具有模糊时间窗的多模式联运建模及优化

以考虑用户偏好的模糊时间窗多式联运为研究对象,建立了基于图状结构的模糊时间多式联运模型。在分析模型特征的基础上,设计了基于正交试验的混合田口遗传算法实现路径及运输方式的组合优化。通过考虑不同用户偏好的5个任务来考察模型和优化算法的有效性。计算结果表明,该算法能有效求解出满足用户偏好的路径及运输组合方式,为物流企业决策提供依据。     

考虑港口忙闲时间窗约束的多式联运路径优化

随着海运贸易量的持续增长,以港口为核心枢纽的多式联运引起了多方关注。为追求多式联运整体效益,立足承运商角度,研究集港模式下集装箱多式联运的路径优化问题,基于港口忙闲时间段考虑忙闲时间窗约束,兼顾中转节点的周期性时间窗以及各运输方式的班期限制,构建以总运输成本最小为目标的路径优化模型,并设计改进遗传算法(IGA)对模型求解。结果表明,与不考虑港口忙闲时间段相比,考虑该因素可令集港货物有效错开港口业务繁忙期,进而避免过高的预集港费和晚集港费;同时,忙碌时间窗越接近截港时刻,空闲时间窗之间的间隔越长,对承运商的影响越大,因此考虑港口忙闲时间窗的运输方案更为合理。     

基于分支定价算法的多时间窗家庭医护人员调度问题研究

为了减少医护人员调度成本，提高客户满意度，研究了家庭医疗护理人员调度问题。考虑客户具有多个可接受服务的时间窗，并对不同时间窗具有不同偏好的特性，建立以总运营成本最小、满意度最大为目标的数学模型。基于Dantzig-Wolfe分解原理将所建模型重构为集合划分主问题和含多时间窗的最短路径子问题模型。运用将列生成嵌入分支定界框架中的分支定价算法对问题求解，并根据多时间窗的问题特性设计了快速获得初始解的随机贪心算法和求解子问题的改进标签算法。对50组算例进行测试，将所提出的算法与CPLEX对比，验证了算法的有效性。最后比较单时间窗和多时间窗算例结果发现，客户提供多个可接受服务的时间窗能有效降低调度成本。     

带时间窗口的多式联运模型与算法

以带时间窗的多式联运模型为基础,考虑节点间的运输时间、节点处的运输方式转换时间以及可能发生的货运延迟,以包括运输成本、转运成本和时间偏离惩罚成本在内的总成本最小为目标函数,建立基于满意时间路径的多式联运模型.在算法上,构造了基于K最短路的改进遗传算法,通过3个算例说明多式联运的运输路径与运输方式搭配可以不是唯一的,为多式联运的相关研究工作提供了理论基础.     

改进樽海鞘算法求解带时间窗的应急选址路径问题

目的 为使应急物资及时高效地送到灾区,针对多目标应急选址-路径问题,在考虑灾区的时间窗及物资运输过程中道路安全的情况下,以最小化经济成本、最小化时间惩罚成本及最大化道路安全性为目标,构建多目标优化模型。同时,设计改进的樽海鞘算法求解问题,以验证模型的可行性和算法的有效性。方法 根据模型的特征对樽海鞘算法进行改进,运用随机生成和贪心算法相结合的方式生成初始解,利用交叉算子和邻域搜索算子改进原始算法的位置更新操作,引入非支配排序遗传算法（NSGA-Ⅱ）的精英保留策略,以提高算法的性能。结果 经过多个算例测试,该算法能快速获得一簇Pareto解,与基本樽海鞘算法进行对比后可知,改进后的算法性能更优越。结论 对于灾后及时响应的应急选址路径问题,采用改进的樽海鞘算法具有一定优越性,并在多个目标权衡的情况下,可供决策者根据目标的偏好找到较满意的解,对于研究应急选址路径问题具有一定的参考价值。     

考虑拥堵情形的污染路径问题及其求解算法

考虑拥堵情形的污染路径问题是经典的带时间窗车辆调度问题的一个扩展。该问题的目标函数包括车辆行驶产生的排放成本,约束条件则包括交通拥堵带来的车辆行驶速度约束——该拥堵只与时间有关(time-dependent),且拥堵的开始时刻和结束时刻都可以自由设定。首先提出了拥堵情形下的行驶时间计算模型,在此基础上建立污染路径问题的线性规划模型,并提出了基于节点时间窗变换以及速度和出发时间优化的求解算法。算例结果验证了该算法的高效性。     

具有时间窗约束累积性车辆路径问题的禁忌搜索优化算法

基于对中国实际物流运输中成本计算方法的研究,考虑到我国高速公路系统计价方式的特殊性,提出了具有时间窗约束的累积性车辆路径问题。以降低实际车辆运输成本为目标,设计了新型的禁忌搜索算法对问题进行有效求解;算法中嵌入多种邻域搜索方法,允许同时在可行和不可行解空间内进行邻域搜索,同时采用Nagata提出的时间窗违反量计算方法[1-2]对解的时间窗约束违反进行评估。针对提出的新型问题的数值试验证明了所采用的时间窗违反量计算方法的时间节约性和有效性;同时由于该问题可以覆盖传统的累积性车辆路径问题,对后者的数值实验以及与其他优化算法的对比验证了所提出算法的优良求解效果。     

顾客可选时间窗下城市配送订单接收策略研究

在顾客可选城市配送时间窗的情境下,研究了带硬时间窗约束的动态车辆路径问题,提出了基于辐射半径的订单接收策略,设计了可接受时间窗分配初始化、可接受时间窗动态调整、参照点动态选择及基于时间窗偏差阈值的配送需求评估等方法,构建了求解动态车辆路径问题的插入算法。通过与先到先服务策略的对比表明,提出的策略能够实现配送收益和行驶距离的有效平衡;基于辐射半径的订单接受策略在平均接受决策耗时、路径更新耗时和全局优化耗时与先到先服务策略差异较小;随着参照点数量增多,算例中收益和行驶距离都在逐渐增长,总收益/距离之比呈现先下降后上升的趋势。     

多时段可搭载零担物流路径优化问题模型及算法研究

基于承运人的视角,考虑运输任务在可搭载情况下的零担物流路径规划问题,建立了以收益最大为目标函数的数学模型,并设计了一种基于动态规划的启发式(a heuristic algorithm based on dynamic planning,HAbDP)算法进行求解。首先利用改进的动态规划算法求出无搭载任务时任意起点到任意终点间的最短路径,随后利用2-opt交换算子将各阶段出现的不可行路径转化为可行路径并进行任务搭载操作,统筹优化后得到完整计划期内车辆路径运行方案。通过数值算例分析得到如下结论:①在小规模问题中,HAbDP算法与精确算法相比,二者的最优收益相近,但前者计算时间大幅度减少;②在较大规模问题中,HAbDP算法与粒子群算法相比,在计算时间上较为接近,但是在求解质量上前者占优;③与传统零担物流路径方案相比,考虑运输任务可搭载后,其空载率、空载线路数量均有显著降低,而总收益显著提升。因此,提出的模型及HAbDP算法对求解可搭载零担物流路径优化问题具有一定的现实意义和参考价值。     

第三方物流联盟中物流任务的优化调度

为了提高物流服务水平、降低物流运作成本,针对由多个第三方物流服务商组建而成的第三方物流联盟中物流任务与物流服务资源的优化调度问题展开研究,综合考虑各第三方物流服务商资源节点提供物流活动服务成本和物流服务总时间,以时间最短和成本最低为优化目标,提出了基于时间和成本的多目标优化调度模型,针对目前物流任务调度优化模型中只考虑各物流服务资源节点本身的服务成本和时间,而未考虑执行各个物流活动之间的物流资源节点之间的衔接时间与衔接成本的问题,提出一种计算不同物流服务资源节点之间的物流服务衔接时间和衔接成本的方法,在模型中,考虑了物流资源服务时间窗限制问题.最后提出了一个改进的遗传算法进行模型求解,并通过算例验证了研究的有效性.     

A two-stage decomposition method on fresh product distribution problem

Refrigerator cars are widely used for fresh product distribution. The energy consumption of these vehicles is sensitive to the environment temperature, and changes continuously due to fluctuations of the environment temperature. As a result, the total refrigeration cost is influenced by the car’s departure time. To reduce operation costs of third-party transportation providers, the refrigerator car scheduling problem is addressed in this research. A time-dependent mixed-integer programming model is established to reduce total operation costs, including routing, time penalty, cargo damage and refrigeration costs. An adaptive heuristic method is proposed by combining the variable neighbourhood search and particle swarm optimisation. To improve the algorithm quality, a two-stage decomposition method is developed. The problem is divided into two echelon sub-problems. One is the shortest path problem, and the other is the departure time scheduling problem. A feedback strategy is utilised to avoid local optimal solutions and design of experiments methodology is adopted to derive the optimal parameter setting of the algorithm. Numerical experiments are conducted to demonstrate the effectiveness of the proposed time-dependent decision model.     

改进分散搜索算法求解包装废弃物回收路径规划问题

目的 将包装废弃物回收路径规划归纳为一个带回路和时间窗的逆向物流车辆路径问题（RL-VRPBTW）,以最小化回收成本、发车成本和时间窗惩罚为联合优化目标进行建模。方法 引入“车辆剩余空间回收能力”因素,改进经典节约里程算法,求得较好的初始解;基于分散搜索框架,设计基于初始解改进的分散搜索算法（ISISS）,根据问题模型,采用含0的编码方式,通过多样性产生、参考集更新、子集产生、子集合并、解改进等5个步骤实现算法功能。结果 在“部分回收点分布较密集”的城市型地理场景下,针对快消企业的低值固废包装,生成回收点数量分别为50、100、200的3种规模算例,并考虑大小两种车型进行仿真实验。将ISISS算法与改进节约里程、遗传和分散搜索3种算法比较后可知,ISISS算法在大规模包装废弃物回收车辆路径问题上具有更优的求解性能。结论 仿真实验结果表明,ISISS是一种求解多目标大规模包装废弃物回收路径规划问题的较优算法。     

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.     

大规模基站运维优化与车队规划研究

研究了考虑充电量决策的大规模基站断电运维的多车辆路径规划问题。目标是在运维能力有限的条件下最小化基站断电产生的损失和车辆运行成本的加权总和。分析了该问题区别于传统取送货问题的差异与难点,利用图论模型建立了动态多阶段车辆路径的混合整数规划模型,利用库存理论辅助基站充电量的决策并通过软时间窗口对车辆服务与路径规划进行约束,最后设计了基于局部最优插入和变邻域搜索的动态算法框架进行求解。基于中国铁塔公司真实数据生成的多个算例验证了该算法可以显著降低运维成本,并协助公司进行运维车队规模的规划。     

考虑电量消耗的车辆调度优化研究

基于电动汽车电量消耗特性,考虑电动车里程、载重、顾客服务时间窗等约束,建立以配送总成本最小为目标的电动车调度优化问题模型;利用自然数编码的遗传算法,求解出电动车的配送路线以及车辆的充电计划,再结合枚举法,在配送中心运营时间内以10 min为时间间隔,计算出配送车辆惩罚成本最小时的最优发车时刻。最后结合算例,验证该模型和方法的有效性、正确性。     

考虑客户满意度的车辆路径优化研究

针对传统的车辆路径问题较少关注客户满意度的情况，以客户对服务时间和货物完好性的要求来衡量客户满意度，构建基于模糊时间窗的时间满意度函数和基于货损率的货物完好满意度函数，在此基础上以客户满意度最大和运输成本最小为目标建立优化模型，设计相应算例并利用LINGO17.0软件进行求解，与中小物流企业常用的扫描法进行对比验证模型的有效性。结果表明：利用LINGO17.0求得的优化结果与扫描法相比，虽然运输成本有一定增加，但相应的平均客户满意度提高了36.3%，建立的模型能较好地平衡客户满意度和运输成本，对于物流企业配送路径的决策优化有一定的参考价值。     

Multi-attribute label matching algorithms for vehicle routing problems with time windows and backhauls

We consider the class of vehicle routing problems with backhauls and time window constraints. Our motivating application is the land transportation of air-cargo freight forwarders, which requires fast solution times and the ability to handle various operational issues such as heterogeneous vehicles, multiple trips per vehicle, and penalty for early arrival at customer sites. We formulate the problem in the framework of label matching where the labels have multiple attributes representing the states of vehicles at customer locations or possible routes that vehicles may continue to cover. Two optimization-based heuristics are developed. Experimental tests on random problems and real data show that the methods can produce quality solutions quickly and are flexible in incorporating complex constraints.     

Study on the vehicle routing problem considering congestion and emission factors

To meet the requirement of greening transportation in poor traffic condition, vehicle routing problem (VRP) with consideration of fuel consumption and congestion is studied. We formulated a time-dependent green vehicle routing problem (TD-GVRP) model with minimised total cost as the objective function which includes fuel consumption cost, and the measurement of fuel consumption is based on the Comprehensive Modal Emissions Model (CMEM). In the model, the situation of waiting at customer nodes to avoid bad traffic is defined. To solve this model, a Response Surface Method (RSM)-based hybrid algorithm (HA) that combines genetic algorithm (GA) and particle swarm optimisation (PSO) is constructed. Finally, using instances from PRPLIB database, the following experiments are carried out and the corresponding conclusions are drawn. (i) Comparison of the proposed objective and traditional VRP objectives shows that fuel consumption can be greatly reduced by introducing fuel consumption factor into the objective function. (ii) Sensitivity analysis of congestion duration provides the influence of congestion duration on fuel consumption and travel time. (iii) Experiments based on different waiting time reveal that the optimisation of departure time can reduce fuel consumption and total cost to some extent.     

A construction algorithm for designing guide paths of automated guided vehicle systems

A construction algorithm is suggested to design guide path networks for automated guided vehicle systems (AGVSs). This study uses the total travel time, including waiting and interference time, of vehicles as the decision criteria for determining the direction of path segments on unidirectional guide path layouts. The Q-learning technique is applied to estimate the travel times of vehicles on path segments. Computational experiments were performed to evaluate the performance of the proposed algorithm. The simulation results showed that the proposed algorithm is superior to Kim & Tanchoco's (1993) algorithm in terms of the average travel time, the interference time and the number of deliveries.     

An optimization algorithm for a capacitated vehicle routing problem with time windows

In this paper, vehicle routing problem (VRP) with time windows and real world constraints are considered as a real-world application on google maps. Also, tabu search is used and Hopfield neural networks is utilized. Basic constraints consist of customer demands, time windows, vehicle speed, vehicle capacity and working hours. Recently, cost and on-time delivery are the most important factors in logistics. Thus, the logistic applications attract attention of companies. In logistic management, determining the locations of delivery points and deciding the path are the vital components that should be considered. Deciding the paths of vehicles provides companies to use their vehicles efficiently. And with utilizing optimized paths, big amounts of cost and time savings will be gained. The main aim of the work is providing the best path according to the needs of the customers, minimizing the costs with utilizing the VRP and presenting an application for companies that need logistic management. To compare the results, simulated annealing is used on special scenarios. And t-test is performed in the study for the visited path in km with p-value of 0.05.