In-Depth Study of Potential-Based Routing and New Exploration of Its Scheduling Integration

Industrial wireless mesh networks(WMNs)have been widely deployed in various industrial sectors,providing services such as manufacturing process monitoring,equipment control,and sensor data collection.A notable characteristic of industrial WMNs is their distinct traffic pattern,where the majority of traffic flows originate from mesh nodes and are directed towards mesh gateways.In this context,this paper adopts and revisits a routing algorithm known as ALFA(autonomous load-balancing field-based anycast routing),tailored specifically for anycast(one-to-one-of-many)networking in WMNs,where traffic flows can be served through any one of multiple gateways.In essence,the scheme is a hybrid-type routing strategy that leverages the advantages of both back-pressure routing and geographic routing.Notably,its novelty lies in being developed by drawing inspiration from another field,specifically from the movement of charges in an electrostatic potential field.Expanding on the previous work,this paper explores further in-depth discussions that were not previously described,including a detailed description of the analogy between an electrostatic system and a WMN system based on precise mapping perspectives derived from intensive analysis,as well as discussions on anycast,numerical methods employed in devising the ALFA scheme,its characteristics,and complexity.It is worth noting that this paper addresses these previously unexplored aspects,representing significant contributions compared to previous works.As a completely new exploration,a new scheduling strategy is proposed that is compatible with the routing approach by utilizing the potential-based metric not only in routing but also in scheduling.This assigns higher medium access priority to links with a larger potential difference.Extensive simulation results demonstrate the superior performance of the proposed potential-based joint routing and scheduling scheme across various aspects within industrial WMN scenarios.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

An Efficient Routing Tree Algorithm for Combined Uplink and Downlink Subframes on Centralized Scheduling in IEEE 802.16 Based Wireless Mesh Networks

IEEE 802.16 mesh mode defines routing tree for transmitting data in centralized scheduling but it does not define any explicit proposal for combining uplink and downlink subframes. Deploying combined uplink and downlink subframes on the centralized scheduling scheme can be more flexible and utilization is improved. However, existing interferences among the transmission of neighboring nodes lead to performance reduction of the network. In this paper, an efficient routing tree algorithm is proposed with combined uplink and downlink slot allocation on the centralized scheduling scheme which can abate interferences in the network. This algorithm allows more subscriber stations to transmit concurrently and so improves spatial reuse in the network. Also, the algorithm uses multi-channel and single channel systems and considers relay model, smoothing switching frequently between transmitting and receiving in successive time slots and fairness in the network. Extensive simulation results demonstrate the effectiveness of the proposed method in terms of scheduling length, link concurrency ratio, network throughput and Channel Utilization Ratio (CUR).

An improved cross entropy algorithm for steelmaking-continuous casting production scheduling with complicated technological routes

In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).

A Two-Stage Scenario-Based Robust Optimization Model and a Column-Row Generation Method for Integrated Aircraft Maintenance-Routing and Crew Rostering

Motivated by a critical issue of airline planning process,this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions.Following the route network scheme and generated flight timetables,aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management.This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints,rules,and regulations.Considering multiple locations for airline maintenance and crew bases,we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering(AMRCR)to achieve the minimum airline cost.One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights.Due to the fact that disruption scenarios are expressed discretely with a specified probability,and we provide adjustable decisions under disruption to deal with this disruption risk,we provide a Two-Stage Scenario-Based Robust Optimization(TSRO)model.In this model,here-and-now or first-stage variables are the initial resource assignment.Furthermore,to adapt itself to different disruption scenarios,the model considers some adjustable variables,such as the decision to cancel the flight in case of disruption,as wait-and-see or second-stage variables.Considering the complexity of integrated models,and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance,we apply the column and row generation(CRG)method that iteratively considers the disruption scenarios.The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability.To evaluate the proposed TSRO model,which solves the AMRCR problem in an integrated and robust manner,five Key Performance Indicators(KPIs)like Number of delayed/canceled flights,Average delay time,and Average profit are taken into account.As key results driven by conducting a case study,we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models.The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems.However,for large-scale instances the proposed TSRO model falls short in terms of computational efficiency.Conversely,the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level.

Particle swarm optimization for train schedule of Y-type train routing in urban rail transit

The train schedule usually includes train stop schedule,routing scheme and formation scheme.It is the basis of subway transportation.Combining the practical experience of transport organizations and the principle of the best match between transport capacity and passenger flow demand,taking the minimum value of passenger travel costs and corporation operating costs as the goal,considering the constraints of the maximum rail capacity,the minimum departure frequency and the maximum available electric multiple unit,an optimization model for city subway Y-type operation mode is constructed to determine the operation section of mainline as well as branch line and the train frequency of the Y-type operation mode.The particle swarm optimization(PSO)algorithm based on classification learning is used to solve the model,and the effectiveness of the model and algorithm is verified by a practical case.The results show that the length of branch line in Y-type operation affects the cost of waiting time of passengers significantly.

A two-phase tabu search approach to scheduling optimization in container terminals

An optimization model for scheduling of quay cranes (QCs) and yard trailers was proposed to improve the overall efficiency of container terminals. To implement this model, a two-phase tabu search algorithm was designed. In the QCs scheduling phase of the algorithm, a search was performed to determine a good QC unloading operation order. For each QC unloading operation order generated during the QC's scheduling phase, another search was run to obtain a good yard trailer routing for the given QC's unloading order. Using this information, the time required for the operation was estimated, then the time of return to availability of the units was fed back to the QC scheduler. Numerical tests show that the two-phase Tabu Search algorithm searches the solution space efficiently, decreases the empty distance yard trailers must travel, decreases the number of trailers needed, and thereby reduces time and costs and improves the integration and reliability of container terminal operation systems.

Freight Vehicle Routing Optimization for Sporadic Orders Using Floating Car Data

The increasing popularity of e-commerce brings large volumes of sporadic orders from different customers,which have to be handled by freight trucks and distribution centers. To improve the level of service and reduce the total shipping cost as well as traffic congestions in urban area, flexible methods and optimal vehicle routing strategies should be adopted to improve the efficiency of distribution effort. An optimization solution for vehicle routing and scheduling problem with time window for sporadic orders (VRPTW- S) was provided based on time-dependent travel time extracted from floating car data (FCD) with ArcGIS platform. A VRPTW-S model derived from the traditional vehicle routing problem was proposed, in which uncertainty of customer orders and travel time were considered. Based on this model, an advanced vehicle routing algorithm was designed to solve the problem. A case study of Shenzhen, Guangdong province, China, was conducted to demonstrate the vehicle operation flow,in which process of FCD and efficiency of delivery systems under different situations were discussed. The final results demonstrated a good performance of application of time-dependent travel time information using FCD in solving vehicle routing problems.

Routing and scheduling co-design for holistic software-defined deterministic network

As a result of the growing complexity of industrial Internet applications,traditional hardware-based network designs are encountering challenges in terms of programmability and dynamic adaptability as they struggle to meet the real-time,high-reliability transmission requirements for the vast quantities of data generated in industrial environments.This paper proposes a holistic software-defined deterministic network(HSDDN)design solution.This solution uses a centralized controller to implement a comprehensive software definition,ranging from the network layer down to the physical layer.Within the wireless access domain,we decouple the standard radio-frequency modules from baseband processing to realize a software-defined physical layer,which then allows us to adjust the data transmission cycles and tag the trigger rates to meet demand for low-power,high-concurrency transmission.Within the wired network domain,we integrate software-defined networking with time-sensitive networking and propose a coordinated design strategy to address routing and the deterministic scheduling problem.We define a set of constraints to ensure collaborative transmission of the periodic and aperiodic data flows.To guarantee load balancing across all paths and timeslots,we introduce the Jain’s fairness index as the optimization objective and then construct a nondeterministic polynomial-time(NP)-hard joint optimization problem.Furthermore,an algorithm called Tabu search for routing and scheduling with dual-stages(TSRS-DS)is proposed.Simulation experiments demonstrate the effectiveness of the proposed HSDDN architecture.

TRS Scheduling for Improved QoS Performance in Cloud System

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

A Heuristic Method for Vehicle Scheduling Problems

This paper presents a sequential optimum algorithm for vehicle schedulingproblem, which includes obtaining initial theoretical solution, adjustingsolution, forming initial routes and adjustins routes. This method can beapplied to general transportation problems with multiple depots and multiplevehicle types on complex network. In comparison with manual scheduling ofChengdu Transportation Company II, the result shows that this method isreasonable, feasible and applicable.

Joint routing, scheduling, and power control for multichannel wireless sensor networks with physical interference

Reliability and real-time requirements bring new challenges to the energy-constrained wireless sensor networks, especially to the industrial wireless sensor networks. Meanwhile, the capacity of wireless sensor networks can be substantially increased by operating on multiple nonoverlapping channels. In this context, new routing, scheduling, and power control algorithms are required to achieve reliable and real-time communications and to fully utilize the increased bandwidth in multichannel wireless sensor networks. In this paper, we develop a distributed and online algorithm that jointly solves multipath routing, link scheduling, and power control problem, which can adapt automatically to the changes in the network topology and offered load. We particularly focus on finding the resource allocation that realizes trade-off among energy consumption, end-to-end delay, and network throughput for multichannel networks with physical interference model. Our algorithm jointly considers 1) delay and energy-aware power control for optimal transmission radius and rate with physical interference model, 2) throughput efficient multipath routing based on the given optimal transmission rate between the given source-destination pairs, and 3) reliable-aware and throughput efficient multichannel maximal link scheduling for time slots and channels based on the designated paths, and the new physical interference model that is updated by the optimal transmission radius. By proving and simulation, we show that our algorithm is provably efficient compared with the optimal centralized and offline algorithm and other comparable algorithms.

Applying Hierarchy Model to Routing and Scheduling Operations in Postal Logistics

Postal departments are actively taking part in e commerce, of which logistics is a key joint. Computerized routing and scheduling of postal transportation operations offers significant potential for cost decreases and productivity gains. Routing and scheduling hierarchy model is initially built and demonstrated in detail on the basis of statements of specific requirements of postal logistics in this paper, and the realized software is proved to be practical and reliable.

A Multi-Objective Scheduling and Routing Problem for Home Health Care Services via Brain Storm Optimization

At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial communities.This work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care centers.Then,various kinds of services are provided by caregivers for customers in different regions.By considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost.To handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated HHCSRP.Moreover,we perform experiments to test the effectiveness of the designed method.Via comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.

Designing Container Shipping Routes for Heterogeneous Fleet for Coastal Services

Shipping companies operating liner services keep facing a high level of competition because of the increasing demand for these operators to provide fast, efficient, effective and reliable service. It is challenging for these liner operators to offer such services and still be competitive without strategic planning. It, therefore, makes planning and scheduling of shipping routes essential for the smooth operation of liner ships, especially shipping lines operating heterogeneously mixed size fleets. This paper aims to solve a heterogeneously mixed size fleet problem by using an operation research method with the implementation of linear programming to develop optimal shipping routes for a fleet of five vessels serving six coastal ports to get optimal results. An optimal solution to the problem is found with only two routes selected as the optimal shipping routes out of four routes that are considered. The results also showed that a vessel can be assigned to multiple shipping routes.

基于离散混合蛙跳算法的地震应急物资调度

建立震后应急物资调度数学模型。该模型根据各灾区的受灾情况评估其救援紧急程度,并设计一种需求拆分供应的运输机制,提高车辆的利用效率。为求解该模型,提出一种多源信息学习的离散混合蛙跳算法。所提算法引入多种信息源以扩展算法的搜索方向,降低种群的同化速度。同时,让子组最差个体学习种群中的有效信息,提高算法的收敛精度。实验结果表明,所提算法能够搜索到精度更优的调度方案,对问题规模具有良好的可扩展性。

基于强化学习的大规模多模Mesh网络联合路由选择及资源调度算法

为了平衡新型电力系统中大规模多模Mesh网络的传输可靠性和效率,该文在对优化问题进行描述和分析的基础上提出一种基于强化学习的大规模多模Mesh网络联合路由选择及资源调度算法,分为两个阶段。在第1阶段中,根据网络拓扑结构信息和业务需求,利用一种多条最短路径路由算法,输出所有最短路径。在第2阶段中,提出一种基于多臂老虎机(MAB)的资源调度算法,该算法基于得到的最短路径集合构建MAB的摇臂,然后根据业务需求计算回报,最终给出最优的路由选择及资源调度方式用于业务传输。仿真结果表明,所提算法能够满足不同的业务传输需求,实现端到端路径的平均时延和平均传输成功率的高效平衡。

基于全局寻优的旅游路线自动调度系统设计

由于系统调度旅游路径时,部分景点游客数量较多且排队等待时间较长,为缩短行走和排队等待时间等问题,对基于全局寻优的旅游路线自动化调度进行系统设计。选取嵌入式中央处理器作为核心器件,采集游客行走速度和位置信息;分析游客偏好、景点拥挤程度与旅游感受的关系,结合游客感受和旅游成本,计算游客旅游效用值,采用蚁群算法将效用值作为蚂蚁信息素强度,全局寻优景区内的最佳旅游路线。实验结果表明,采用该方法的设计系统平均行走时间为46.7 min,平均排队等待时间为28.1 min,设计系统调度路线下的游客行走时间、排队等待时间最短,减少了游客时间消耗。