多配送中心车辆路径问题的狼群算法

针对多配送中心动态启用和车辆的合理分配,文章首先建立了以总路径长度最小为目标函数的多配送中心车辆路径问题的数学模型;其次,根据多配送中心车辆路径问题的具体特征,模拟狼群捕食行为设计了求解该问题的狼群算法;最后,应用狼群算法求解测试算例,并将其计算结果与几种常见智能优化算法的计算结果进行比较,验证了狼群算法求解多配送中心车辆路径问题的可行性与有效性。     

考虑配送中心弹性库存的果品采购优化模型

为保证果品质量与降低运输成本,基于小批量多频次采购方式,提出考虑配送中心弹性库存的果品采购优化问题。以水果超市配送中心向多个供应商采购不同品种果品为对象,将库存变化因子引入周期性路径优化模型,建立果品采购优化模型,并设计一种两阶段求解的混合启发式算法。结合算例测试模型与算法的可行性,与已知最优解比较,目标解偏差约为2%,最大为3.3%,经验证,供应商和采购期数量在一定取值范围内,算法具有竞争优势,有利于改善超市物流中心的小批量多频次采购决策能力。     

两级系统多周期随机库存路径优化

库存和运输是物流活动的最主要环节,如何同时对二者进行优化,是供应商管理库存必须解决的核心问题。针对一个配送中心为多个零售商实施统一配送的两级系统,以最小化系统计划期平均总费用为目标,分析包括零售商存储费、缺货费,以及配送中心进货费、存储费和配送费的系统全部费用组成,整合库存与配送建立了库存路径问题数学模型。根据零售商需求的随机性和库存路径问题本身的两阶段性,估计零售商库存上下限,选择配送中心订货策略及配送优先原则,借鉴旅行商问题求解思路,设计了求解问题的启发式算法。用Matlab7.0编程实现仿真算例求解表明,整合优化比分别优化节省平均总费用6.2%。     

.物流配送中心动态选址模型及算法研究*

针对传统物流配送中心动态选址模型没有充分考虑配送中心的可能状态和库存持有成本的问题,建立了一种新的模型。首先,利用两步骤近似法构建了在有库存和运输双重能力约束下,每一个周期配送中心的库存成本计算方法;然后,分别给出了配送中心在整个规划期内的打开、运营、关闭和再次打开的成本表达式;最后,分别用遗传算法、克隆选择算法、粒子群算法求解所建立的模型,并从算法的寻优能力、稳定性、运算速度和收敛性方面比较了三种算法的性能。算例测试结果表明,所建立的模型是有效的;从总体上看,遗传算法的适应性要强于克隆选择算法和粒子群算     

整合供应商选择的多层级选址-库存模型

针对多级供应链网络设计中选址和库存一体化决策问题,基于梯级库存策略,建立了整合供应商选择的多层级选址-库存模型。模型以网络中供应商的选择成本、工厂和配送中心的打开成本、层级之间的运输成本、库存成本、采购成本和生产成本之和最小为目标,同时对供应商的选择、工厂和配送中心的选址、配送中心对顾客的分配、层级之间的运输量、工厂和配送中心的订货批量进行决策。为了求解所建立的模型,设计了基于部分编码的粒子群优化算法。20个不同规模的算例测试表明：所建立的模型是有效的,能用于多层级供应链网络的设计;所设计的算法无论是在求解精度,还是在运算速度上都明显优于数学优化软件Lingo 9.0,尤其是当供应链网络中总节点数较大时。     

一种需求率随时间变化且允许缺货的库存模型

针对库存管理和控制中存在的实际问题,在最大程度降低库存运营成本的前提下,综合考虑了货物损坏率以及缺货率引起的损失等多种影响,提出了一个需求率为一般连续函数的库存模型,并进一步建立了该模型的两阶段优化方法.该方法通过简单的求解过程,就能够找到最优订货策略,从而有效的降低了库存成本.最后运用一个简单的数据实验,证明了该模型和方法的有效性和准确性.该库存成本函数模型及其求解方法也可以适用于需求率是非单调的随时间变化函数的模型.     

连锁门店选址与配送中心选择联合决策的f-MIGP模型

建立了连锁门店选址和配送中心选择联合决策问题的模糊多目标混合整数规划模型．针对该模型的特殊结构。提出一种适用的求解策略：首先确定每个模糊目标的隶属度函数;然后将模糊多目标混合整数规划模型转化为等价的清晰多目标混合整数规划模型,通过最大最小算子求出目标值;最后借助于两阶段算法,求出问题的最优解．通过应用算例进一步说明了该模型的有效性和可行性．     

有租借仓库并允许缺货的随机存贮模型

库存管理中已经存在的EOQ、(S,s)等经典模型,尚无法解决“有租借仓库”、“允许缺货”和“随机交货”并存的存贮管理问题。本文提出了针对上述三个条件并存情况下的存贮模型,将统计意义下单位时间损失费用作为指标函数来评价该方案,并将单品种固定库存总量随机存贮管理模型推广到多品种固定库存总量随机存贮管理模型中,给出了非线性规划模型。最后给出实例,采用优化算法在MATLAB下对模型求解。     

带有时间窗的多配送中心车辆调度问题研究

在分析时间窗的惩罚函数基础上,建立了带有时间窗的多配送中心车辆调度模型,针对模型设计了两阶段求解算法,先通过扫描算法把客户化分到不同的配送中心负责配送,然后采用改进的遗传算法求解带有时间窗的单配送中心的车辆调度模型,最后,结合算例仿真计算验证了算法的有效性。     

冷链物流多层级配送中心连续选址模型构建

传统物流配送中心选址方法在设计过程中并没有考虑到要将选址问题进行转化,无法获得最优选址结果.为改善上述问题,构建了一种冷链物流多层级配送中心连续选址模型.设计配送中心选址流程,设置配送费用最少与配送时长最短的目标函数及相关约束条件,并将连续选址问题转换为多源Weber问题,采用启发式算法对其进行求解,根据备选策略数量,...     

随机需求下的选址一库存配送系统集成规划模型及算法

研究了随机需求条件下由单供应商、候选分拨中心和分销点构成的选址-库存问题,分销点、分拨中心分别基于周期检查(R,s,Q)和连续检查(s,S)库存控制策略．综合考虑库存成本、运输成本和设施成本之间的均衡关系,建立了二级库存与无能力约束选址集成规划模型．给出了适合求解实际规模问题的拉格朗日松弛算法,提出了求解子问题的有效启发式方法,改进了次梯度优化方法．通过仿真试验验证了模型的正确性和算法的有效性．最后讨论了相对于传统规划方法,需求方差、服务水平、持有成本、提前期等关键库存控制参数对系统运营成本节约的影响规律．     

随机需求下的选址-库存配送系统集成规划模型及算法

研究了随机需求条件下由单供应商、候选分拨中心和分销点构成的选址－库存问题, 分销点、分拨中心分别基于周期检查(R, s,Q)和连续检查(s, S)库存控制策略. 综合考虑库存成本、运输成本和设施成本之间的均衡关系, 建立了二级库存与无能力约束选址集成规划模型. 给出了适合求解实际规模问题的拉格朗日松弛算法, 提出了求解子问题的有效启发式方法, 改进了次梯度优化方法. 通过仿真试验验证了模型的正确性和算法的有效性. 最后讨论了相对于传统规划方法, 需求方差、服务水平、持有成本、提前期等关键库存控制参数对系统运营成本节约的影响规律.     

Joint shipment consolidation and inventory decisions in a two-stage distribution system

This study is motivated by a problem that an industrial distributorship faced while distributing automotive spare parts to service and repair centers. Considering the problem encountered, we present an analytical model for joint inventory and shipment consolidation decisions in a two-stage distribution system with a single distribution center, multiple non-identical retailers, and an outside supplier. The retailers face stochastic end-customer demand and use continuous review to replenish inventories. On the other hand, the distribution center uses a periodic review policy and employs a time-based shipment consolidation policy to dispatch retailers’ accumulated orders at the end of each consolidation cycle. We present an exact optimization technique to compute the optimal replenishment quantity at the distribution center, order-up-to level at retailers, and a shipment consolidation cycle length to measure the effects of inventory at retailers on the overall performance. Finally, we perform numerical experiments to measure the impact of various parameters on the overall distribution system.     

An integrated local search method for inventory and routing decisions

The present article studies an inventory routing model which integrates two important components of the supply chain: transportation logistics and inventory control. The distribution system examined consists of customers that face product demand at a deterministic and constant rate. Customer demand is satisfied by a fixed vehicle fleet located at the central depot. The aim of the problem is to determine the timing and size of the replenishment services together with the vehicle routes, so that the total transportation and inventory holding cost of the system is minimized. In methodological terms, we propose a solution approach applying two innovative local search operators for jointly dealing with the inventory and routing aspects of the examined problem, and Tabu Search for further reducing the transportation costs. The proposed algorithmic framework was tested on a set of new benchmark instances of various scales. It produced satisfactory results both in terms of effectiveness and robustness.     

Modelling of the inventory replenishment problem with a two-segment piecewise linear demand

The classical inventory replenishment problem with a linear function in demand uses a ‘single-segment’ linear function as its demand and can be modelled by a simple algorithm. Moreover, this article extends the algorithm to provide a heuristic solution for the inventory replenishment model with a two-segment linear function in demand called the ‘two-segment piecewise linear demand model’. In addition, this article proposes a general procedure for solving both models. Meanwhile, several examples taken from the literature illustrate our algorithm for these two models with convincing results. Furthermore, this study shows that when the demand is a two-segment piecewise linear function over time, it is better to use the proposed algorithm rather than devising a decoupled solution approach by treating segments separately. Finally, a sensitivity analysis of two factors, demand and cost, is performed. The model is highly extensible and applicable, so it can serve as an inventory planning tool to solve the replenishment problem.     

A heuristic method for the inventory routing and pricing problem in a supply chain

The inventory routing problem (IRP) in a supply chain (SC) is to determine delivery routes from suppliers to some geographically dispersed retailers and inventory policy for retailers. In the past, the pricing and demand decisions seem ignored and assumed known in most IRP researches. Since the pricing decision affects the demand decision and then both inventory and routing decisions, it should be considered in the IRP simultaneously to achieve the objective of maximal profit in the supply chain. In this paper, a mathematical model for the inventory routing and pricing problem (IRPP) is proposed. Since the solution for this model is an NP (non-polynomial) problem, a heuristic method, tabu search adopting different neighborhood search approaches, is used to obtain the optimal solution. The proposed heuristic method was compared with two other methods considering the IRPP separately. The experimental results indicate that the proposed method is better than the two other methods in terms of average profit.     

Some remarks on an optimal order quantity and reorder point when supply and demand are uncertain

In the reports in the literature on inventory control, the effects of the random capacity on an order quantity and reorder point inventory control model have been integrated with lead time demand following general distribution. An iterative solution procedure has been proposed for obtaining the optimal solution. However, the resulting solution may not exist or it may not guarantee to give a minimum to the objective cost function, the expected cost per unit time. The aim of this study was to introduce a complete solution of the order quantity/reorder point problem, optimality, properties and bounds on the optimal order quantity and reorder point. The two most appealing distributions of lead time demand, normal and uniform distributions, in conjunction with an exponentially distributed capacity, are used to illustrate our findings in determining the optimal order quantity and reorder point.     

Price sensitive demand with random sales price – a newsboy problem

Up to now, many newsboy problems have been considered in the stochastic inventory literature. Some assume that stochastic demand is independent of selling price (p) and others consider the demand as a function of stochastic shock factor and deterministic sales price. This article introduces a price-dependent demand with stochastic selling price into the classical Newsboy problem. The proposed model analyses the expected average profit for a general distribution function of p and obtains an optimal order size. Finally, the model is discussed for various appropriate distribution functions of p and illustrated with numerical examples.     

A computational study for common network design in multi-commodity supply chains

In this paper, we study a supply chain network design problem which consists of one external supplier, a set of potential distribution centers, and a set of retailers, each of which is faced with uncertain demands for multiple commodities. The demand of each retailer is fulfilled by a single distribution center for all commodities. The goal is to minimize the system-wide cost including location, transportation, and inventory costs. We propose a general nonlinear integer programming model for the problem and present a cutting plane approach based on polymatroid inequalities to solve the model. Randomly generated instances for two special cases of our model, i.e., the single-sourcing UPL&TAP and the single-sourcing multi-commodity location-inventory model, are provided to test our algorithm. Computational results show that the proposed algorithm can solve moderate-sized problem instances efficiently.     

需求不确定下的低碳物流配送中心选址

针对低碳物流配送中心选址问题,结合需求不确定的现实问题,采用随机规划理论,建立以包含碳排放成本在内的总成本最低为目标、以不确定性需求为随机约束的选址模型,通过实例运用验证模型的有效性,能够为企业在实际市场需求波动下的选址投入以及收益提供一定的参考价值.另外,对不确定性需求的置信水平和标准差进行灵敏度分析,案例分析结果显示:碳排放因素与需求的不确定性对配送中心选址结果存在较为显著的影响;选址总成本与需求的不确定程度密切相关,碳排放成本也会随着需求的变动产生较大的波动.