Route Search Method for Railway Replacement Buses Adopting Ant Colony Optimization

In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the discontinuation of local railway lines and introduce replacement buses to secure the transportation methods of the local people especially in rural areas. Based on the above background, targeting local railway lines that may be discontinued in the near future, appropriate bus stops when provided with potential bus stops were selected, the present study proposed a method that introduces routes for railway replacement buses adopting ant colony optimization (ACO). The improved ACO was designed and developed based on the requirements set concerning the route length, number of turns, road width, accessibility of railway lines and zones without bus stops as well as the constraint conditions concerning the route length, number of turns and zones without bus stops. Original road network data were generated and processed adopting a geographic information systems (GIS), and these are used to search for the optimal route for railway replacement buses adopting the improved ACO concerning the 8 zones on the target railway line (JR Kakogawa line). By comparing the improved ACO with Dijkstra’s algorithm, its relevance was verified and areas needing further improvements were revealed.

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.

Vehicle routing optimization algorithm based on time windows and dynamic demand

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

Research on Traveling Routes Problems Based on Improved Ant Colony Algorithm

This paper studies how to obtain a reasonable traveling route among given attractions. Toward this purpose, we propose an objective optimization model of routes choosing, which is based on the improved Ant Colony Algorithm. Furthermore, we make some adjustment in parameters in order to improve the precision of this algorithm. For example, the inspired factor has been changed to get better results. Also, the ways of searching have been adjusted so that the traveling routes will be well designed to achieve optimal effects. At last, we select a series of attractions in Beijing as data to do an experimental analysis, which comes out with an optimum route arrangement for the travelers;that is to say, the models we propose and the algorithm we improved are reasonable and effective.

Research on Vehicle Routing Problem with Soft Time Windows Based on Hybrid Tabu Search and Scatter Search Algorithm

With the expansion of the application scope of social computing problems,many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes,cultures,and the emotional needs of customers.The actual soft time window vehicle routing problem,speeding up the response of customer needs,improving distribution efficiency,and reducing operating costs is the focus of current social computing problems.Therefore,designing fast and effective algorithms to solve this problem has certain theoretical and practical significance.In this paper,considering the time delay problem of customer demand,the compensation problem is given,and the mathematical model of vehicle path problem with soft time window is given.This paper proposes a hybrid tabu search(TS)&scatter search(SS)algorithm for vehicle routing problem with soft time windows(VRPSTW),which mainly embeds the TS dynamic tabu mechanism into the SS algorithm framework.TS uses the scattering of SS to avoid the dependence on the quality of the initial solution,and SS uses the climbing ability of TS improves the ability of optimizing,so that the quality of search for the optimal solution can be significantly improved.The hybrid algorithm is still based on the basic framework of SS.In particular,TS is mainly used for solution improvement and combination to generate new solutions.In the solution process,both the quality and the dispersion of the solution are considered.A simulation experiments verify the influence of the number of vehicles and maximum value of tabu length on solution,parameters’control over the degree of convergence,and the influence of the number of diverse solutions on algorithm performance.Based on the determined parameters,simulation experiment is carried out in this paper to further prove the algorithm feasibility and effectiveness.The results of this paper provide further ideas for solving vehicle routing problems with time windows and improving the efficiency of vehicle routing problems and have strong applicability.

A Novel Method for Node Connectivity with Adaptive Dragonfly Algorithm and Graph-Based m-Connection Establishment in MANET

Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense circumstances.The involvement of the Heuristic model directly is not appropriate to offer an effectual solution as it becomes NP-hard issues;therefore investigators concentrate on using Meta-heuristic approaches.Dragonfly Optimization(DFO)is an effective meta-heuristic approach to resolve these problems by providing optimal solutions.Moreover,Meta-heuristic approaches(DFO)turn to be slower in convergence problems and need proper computational time while expanding network size.Thus,DFO is adaptively improved as Adaptive Dragonfly Optimization(ADFO)to fit this model and re-formulated using graph-based m-connection establishment(G-𝑚𝑚CE)to overcome computational time and DFO’s convergence based problems,considerably enhancing DFO performance.In(G-𝑚𝑚CE),Connectivity Zone(CZ)is chosen among source to destination in which optimality should be under those connected regions and ADFO is used for effective route establishment in CZ indeed of complete networking model.To measure complementary features of ADFO and(G-𝑚𝑚CE),hybridization of DFO-(G-𝑚𝑚CE)is anticipated over dense circumstances with reduced energy consumption and delay to enhance network lifetime.The simulation was performed in MATLAB environment.

Intelligent Iterated Local Search Methods for Solving Vehicle Routing Problem with Different Fleets

To solve vehicle routing problem with different fleets, two methodologies are developed. The first methodology adopts twophase strategy. In the first phase, the improved savings method is used to assign customers to appropriate vehicles. In the second phase, the iterated dynasearch algorithm is adopted to route each selected vehicle with the assigned customers. The iterated dynasearch algorithm combines dynasearch algorithm with iterated local search algorithm based on random kicks. The second methodplogy adopts the idea of cyclic transfer which is performed by using dynamic programming algorithm, and the iterated dynasearch algorithm is also embedded in it. The test results show that both methodologies generate better solutions than the traditional method, and the second methodology is superior to the first one.

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles(UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately,this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learningbased heuristic, namely, population based adaptive large neighborhood search(P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size(20 targets)instances in much shorter time.

多舱共配绿色车辆路径问题的改进变邻域搜索算法

针对社区团购前置仓配送场景中“多中心、高时效、多品类、高排放”难题,本文提出多车场带时间窗的绿色多舱车车辆路径问题(MDMCG-VRPTW),构建混合整数线性规划模型,并设计改进的变邻域搜索算法(IVNS)实现求解.采用两阶段混合算法构造高质量初始解.提出均衡抖动策略以充分探索解空间,引入粒度机制以提升局部搜索阶段的寻优效率.标准算例测试结果验证了两阶段初始解构造算法和IVNS算法的有效性.仿真实验结果表明,模型与算法能够有效求解MDMCGVRPTW,且改进策略提高了算法的求解效率和全局搜索能力.最后,基于对配送策略和时效性的敏感性分析,为相关配送企业降本增效提供更多决策依据.

求解带容量约束车辆路径问题的改进遗传算法

为解决传统遗传算法求解带容量约束的车辆路径问题时收敛速度慢和局部搜索能力差的问题,对传统遗传算法提出一种改进策略。使用基于贪婪策略的启发式交叉算子加强算法接近最优解的能力,加快算法收敛速度,在变异操作中,引入最近邻搜索算子,缩小基因变异范围,使用单点局部插入算子提高算法的局部优化能力。采用精英选择和轮盘赌法结合的选择策略,保持种群多样性以加强算法的全局搜索能力。实例计算测试表明,与传统遗传算法相比,所提算法求解平均偏差降低了70.25%,求解时间减少了87.41%;与ALNS和AGGWOA算法相比,有更高的求解质量和更好的稳定性。

集配一体化需求背景下选址路径集成问题算法

为做好集配一体化背景下物流网络选址-路径规划设计,用大规模邻域搜索算法的破坏、重组策略代替传统混合自适应遗传算法中的交叉、变异过程,实现算法的优化设计.通过模拟算例分析可知,优化后的算法能够有效克服传统算法在运算过程中出现的早熟及稳定性差等问题,在一定程度上提升获取更优解的概率,提高客户满意度.利用已知标杆数据对算法进行有效性检验.计算结果表明:优化后的算法各项指标表现良好,对于部分数据的计算结果优于其他3个已有算法,与已知最优解基本保持一致,进一步验证了优化算法的科学性和有效性.

考虑碳排放带时间窗的商超配送路径优化

针对商超配送多批次、小批量的实际情况,综合考虑了需求点的服务时间窗、最小配送量、访问次数等要求,设计了一种新的拆分策略,即“最大车辆载重量—最小配送量”需求拆分策略,构建相关商超配送车辆路径优化模型,并使用人工免疫算法对该模型进行求解。由于需求拆分车辆路径问题是一个复杂的组合优化过程,考虑到传统人工免疫算法局部搜索能力不足的局限,设计了多种变邻域操作改进人工免疫算法,并采用轮盘赌选择法将变邻域操作用于抗体突变,形成变邻域人工免疫算法。通过数值仿真实验,结果显示,变邻域人工免疫算法比人工免疫算法求得的综合成本平均优化3%~5%,碳排放相关成本平均降低5%~10%。

基于HTGVNS算法的卡车与无人机协同配送方法

在现代物流中,针对最后一公里配送中的成本高昂、耗时长、道路不易通行等难题,首次引入卡车与无人机协同配送模式,并以总配送时间最小化为目标,建立混合整数规划模型(MIP)。提出一种新的混合禁忌可变邻域搜索算法(HTGVNS)进行求解。算法利用旅行商问题(TSP)求解器和系统性邻域变化的探索能力求解卡车路径,将客户分配给无人机形成聚类,再使用禁忌搜索过程来优化无人机路径。通过求解不同规模算例验证了所建模型的可行性和算法的有效性。结果表明卡车与无人机协同配送模式能有效地减少配送时间,提高配送效率。

冷链物流配送网络选址-路径-库存的优化

为了控制冷链配送成本,优化3级冷链物流网络节点空间布局,提出了一种基于需求存在模糊随机性的选址-路径-库存多目标优化模型。以京津冀城市群为例,提供了优化方案。针对由物流基地、配送中心、末端网点构成的冷链物流3级配送网络,考虑到冷链产品存在订货提前期及需求具有模糊随机性,引入模糊随机变量,建立了CLRIP集成优化模型。首先,通过穷举法计算出各备选配送中心点的最佳订货周期和最优库存管理方式,将客户需求点与备选配送中心点进行随机组合,确定需要开放的备选配送中心点的位置与数量。然后,基于客户模糊随机需求,综合考虑土地、库存和配送等成本,进行选址-路径动态规划;利用改进禁忌搜索算法修正模型,直至达到最终要求,从而确定需要开放的备选配送中心点及其最优巡回路线。最后,对京津冀城市群多组实例进行分析。结果表明:使用模糊随机变量的试验数据中,最优解目标函数值和偏差稳定度均优于使用固定需求量的对照组数据,验证了CLRIP模型和改进算法的有效性。研究结果可为京津冀城市群冷链物流配送网络构建提供最优选址位置、配送路径和库存管理,为相关冷链物流配送网络优化提供实用可靠的参考方案。

部分充电策略下多中心混合车队联合配送路径优化

城市物流电动车与燃油车混合运输场景中,运输资源共享调度和充电策略联合优化方面存在不足。基于此,综合考虑客户时间窗、混合动力车队、电动车部分充电策略、多中心间联合配送机制和碳排放等实际因素,研究带时间窗和部分充电的多中心混合车队绿色车辆路径问题。以车辆固定成本、运输成本、充电成本、碳排放成本和时间惩罚成本之和最小化为目标构建优化模型,并设计混合改进遗传-变邻域搜索算法进行求解。基于湖南省某物流企业的实际数据进行仿真实验,验证了上述模型及算法的有效性,并从配送模式、车队配置和充电策略3个方面进行了敏感性分析。研究结果表明:1)联合配送模式有助于加强配送中心间的协同合作,促进运输资源共享调度,降低物流配送成本并减少碳排放,是一种经济环保的配送模式。2)电动车充电时间过长会影响客户时间满意度下降,且对纯电动车队而言,这一影响更为显著。3)混合车队相比纯电动车队具有更低的配送成本和更高的客户满意度,相比纯燃油车队在降低配送成本和减少碳排放方面更有优势。合理的车队配置不仅能减少企业运营成本,还可以同时兼顾客户利益和环境利益。4)在物流配送中采用部分充电策略能有效节省充电时间并提升客户服务体验。研究成果可为物流企业进行运输资源联合调度和配送方案优化决策提供参考依据。

多车场异构车型绿色路径规划问题研究

为拓展多车场车辆路径类问题(MDVRP)的求解方式及途径,引入了油耗和碳排放量的近似计算方法,建立了带时间窗的多车场多车型绿色车辆路径问题模型(GMDHF-VRPTW)。将物流配送和碳排放总成本最小化作为优化目标,设计改进的禁忌搜索算法(ITS)求解该模型。为验证提出模型的有效性和可行性,采用Solomon测试数据集,分别从最优目标值、求解时间等方面与经典的禁忌算法进行对比,实验结果表明ITS方法有显著的优势。此外,为降低问题求解的复杂度,结合K-means聚类算法,将多车场转化为单车场,通过判断满足需求的最小车辆数进行建模并提出了基于Gurobi求解器的聚类过滤算法(CF),在求解同规模多车场多车型路径问题进行了探索,结果表明CF颇具优势,能够为企业实际的低碳物流运输提供决策支持和方法指导。

DCS算法求解带软时间窗的送取货一体化VRP

为求解带软时间窗的送取货一体化车辆路径问题,提出一种离散布谷鸟(DCS)搜索算法。该算法在基本布谷鸟搜索算法基础之上,对莱维飞行获取新鸟巢和以一定概率抛弃鸟巢的迭代公式进行重新定义;同时设计客户序列和车辆序列双倍体鸟巢,并对客户序列采用交换、逆序、插入操作和对每辆车的访问路线采用2-Opt操作。通过5个算例的仿真实验和相关文献比较,结果表明所提DCS算法行之有效。

改进麻雀搜索算法求解多目标低碳冷链物流车辆路径问题

目的 在传统冷链物流的车辆路径问题模型基础上,考虑服务节点和车辆运输过程中产生的碳排放,并加入客户满意度,在有限资源情况下最小化路径成本和最大化客户满意度。方法 构建多目标低碳冷链物流车辆路径问题模型,将爬山算法局部搜索思想应用到麻雀搜索算法中,形成改进麻雀搜索算法,并用其对上海市某区域内的冷链物流配送路径优化问题算例进行求解。结果 通过与改进前及其他2种智能优化算法运行结果进行对比发现,改进后的麻雀搜索算法具有更快的寻优速度和更好的寻优能力,且改进后的算法对模型的碳排放效用性更高。结论 基于国家的低碳政策,设计出符合当下实情的低碳冷链物流运输模型,通过改进优化算法设计运输方案,验证了爬山算法局部搜索思想对麻雀搜索算法进行改进的有效性及所构建低碳冷链物流车辆路径模型的合理性。

考虑时变路网的多行程电动车辆路径问题研究

为了降低城市物流的电动车配送成本,考虑道路交通网络的时变性对电动车多行程配送活动进行研究。首先,以固定成本、充电成本和时间惩罚成本之和最小化为优化目标,构建时变路网下的多行程电动车辆路径问题模型;其次,利用自适应大规模邻域搜索算法对实际案例进行求解;最后,分别对出发时刻、路网时变性、充电策略和电价进行分析。结果表明:路网时变性在不同时刻下对成本和配送用时的影响不同,不同出发时刻有着不同的最优路线规划;电价的小范围波动对配送活动的影响很小;充电策略的选择对配送用时和成本的影响显著。