客户订单合成配送问题的建模

为实现客户订单合成配送的自动化,以便节省人力、物力和运输时间,实现最优或近优合成配送,构造了客户订单合成配送问题的数学模型,此模型能够广泛应用于大中型企业、商场等产品配送系统,为实现客户订单合成配送问题的自动化提供了一个基本方法.     

客户成套订单配送策略混合遗传算法研究

针对第三方物流企业如何安排成套订单的合成运输问题,提出了客户成套订单配送决策问题,通过增加虚拟节点的方式,使此问题模型巧妙地转化为有时间窗车辆路径问题VRPTW模型。同时提出了一种混合遗传算法,成功地解决了在车辆数最小条件下,行车路程最短的关键问题。算例结果以及与其他文献算法的比较结果证明算法是有效的。     

客户订单合成配送问题的建模与启发式算法

在一些模型假设的基础上,建立了客户订单合成配送问题的数学模型;然后提出了求解该问题的以最小化运输费用为目标函数的两阶段启发式算法;最后给出一个用此算法求解该问题的实例。实验结果表明,该算法对于2×30×2规模能够在较短时间内有效地求得问题的近优解,算法的运行时间取决于源批发商数、订单数和交货组数。此算法为实现客户订单合成配送问题的自动化提供了一个基本方法。     

客户订单合成配送问题建模与遗传算法研究

为了降低成本,取得企业竞争优势,合理进行配送成为实现物流规模经济、降低物流成本的重要手段。在一些模型假设的基础上,建立了目标函数为最小化运距的客户订单合成配送问题的数学模型,提出并实现了解决该问题的遗传算法。大量仿真结果表明：该算法能够在较短的时间内有效求得问题的近优解,该算法的运行时间主要取决于订单数和交货组数,为实现物流配送提供了一个基本方法。     

顺丰大规模布局电商配送：已接易迅30％订单

原本定位高端商务快递市场的顺丰速递，正在想办法分食电商快递这一高速增长的市场，除了自建电商做经验探索、推出电商类客户特惠产品，顺丰还悄然与腾讯旗下电商易迅达成深度合作，目前顺丰已经接下易迅30％订单量。     

考虑客户满意度的网络零售配送时隙定价策略

为研究网络零售配送时隙的定价问题,结合客户满意度和收益双重目标,根据预期不确认理论和层次分析法给出了基于价格、交付期长度和宽度的客户满意度模型。基于收益管理理论,引入0-1变量,给出了考虑机会成本及额外成本的收益模型。根据零售商不同的收益偏好给出了双目标条件下的时隙定价模型。通过算例模拟分析了不同的零售商收益偏好对于时隙定价以及收益的影响;根据客户在线时隙选择随机性的特点,分析了在不同交付期长度及敏感度下的时隙定价策略,以及不同的客户到达率对于收益、客户满意度的影响。分析结果得出了考虑客户满意度和不同影响因素下的时隙定价策略,以及一系列比较有指导意义的性质和方法。     

考虑具有不同交付选择的物流配送路径问题研究

为了满足城市配送中顾客对交付方式及特定时间的个性化需求,引入顾客偏好概念刻画送货上门与自提服务的交付需求,以总运营成本最小化为优化目标建立了具有不同交付选择的车辆路径优化模型。考虑到模型的复杂性引入多种算子,设计并改进自适应大邻域搜索算法对模型进行求解。最后结合重庆市南岸区某配送案例进行实例分析,验证了该模型与算法的有效性。     

基于聚类的城市共同配送海量订单调度问题研究

针对云物流环境下城市共同配送海量订单调度难的问题,本文提出基于订单聚类的调度算法.首先针对单中心多车辆调度问题,提出基于单亲遗传的优化调度算法;在此基础上综合考虑城市配送中心的地理位置、车辆及配送点的地理位置、货物的种类、需求量,提出采用蚁群算法构建基于配送中心的海量订单聚类、优化调度算法.     

多门店协同下的订单拆分与配送联合优化方法

新零售带动传统企业转型,加速了以实体门店作为前置仓的线上订单履行模式的发展。针对订单需求不确定导致的就近门店无法满足订单需求的情况,提出多门店协同下的订单拆分与配送的联合优化问题。通过引入拆单数量限制,缩减问题求解空间,同时为了减少单独配送导致的路径重叠,采用协同配送的模式整合路径,并通过订单拆分与配送之间的调整优化降低订单履行成本。集成广度优先搜索和局部搜索算法,构造TNILS(top-N&improved local search)混合启发式算法求解问题。在合成数据集的基础上,通过协同配送与单独配送的结果对比,证明了协同配送的有效性及提出算法的可行性。通过与其他算法的实验结果对比,验证TNILS算法的有效性和稳定性。     

B2C环境下带信息流的多阶段订单配送问题

根据B2C(商家对客户)环境下订单配送问题的特点,建立了相应的多阶段订单配送问题的模型.利用电子订货系统提供的信息流预测订单的未来信息.在处理配送问题时考虑订单信息,可降低企业的配送成本并提高企业的配送效率.提出两种有效整合订单配送的方案,将禁忌搜索算法与插入算法相结合对模型进行求解.仿真实验与比较验证了模型的合理性及算法的有效性.     

Economic dispatch with generation constraints

The classical procedure for solving the economic dispatch problem in the presence of upper and lower limits on the generation levels may fail to lead to the constrained optimum generation schedule. In this paper a simple scheme suitable for real-time applications which resolves this drawback is presented. When transmission losses are neglected, the constrained optimum can be analytically computed based on a distance measure of the unconstrained optimum schedule to the violated limits. In the presence of transmission losses, the problem is first converted into an equivalent lossless case by a simple transformation which can then be solved by the proposed algorithm.     

Real-time urban power dispatch with ambient air quality constraints

A power dispatch methodology for urban utility company facing ambient air quality constraints is developed. This approach consists of (1) a real-time air dispersion model for the urban area and its surrounding region, (2) a fuel minimization procedure which will generate a schedule to satisfy the demand and the constraints on pollution concentration. Forecast meteorological data and ambient air quality information are assumed available for the procedure.     

Optimal dispatch of electric power with ambient air quality constraints

A method is given for allocating power loads for a system of electric generating units in a way which meets the time-dependent demand and minimizes the sum of the fuel cost and a penalty function for pollution. The method takes into account explicitly the dynamic nature of ambient air pollution which depends on the topography of a region and on the past performance of the system. With suitable and reasonably realistic approximations, the problem is reduced to a linear-quadratic optimal control problem for which a linear feedback control rule is obtained. In addition, a discrete version of the problem is given which has the form of a quadratic programming problem, and thus can accommodate, in a straightforward manner, additional constraints such as fuel limitations and upper bounds on emission.     

An approach to on-line power dispatch with ambient air pollution constraints

An on-line optimal environmental/economic dispatch methodology for electric power generation is developed in this paper. Aside from the conventional economic dispatch, constraints on air quality (such as those specified by the U.S. Environmental Protection Agency) are added to the minimum fuel cost problem. Using the integrated Gaussian puff model based on the statistical turbulent theory, rapid dynamic features of pollutant dispersion and its forecast surrounding the plants are emphasized. By applying a convex programming algorithm repeatedly, a set of marginal environmental imposts for the power plants at different times are obtained. Such imposts are incorporated with the fuel cost in the ordinary short-term economic dispatching program to indirectly account for the environmental impact of power generation on the quality of the ambient air. The approach is specifically taken to have little modification for existing economic dispatch programs and be implemented for real power networks. The proposed approach has been simulated in a power system with three plants and three monitoring points.     

Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints

Optimal reactive power dispatch (ORPD) is well known as a complex mixed integer nonlinear optimization problem where many constraints are required to handle. In the last decades, many artificial intelligence-based optimization methods have been used to solve ORPD problem. But, these optimization methods lack an effective means to handle constraints on state variables. Thus, in this paper, the novel and feasible conditional selection strategies (CSS) are devised to handle constraints efficiently in the proposed improved gravitational search algorithm (GSA-CSS). In addition, considering the weakness of GSA itself, the improved GSA-CSS (IGSA-CSS) is presented which employs the memory property of particle swarm optimization (PSO) to enhance global searching ability and utilizes the concept of opposition-based learning (OBL) for optimizing initial population. The presented GSA-CSS and IGSA-CSS methods are applied to ORPD problem on IEEE14-bus, IEEE30-bus and IEEE57-bus test systems for minimization of power transmission losses (P_loss) and voltage deviation (V_d), respectively. The comparisons of simulation results reveal that IGSA-CSS provides better results and the improvements of algorithm in this work are feasible and effective.     

DPD: An intelligent parallel hybrid algorithm for economic load dispatch problems with various practical constraints

The Economic Load Dispatch (ELD) problem has attracted much attention in the field of electric power system. This paper proposes a novel parallel hybrid optimization methodology aimed at solving ELD problem with various generator constraints. The proposed approach combines the Differential Evolution (DE) and Particle Swarm Optimization (PSO). Initially the whole population (in increasing order of fitness) is divided into three groups - Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. The proposed method is called DPD as it uses DE-PSO-DE on a population in parallel manner. Two strategies namely Elitism (to retain the best obtained values so far) and Non-redundant search (to improve the solution quality) have been employed in DPD cycle. Moreover, the suitable mutation strategy for both DEs used in DPD is investigated over a set of 8 popular mutation strategies. Combination of 8 mutation strategies generated 64 different variants of DPD. Top 4 DPDs are investigated through IEEE CEC 2006 functions. Based on the performance analysis, best DPD is reported and further used in solving four different typical test systems of ELD problem. Numerical and graphical results indicate the efficiency, convergence characteristic and robustness of proposed DPD.     

同时取送货的三维装载约束下车辆路径问题

同时取货和送货的三维装载约束下车辆路径问题（3L-VRPSDP）是经典车辆路径问题（VRP）的一个扩展,在3L-VRPSDP中,顾客可能要求同时取货和送货,而且货物装载要考虑其三维大小、先进后出、底面支撑等装载特性。针对这类问题,提出了基于左底优先和最大接触面优先的装载算法,在装载时要考虑其配送特征;在此基础上,设计了对应的禁忌搜索算法对初始结果进行优化,通过实验验证了算法的有效性。     

不确定干扰约束下外集卡提箱策略

为提高集装箱码头作业效率,降低不确定干扰因素对外集卡提箱作业的影响,提出以滚动窗口策略处理干扰因素的方法,并建立以作业延误惩罚成本与场桥移动成本最小化为目标的混合整数模型,采用遗传算法（GA）进行求解。首先,利用滚动窗口策略得到在无干扰因素情况下的外集卡提箱作业调度方案;其次,当出现干扰因素时触发滚动窗口再调度机制对外集卡提箱作业顺序重新安排;最后,计算出各滚动窗口内最优的调度方案,提出总计划时间内最优作业方案。通过对不同情景下的案例求解结果进行对比分析,实验结果表明在无干扰情况下,滚动窗口策略下的最小作业成本比传统作业方式下降低了9%,而在干扰情况下滚动窗口策略优于传统作业方式15%,进而验证了算法的有效性以及滚动窗口策略对外集卡提箱作业的优越性。     

一类输出受限非线性系统的降维非递归预设性能控制策略

针对一类非匹配受扰非线性系统在输出受限条件下的跟踪控制问题, 本文在预设性能控制过程中引入非递归设计框架, 提出了一种降维非递归预设性能控制方案, 以缓解常用的递归预设性能控制过程中出现的复杂性爆炸问题, 大幅简化了系统的控制器设计过程, 便于实际工程应用. 本文设计的降维非递归控制策略能够实现控制器设计过程与稳定性分析的分离, 并且具有控制器形式简洁、控制参数选取机制简单的优点. 最后, 选取具有代表性的数值仿真和永磁同步电机位置跟踪控制实验阐释了所提出的控制策略的有效性及简洁性.     

基于优化一致性算法的微网功率经济分配策略

针对微网经济调度问题而通常采用的分布式控制,被控单元之间物理距离一般比较远,可能会存在较大的通信滞后,而通信滞后会降低分布式控制精度,因此提出了一种基于优化一致性算法的微网功率经济分配策略。首先将下垂控制信息和改进的一致性算法相结合,通过下垂控制实现功率分配,能够有效地克服滞后,使得系统具有很好的收敛性能;同时采用一致性算法设计成本最小化控制器,实时控制微网发电机运行,协调各电源出力,最终在成本最小化下实现功率分配。最后仿真结果验证了策略的有效性。