JITD确定需求单级物流分销网络ILRIP

引入了JITD思想,对分销网络中的总库存进行统一集成运作,考虑了配送的准时性和稳定性,并从物流系统集成的高度出发,研究建立了确定需求下多设施多顾客的集成设施选址——运输路线安排——库存控制问题(ILRIP)模型,设计了一个两层粒子群优化(PSO)算法,并给出了计算实例。研究结果有助于优化物流分销网络结构,提高顾客服务水平以及交期可靠度、降低成本。     

备件物流系统选址库存路径问题模型及算法

考虑到备件需求的随机性和时间紧迫性,以供应链二级分销网络的备件物流系统为研究对象,以系统总成本最低为目标,建立了带软时间窗的选址-库存-路径问题模型。由于该模型属于NP-Hard问题,因此提出了基于禁忌搜索算法和改进的C-W算法的两阶段混合启发式算法。通过实例演算,结果表明了该算法的实用性与有效性。     

离散需求下的多产品二级分销网络优化模型

构建了一个不确定需求下的多产品二级分销网络模型,以成本最小化为目标考虑了建设成本、配送成本、库存成本、缺货成本以及惩罚成本;以实际情况为背景考虑了工厂的产能约束、最小和最大转运量约束、最小配送量等约束。针对模型的复杂性,设计了禁忌搜索算法对模型进行求解,并通过一个算例验证了模型的正确性。通过遗憾值系数的变化找到了算例的最优鲁棒解,并且应用该算法求解模型耗时不到15 s,证明了算法的有效性和可行性,并具有一定的实际应用意义。     

分段线性凹成本函数下协同运输路线优化研究

提出了规模经济条件下的协同运输路线优化问题,引入了分段线性凹成本函数表示协同运输成本的规模经济特征,构造了该问题的混合整数规划模型和求解算法,展开了算法测试,并取得了非常好的计算绩效,同时给出了模型与算法在我国中枢航线网络设计中的应用过程.考虑到协同运输容易因合并运输而延长了路线,扩展了模型与算法,增加了总出行距离的限制.     

多级库存控制的连锁经营企业配送网络设计

研究了随机需求条件下连锁经营企业配送网络设计及其库存决策的联合优化问题.详细分析了基于POT(power of two)多级库存控制策略的连锁企业多级工作库存及订货成本,给出了门店及配送中心在满足给定服务水平条件下的安全库存成本.在综合考虑运输成本和配送中心选址成本的基础上,建立了以系统总成本最小为目标的配送系统总成本优化模型,并采用遗传算法求解该优化模型,在得到最优配送网络设计方案的同时,确定了配送中心订货周期及门店配送周期.通过算例验证了模型及算法的有效性,并分析了需求、运输距离和选址成本等因素的变化对系统总成本的影响,为连锁经营企业的物流配送网络设计及库存控制提供决策支持.     

考虑车速时空动态性的城市配送车辆路径问题

基于对城市路网中配送车辆车速时空动态性的分析,对配送时段和配送网络中的路段分别进行切割,建立城市配送网络的车速矩阵以实现动态计算车辆在配送线路上的行驶时间。基于车辆在配送网络节点间行驶时间的计算,建立了带时间窗的城市配送车辆路径优化模型。为更贴近实际,模型的目标函数在实际运作成本的基础上纳入了配送车辆的碳排放成本。设计了先用改进节约法构造初始解,再用变邻域搜索算法进行改进的求解算法框架。最后,通过实例分析表明了在车辆路径模型中纳入对车速时空性的考量能更好地刻画城市物流配送,给出更合理的配送车辆调度方案,实现提高配送效率、降低配送总成本和减少配送车辆碳排放的目的。     

预制构件生产-运输分批协同调度双目标优化

为了积极响应国家碳中和号召，通过更优的调度方案实现装配式建筑预制构件的柔性分批生产与运输，提高预制构件的生产运输效率。首先，根据装配式建筑的构件生产特点、运输方式建立以实现最大流程时间和最小惩罚成本为目标函数的生产-运输分批协同调度模型。然后，设计了求解该模型的多目标离散灰狼算法，并根据分批与车次上限合理设计编码解码方案。最后，代入实际案例验证了模型与算法的适配性，并将其与多目标粒子群算法进行性能指标评价对比，验证了多目标离散灰狼算法的可行性。结果表明，柔性划分小批次后进行生产运输能够实现预制构件的准时交付，有效改善预制构件的生产运输管理。     

考虑动态需求的生鲜商品物流配送优化方法研究

目的 针对生鲜商品配送过程中客户需求的变化,协调静态与动态客户需求之间的关系,合理规划路径,并降低物流总成本。方法 首先考虑客户生鲜需求的多样化温控区间、随机订单请求时间及动态需求量等因素,构建物流总成本最小化的整数规划模型。然后,设计基于高斯混合聚类的改进蚁群算法求解该模型,并提出动态需求处理策略,用于路径的再优化。其次,通过与粒子群算法、遗传算法和鲸鱼优化算法进行对比分析,验证文中设计算法的有效性。最后,以重庆市某生鲜配送网络为例,对比分析优化前后的运营指标,并探讨生鲜商品价值损失水平与物流总成本之间的关系。结果 经优化后,物流总成本下降了22.35%,其中惩罚成本、价值损失、配送成本和温控成本分别下降了39.84%、61.84%、29.80%、57.00%。结论 文中所提的模型、算法和动态需求处理策略可以合理规划配送路径,有效降低了总成本,为考虑动态需求的生鲜配送网络优化提供了参考。     

随机需求下半开放式冷链物流车辆路径优化

目的 降低冷链物流配送成本的同时,保证客户体验及碳排放达到企业要求。方法 综合考虑运输距离、客户软时间窗约束、碳排放、生鲜变质等因素,以制冷成本及对配送时间惩罚成本在内的总成本最低、碳排放量最低、生鲜产品新鲜度最高为目标,建立多目标生鲜配送路径优化模型,并设计模拟退火算法,以北京某冷链物流企业为例进行求解验证。结果 得出生鲜配送方案,通过配送模式对比表明多中心半开放式在降本和缩短路径长度方面更具优势,其中运输总费用和车辆行驶总距离相比于单中心独立配送模式分别降低了8.41%和36.36%。结论 需求不确定下,合理决策路由可在达到企业对生鲜产品新鲜度及碳排放标准的同时,有效降低配送成本。     

基于时间窗和多仓温控的生鲜商品配送车辆路径优化问题

目的 针对当前生鲜商品配送效率低和成本高等问题,采用车仓温度可控的多仓车辆作为配送装备,并结合时间窗等约束,研究基于时间窗和多仓温控的生鲜商品配送车辆路径优化问题。方法 建立最小化物流运营成本和车辆使用数量的双目标模型,然后设计基于Clarke-Wright节约算法的非支配排序遗传算法（CW-NSGA-Ⅱ）求解该模型。利用CW节约算法生成初始配送路径,以提高初始解的质量,并设计精英迭代策略,以提高算法的寻优性能。结果 基于改进的Solomon算例,将文中所提算法与多目标粒子群算法、多目标蚁群算法、多目标遗传算法进行了对比,验证了CW-NSGA-Ⅱ算法的求解性能。结合实例,对多仓车辆使用数量、温控成本和运营成本等指标进行对比分析,结果表明,经优化后多仓车辆使用数量减少了35.7%,温控成本减少了39.2%,物流运营总成本减少了47.7%。结论 文中所提模型和算法能够有效优化配送路径,降低运营成本,为构建高效率、低成本的生鲜配送网络提供了理论支持和决策参考。     

模糊需求下快速消费品生产配送协调调度

针对具有模糊需求的快速消费品生产配送问题,提出一种模糊线性规划模型,来解决多周期、多工厂、多产品、多配送中心的生产配送协调调度问题。同时考虑配送中心的库存水平,使总生产、库存成本和配送成本最小。最后,通过一个算例来证明该方法的可行性。     

A location-routing-inventory model for designing multisource distribution networks

This article studies a ternary-integration problem that incorporates location, inventory and routing decisions in designing a multisource distribution network. The objective of the problem is to minimize the total cost of location, routing and inventory. A mixed-integer programming formulation is first presented, and then a three-phase heuristic is developed to solve large-sized instances of the problem. The numerical study indicates that the proposed heuristic is both effective and efficient.     

An efficient method for dynamic-demand joint replenishment problem with multiple suppliers and multiple vehicles

How to improve competitive edges to meet rapidly changing market environment and dynamic customer needs is critical for the survival and success of firms these days. A good supply chain and inventory management is a necessity in the intensive competitive market. This paper considers a dynamic-demand joint replenishment problem with multiple vehicle routing. The problem is first formulated as a mixed integer programming model with an objective to minimise total costs, which include ordering cost, purchase cost, production cost, transportation cost and holding cost, under a prerequisite that inventory shortage is prohibited in the system. A particle swarm optimisation model is proposed next to solve large-scale problems which are computationally difficult. A case study of a touch panel manufacturer is presented to examine the practicality of the models.     

Two-echelon spare parts inventory system subject to a service constraint

In this paper, we consider a spare parts inventory problem faced by a manufacturer of electronic machines with expensive parts that are located at various customer locations. The parts fail infrequently according to a Poisson process. To serve customers when a failure occurs, the manufacturer operates a central warehouse and many field depots that stock spare parts. The central warehouse acts as a repair facility and replenishes stock at the field depots. There is a centralized decision-maker who manages the inventory in both the central warehouse and the field depots. We develop a continuous review, base stock policy for this two-echelon, multi-item spare parts inventory system. We formulate a model to minimize the system-wide inventory cost subject to a response time constraint at each field depot. We present an efficient heuristic algorithm and study its computational effectiveness.     

Modelling and analysis of a cooperative production network

In this study, we examine the cooperative production business model for a group of producers serving their own customers and also have access to external customers who can make an agreement to buy products at a lower price if a desired service level can be guaranteed. When the producers cannot meet the desired service level requirement of the external customers at the offered price on their own, they participate in a cooperative network. The network consolidates the external customers for its members and routes an arriving external customer to one of the participants. We determine the optimal production and rationing policies for each participating manufacturer as well as the optimal routing policy for the network. We also propose an accurate approximate method to analyse a network with a high number of homogeneous producers using a single queue approximation method. We show that, based on the parameters of the producers and the external market, the network can provide the desired service level for the external customers at the offered price and makes all the members increase their profit by better utilising their capacity and serving more external customers.     

Dual problem formulation and its application to optimal redesign of an integrated production–distribution network with structure dynamics and ripple effect considerations

In this paper, a dual problem formulation is developed in order to solve a multi-objective, multi-period and multi-commodity production–distribution (re)planning problem for a multi-stage centralised upstream network with structure dynamics considerations. The original idea of this study is to use the real logic of decision-making in companies and exclude demand constraint from the linear programming (LP) model. This allows transit from the classical transportation problem, formulated as an LP model in order to improve service level at the strategic inventory holding point. The formulation as an LP model makes it possible to formulate the dual problem. The performed structural analysis revealed some interesting managerial insights into building robust distribution plans and interconnecting decisions on distribution network design, planning, and sourcing.     

Vehicle routing concepts in the closed-loop container network of ARN—a case study

In this paper we discuss a real-life case study to optimize the logistics network for the collection of containers from end-of-life vehicle dismantlers in the Netherlands. Advanced planning concepts, such as dynamic assignment of dismantlers to logistic service providers, are analyzed using a simulation model. Based on this model, we periodically solve a vehicle routing problem to gain insight into the long-term performance of the system. The vehicle routing problem considered is a multi-depot pickup and delivery problem with alternative delivery locations. A special characteristic of the problem is the limited vehicle capacity of two containers. We solve this problem with a heuristic based on route generation and set partitioning.     

Vehicle routing and reverse logistics: The vehicle routing problem with simultaneous delivery and pick-up

Recently, the increasing focus on environmental protection has led to significant changes in logistics processes. In addition to the distribution process to the customers, re-usable packaging and goods to be recycled or remanufactured have to be transported in the reverse direction. If both tasks have to be performed simultaneously at the customers' locations which are serviced by a fleet of vehicles stationed in a depot or distribution/redistribution center, the vehicle routing problem with simultaneous delivery and pick-up arises. In this paper, the relation between this problem and other vehicle routing problems is investigated. A heuristic construction procedure is suggested. The proposed algorithm is successfully applied to a real-life problem as well as test instances introduced in the literature earlier. In addition, randomly generated instances are tackled in order to try to determine favorable settings for the parameters used in the solution approach.     

A design model and a production–distribution and inventory planning model in multi-product supply chain networks

Supply chain network (SCN) design implicates decision-making at a strategic level. That includes selecting the right suppliers and determining the number and the location of plants, distribution centres and retailers. An apt design model of the supply chain is imperative for the proper function of the supply chain and consequently for making better operational decisions in an attempt of a continuous improvement. In this paper, we propose two models. The first model is a mixed-integer linear programming model which is concerned with the SCN design problem, whereas the second operational model is a mixed-integer non-linear programming model in respect to the production–distribution and inventory planning problem in a supply chain network. The number of customers and suppliers as well as their demand and capacities are assumed to be known in both models. Two steady-state genetic algorithms were implemented in MATLAB in order to solve both the design and the operational model. The results were compared with GAMS. Some examples were devised in order to demonstrate potential ways of use for the designer of the supply chain network, as well as for the supply chain manager.     

Daily scheduling of caregivers with stochastic times

This paper addresses a daily caregiver scheduling and routing problem arising in home health care or home care service providers. The problem is quite challenging due to its uncertainties in terms of travel and service times derived from changes in road traffic conditions and customer health status in practice. We first model the problem as a stochastic programme with recourse, where the recourse action is to skip customers without services if the caregiver arrives later than their latest starting service time (i.e. hard time window requirements). Then, we formulate the problem as a set partitioning model and solve it with a branch-and-price (B&P) algorithm. Specifically, we devise an effective discrete approximation method to calculate the arrival time distribution of caregivers, incorporate it into a problem-specific label algorithm, and use a removal-and-insertion-based heuristic and the decremental state-space relaxation technique to accelerate the pricing process. Finally, we conduct numerical experiments on randomly generated instances to validate the effectiveness of the discrete approximation method and the proposed B&P algorithm.