadays, the Traveling Salesman Problem (TSP) is one of the most studied combinational optimization problems that researchers study. Although it is easy to define, its solution is hard. Therefore, it is one of the NP-hard problems in the research literature. It can be used to solve real-life problems such as route planning and scheduling, and transportation and logistics applications. In this study, for TSP, an interface that can run on mobile devices using Android and IOS operating systems is developed. Real-world data are used online by the interface. Locations, and the distance between them, are obtained instantly by Google Maps APIs. Genetic (GA) and ant colony optimization (ACO) algorithms are used to solve the TSP. Furthermore, users have also been allowed to conduct trials for different parameter values. The application developed has been tested on two different datasets. The test results show that for the determination of the optimum route, the ACO algorithm is better than the GA. However, when considering the run times, GA works much faster than ACO.

A Strategy of CDN Traffic Optimization Based on the Technology of SDN

Modern Content Delivery Network(CDN)[1,2] by caching and scheduling strategy to reduce user-perceived latency, because CDN can schedule user to the CDN cache node which closest to. At the same time, this strategy can reduce the data flow which has to be sent back to the source station, and decrease the calculation pressure of source station. For some content which is dynamic and cannot be cached, the current acceleration approach is to create a GRE tunnel between the CDN nodes, using the static route scheduling dynamic data flow, improve the speed of data flow which back to the source station. It has been a recent trend of CDN to separate the control flow and data flow through technical, in order to change traditional dynamic/static content acceleration model, to optimize the CDN traffic. The core idea of Software Defined Network(SDN)[3,4]is through the network control plane and data plane separation equipment, so as to realize the flexible control of network traffic. SDN technology is used to transform the CDN, Make the CDN have an intelligent traffic scheduling environment which can focus on monitoring network and scheduling flow path real-time According to the different business. Ultimate aim is to avoid local congestion, optimize the CDN traffic, and improve the quality of the CDN service effect.

An artificial intelligent approach to the bicycle repositioning problems

PurposeMany bike rental organizations permit customers to pick-up bikes from one bike station and return them at a different one. However, this service may result in bike imbalance, as bikes may accumulate in stations with low demand. To overcome the imbalance problem, this paper aims to develop a decision model to minimize the total costs of unmet demand and empty bike transport by determining bike fleet size, deployments and the vehicle routing schedule for bike transports.Design/methodology/approachThis paper developed a constrained mixed-integer programming model to deal with this bike imbalance problem. The proposed model belongs to the non-deterministic polynomial-time (NP)-hard problem. This paper developed a two-phase heuristic approach to solve the model. In Phase 1, the approach determines fleet size, deployment level and the number of satisfied demands. In Phase 2, the approach determines the routing schedule for bike transfers.FindingsComputational results show the following results that the proposed approach performs better than General Algebraic Modeling System (GAMS) in terms of solution quality, regardless of problem size. The objective values and the fleet size of rental bikes allocated increase as the number of rental stations increases. The cost of transportation is not directly proportional to the number of bike stations.Originality/valueThe authors provide an integrated model to simultaneously deal with the problems of fleet sizing, empty-resource repositioning and vehicle routing for bike transfer in multiple-station systems, and they also present an algorithm that can be applied to large-scale problems which cannot be solved by the well-known commercial software, GAMS/CPLEX.

Distributed scheduling approach for dynamic evacuation networks

ABSTRACTThe aim of this paper is to propose a novel distributed scheduling model for evacuation route scheduling from buildings and out of an affected area. The model is based on a hybrid approach that is spatio-temporal algorithm with optimization models as subproblems. The proposed distributed scheduling approach is an iterative process optimizing the one-step arrival of objects to each intermediate destination nodes at a time. To illustrate such an approach, we consider the problem of finding and scheduling evacuation paths from an urban building and out of a predetermined neighbourhood of the building on foot; the evacuation route involves pathways such as corridors, and stairs in buildings and road networks and sidewalks outside the building, there is a predefined set of exit points out of the target building and out of the road network serving the building. A well-known efficient heuristic algorithm is selected as a reference for comparative analysis and to illustrate the outperformance of the proposed approach in large-scale scenarios. The key results are the step-based optimal route schedules and the competitive evacuation time provided by the proposed distributed scheduling approach.

АТЫРАУ ОБЛЫСЫНДА ЭКОТУРИЗМДІ ДАМЫТУДАҒЫ «АҚЖАЙыҚ » РЕЗЕРВАТЫНЫҢ МАҢЫЗЫ

This article considers the importance of the Akzhayik reserve for the development of ecotourism in the Atyrau region. The concept of a reserve and the geographical description of the natural reserve Akzhayik are given in this article. The possibilities of ecotourism route development on the territory of the Akzhayik reserve were studied and the route schedule is attached. Excursions will be organized through drawing routes. Routes are being organized on foot, by road and by water. You can organize a tour to the flora and fauna of the Akzhayik reserve. The tourist ecoroutes of the State Natural Reserve “Akzhaiyk” allow us to get closer acquaintance with its flora and fauna and the ecosystem of our region. At the same time, one of the tasks set for the SNR “Akzhaiyk» is to involve the local population in the development of tourism in the domestic tourism, to organize joint routes with them, to invite tourists. Ecotourism development and the protection of the nature of its territory contribute to the improvement of the living conditions of the local population. Different views of Russian and foreign scientists are presented to study the importance of the Akzhayik reserve in the development of ecotourism in the Atyrau region using the methods of M.I. Krivosheev and V.Zorin (1979).

New approach in definition of multi-stop flight routes

Optimization and profitability approaches play a crucial and central role in airline industry The main problem is how to overcome complexity by providing effective route schedule with minimal empty seats. So we need capable tools to re-optimize existing flight routes or to offer new one instead. This research deals about the efficient heuristic algorithm for optimal transportation of N different passenger contingents between ending points. We want to find out better transport plan with minimal transport cost for the route with more charging/discharging points (airports). Such optimization tool can help in sizing of appropriate airplane for definite direction, too.

A Central Navigation of the Autonomous Vehicles in Zone-based Tessellations with Target Velocities

The autonomous are more adaptable to traffic congestion if they join with each other in propagating the situation of traffic around, and have a potential to avoid the traffic jam if they all drive along scheduled routes given from one centralized navigation system. This work presents a central navigation for autonomous travel through a number of tessellated zones. Routes are scheduled every area, and they could be partially altered by traffic situations. Each vehicles compliance with its scheduled route is checked for every route scheduling, and the vehicle positions should be accurate in the traversal through the zone. The vehicle position is estimated with its velocity calculated by imitating autonomous driving, especially for use in the situation of unavailable communication. The estimation technique does not adopt an inertial measuring unit and the accelerometer, differently from previous works in estimating the position of a moving entity. The velocity calculated by imitating autonomous driving has the lower deviation range of errors than that by linear interpolation at a feasible reporting interval through the simulation.

Possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An Australian case study

This paper aims to develop a capacitated vehicle routing solution to evacuate short-notice evacuees with time windows and disruption risks under uncertainties during a bushfire. A heuristic solution technique is applied to solve the triangular possibilistic model to optimise emergency delivery service. The effectiveness of the proposed algorithm is evaluated by comparing it with a designed genetic algorithm on sets of 20 numerical examples. The model is then applied to the real case study of 2009 Black Saturday bushfires in Victoria, Australia. The results show that it is possible to transfer the last-minute evacuees during the Black Saturday bushfires under the hard time window constraint. Network disruptions however have impact on resource utilisation. The modelling outputs will be useful in the development of emergency plans and evacuation strategies to enhance rapid response to last-minute evacuation in a bushfire emergency.

A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand

Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs are among the biggest sources of expenditure in company activities, it is of great significance to optimize this in the Home Health Care industry. From the perspective of optimizing the cost of transportation, this paper studies the Vehicle Routing Scheduling problem as it applies to HHC companies. According to a survey of the HHC companies, during the process of delivering medication drugs, the quantity of drugs required for each patient is non-deterministic when the company makes planned routes. This paper considers uncertain demand as a fuzzy variable, which is closer to a potential real life scenario. A Home Health Care Scheduling Problem with fuzzy demand is considered and a fuzzy chance constraint model is designed. We propose a hybrid genetic algorithm integrated with stochastic simulation methods to solve the proposed model. Firstly, the problem is reduced to the classical vehicle routing problem within a time window. Experimental results for Solomon’s and Homberger’s benchmark instances show that the proposed algorithm performs efficiently. Then other experiments on the fuzzy version model are undertaken with the variable value of the Dispatcher Preference Index (DPI) parameter between [0, 1]. Finally, the influence of DPI on the final objective and the indicators of the problem are discussed using stochastic simulation, and the best value of DPI is obtained. This research will help HHC companies to make appropriate decisions when arranging their vehicle scheduling routes.

Two-echelon location routing synchronized with production schedules and time windows

In this paper we study a problem that arose while planning the delivery of perishable goods, such as newspapers or foodstuff, in urban areas. The customers require the delivery of several different products within specified time windows. Given a production schedule, facilities for cross docking and a vehicle fleet, the problem is to create a cost effective delivery plan. We propose a heuristic that maintains product availability, and simultaneously, generates an efficient routing schedule. Product availability is maintained through a preflow-push algorithm performed on a time-space facility network, while the routing is guided by simulated annealing. The heuristic was tested on a set of specifically created test instances and on standard 2E-LRP instances by Prodhon.

Trip-based optimization methodology for a rule-based energy management strategy using a global optimization routine: the case of the Prius plug-in hybrid electric vehicle

The fuel savings of plug-in hybrid electric vehicles strongly rely on the energy management strategy deployed onboard. For the current mass-produced plug-in hybrid electric vehicles, notably the Toyota Prius, the energy management strategy is a rule-based type, which is configured to optimize instantly the fuel consumption without taking into consideration the upcoming driving patterns of the given route schedule. Hence, it operates the vehicle first in the electric mode over a predefined all-electric range and then in the charge-sustaining mode. The energy consumption results are seen to be far from optimal when compared with global optimization strategies with prior knowledge of the scheduled route, such as dynamic programming. Hence, this study presents the methodology to optimize the rule-based energy management strategy for real-time implementation in the Prius plug-in hybrid electric vehicle, using dynamic programming as the global optimization routine. The optimization process takes into account the desired trip profile selected by the driver on the vehicle’s onboard Global Positioning System and linked to a traffic management system. A basic rule-based energy management strategy, which emulates the vehicle performance and the energy consumption, has been set first using on-road measurement data logging. As a second step, the dynamic programming optimization routine was applied to the model, assuming a repeated New European Driving Cycle as the scheduled route. The results obtained for the behaviours of the powertrain components under optimal control are evaluated and used to update the operating energy management rules of the basic controller. Finally, an optimized rule-based controller is proposed by coupling between the dynamic programming and the basic rule-based controller, followed by an evaluation of the energy consumption and the powertrain efficiency of the three investigated control strategies.

The driver assignment vehicle routing problem

We introduce the driver assignment vehicle routing problem, DAVRP. In this problem, drivers are assigned to customers before demand is known, and after demand is known a routing schedule has to be made such that every driver visits at least a fraction α of its assigned customers. We present a solution procedure to investigate how much transportation costs increase by adhering to the driver assignments. Furthermore, we distinguish between the case in which customers that are not visited by their assigned driver are visited by backup drivers only, and the case in which slack capacity of regular drivers is utilized to visit these customers. We use randomly generated instances of the DAVRP to provide examples where the difference in transportation costs is substantial. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(3), 212–223 2016

Distributed Scheduling and Delay-Aware Routing in Multihop MR-MC Wireless Networks

In multiradio multichannel (MR-MC) networks with significantly expanded network resource space, many existing scheduling/routing algorithms rely on a link-based network model and apply different heuristics in algorithm design to achieve/approximate throughput optimality. In this paper, using a tuple-based multidimensional conflict graph model, we establish a cross-layer framework, which facilitates systematically studying distributed scheduling and routing in multihop multipath MR-MC networks. In this framework, each tuple link is installed with a routing controller, which feeds controlled amounts of data to the tuple-link output queues for scheduling and transmission. We rigorously prove that, under a set of certain conditions, the network is queue stable in the mean sense under the distributed maximal scheduling policy. Based on Lyapunov optimization, we further propose a distributed delay-aware multipath routing method, which aims at minimizing the end-to-end delay of each commodity flow. Extensive simulation results demonstrate that the proposed joint scheduling/routing algorithm outperforms existing link-based single-path and multipath algorithms and tuple-based cross-layer control algorithm.

Efficiency assessment and output maximization possibilities of European small and medium sized airports

The aim of this research is to investigate the operational efficiency and potential output increase scenarios of 21 small and medium sized airports (SMA) located in 10 different European countries. A key feature of the proposed Data Envelopment Analysis (DEA) models is the consideration of two novel inputs related to the number of airlines and scheduled routes operating at each airport, in addition to the more common capital non-discretionary inputs (runways, boarding gates and apron). These novel inputs are a way of considering the effects of the air transport market on airports' performance and growth prospects. In particular, a model to estimate the growth in passenger numbers, aircraft movements and cargo that can be achieved by an airport through increasing its number of airlines and routes, and assuming an efficient operation, is presented. Also, a multi-objective DEA model is proposed to explore the possible trade-offs in the output Pareto efficient frontier of a given airport. In addition, a second stage fractional regression model specific for explaining DEA efficiency scores is conducted and the results suggest that ownership structure and hub airport status are significant factors for explaining variations in the operational efficiency of European SMA.

The Shortest Path Problems in Battery-Electric Vehicle Dispatching with Battery Renewal

Electric vehicles play a key role for developing an eco-sustainable transport system. One critical component of an electric vehicle is its battery, which can be quickly charged or exchanged before it runs out. The problem of electric vehicle dispatching falls into the category of the shortest path problem with resource renewal. In this paper, we study the shortest path problems in (1) electric transit bus scheduling and (2) electric truck routing with time windows. In these applications, a fully-charged battery allows running a limited operational distance, and the battery before depletion needs to be quickly charged or exchanged with a fully-charged one at a battery management facility. The limited distance and battery renewal result in a shortest path problem with resource renewal. We develop a label-correcting algorithm with state space relaxation to find optimal solutions. In the computational experiments, real-world road geometry data are used to generate realistic travel distances, and other types of data are obtained from the real world or randomly generated. The computational results show that the label-correcting algorithm performs very well.

A VEHICLE ROUTING PLANNING SYSTEM FOR GOODS DISTRIBUTION IN URBAN AREAS USING GOOGLE MAPS AND GENETIC ALGORITHM

This article presents a system aimed at generating optimized vehicles routes - the vehicle routing problem with time windows (VRPTW) based on using a Google Maps™ network data and imperialist competitive algorithm meta-heuristic. The vehicle routing problem with time windows is an important problem in the supply chain management. It copes with route scheduling and the distribution of goods from the distribution center to geographically dispersed customers in urban areas by a fleet of vehicles with constrained capacities. The system was tested for urban goods distribution in Sofia, Bulgaria - 35 retailers, warehouse, two types of vehicles (capacity) and a working day.

A joint optimization model for liner container cargo assignment problem using state-augmented shipping network framework

This paper proposes a state-augmented shipping (SAS) network framework to integrate various activities in liner container shipping chain, including container loading/unloading, transshipment, dwelling at visited ports, in-transit waiting and in-sea transport process. Based on the SAS network framework, we develop a chance-constrained optimization model for a joint cargo assignment problem. The model attempts to maximize the carrier’s profit by simultaneously determining optimal ship fleet capacity setting, ship route schedules and cargo allocation scheme. With a few disparities from previous studies, we take into account two differentiated container demands: deterministic contracted basis demand received from large manufacturers and uncertain spot demand collected from the spot market. The economies of scale of ship size are incorporated to examine the scaling effect of ship capacity setting in the cargo assignment problem. Meanwhile, the schedule coordination strategy is introduced to measure the in-transit waiting time and resultant storage cost. Through two numerical studies, it is demonstrated that the proposed chance-constrained joint optimization model can characterize the impact of carrier’s risk preference on decisions of the container cargo assignment. Moreover, considering the scaling effect of large ships can alleviate the concern of cargo overload rejection and consequently help carriers make more promising ship deployment schemes.

CARE: Heuristics for two-stage multi-product inventory routing problems with replenishments

The integration of distribution and routing constitutes the Inventory Routing Problem (IRP), an important aspect of Vendor Managed Inventory (VMI) systems. IRP involves decisions on timing of deliveries, sizes of shipments and routing of vehicles. In the context of supply chain collaboration, the scope of IRP expands to include replenishments at the depot. This paper studies an IRP involving multiple products from different suppliers, a central depot (CD) and a set of warehouses. We develop an integrated Integer Linear Programming (ILP) model to determine the optimal replenishments from suppliers to the CD as well as the routing schedules of vehicles from CD to the multiple warehouses over the defined time horizon. We propose an extended three-phase heuristic called CARE (Clustering, Allocation, Routing -Extended). In the heuristic, the replenishment schedules, from suppliers to the CD (for multiple products in the defined time horizon), is solved using a dynamic programming (DP) model. The routing schedules of vehicles from CD to the multiple warehouses is then modeled and solved as a capacitated IRP (CIRP) problem. With the replenishment schedules as an input from the DP model, the CIRP is solved in three-phases: Cluster the nodes, Allocate to these nodes and Route delivery vehicles through clusters of nodes. Three variants of heuristics are proposed based on the changes to the routing phase – CARE1, CARE2, and CARE3. Computational results for a broad range of test instances yield promising results and show usefulness of the solution approaches. Particularly, the proposed heuristics outperformed CPLEX upper bounds for larger problems with lesser computational effort. The average improvement obtained by the heuristics over CPLEX upper bounds for 15 and 20 warehouse problems, respectively, are: CARE1 – 0.94% and 3.6%, CARE2 – 1.36% and 4.26%, CARE3 – 2.87% and 6.08%. We also show that the results from the proposed heuristic are statistically better than the CPLEX upper bounds. The proposed approaches can easily be implemented into existing VMI systems.

Optimization of Ship’s Route Scheduling Using Genetic Algorithm

<span lang="EN-US">Route scheduling is a quite complicated process because it involves some determinant factors. Several methods have been used to help resolve the NP-hard problems. This research uses genetic algorithm to assist in optimizing ship scheduling, that where there are several ports to be visited by some ships. The goal is to divide the ship to go to a specific port so that each port is only visited by one ship to minimize the total distance of all ships. The computational experiment produces optimal parameters such as the number of popsize is 30, the number of generations is 100, crossover rate value is 0.3 and mutation rate values is 0.7. The final results is an optimal ship route by minimizing the distance of each ship.</span>