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。     

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.     

考虑碳排放的冷链物流联合配送路径优化

基于绿色物流发展理念,将碳排放转化为成本融入冷链物流配送路径优化问题。结合冷链物流企业拥有多个配送中心且各配送中心在各决策阶段运力不均衡的实际情况,提出一种在企业拥有多个配送中心、有限车辆数且各车辆可回到任一配送中心继续配送的情况下,考虑时间窗、碳排放成本和货损成本的冷链物流联合配送路径优化模型。通过引入一个虚拟车场将多配送中心问题转化为单配送中心问题,采用改进遗传算法求解路径优化问题。通过算例将传统分区配送方式与联合配送方式的路径优化结果比较发现:联合配送不仅在冷链配送费用方面远远低于传统分区配送,而且产生的碳排放成本也低于分区配送。     

考虑时间窗的船用钢材加工配送中心选址规划

船用钢材加工配送中心选址规划是钢材供应链优化设计研究的重要内容。针对船用钢材配送不及时,难以满足多规格成套配送需求问题,建立了包含惩罚成本的多配送中心选址规划模型。该模型以钢材运输成本、配送中心运营固定成本和变动成本、配送不及时惩罚成本之和作为优化目标,综合考虑了配送中心容量约束、船厂对于钢材配送的时间要求,以某造船集团为例进行算例分析,结果表明:采用差分改进粒子群算法求解比传统分支定界法得出的选址规划结果更有效。     

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。     

引入补偿、惩罚策略的配送中心选址问题研究

根据配送中心与零售商在市场中的相对地位,针对保持市场份额或降低物流成本不同目标,配送中心对远距离零售商可制定补偿或惩罚两种策略。在LMRPVCC问题优化模型基础上,在目标函数与约束条件中引入运输补偿、惩罚成本项及服务半径Dr,可将模型扩展为引入补偿、惩罚策略的LMRPVCC选址-库存问题的非线性整数规划模型。利用所设计的粒子群算法对Hakimi文章中10节点算例和Daskin和Shen的文章中的49节点求解,对惩罚、补偿系数W、服务半径Dr进行敏感性分析的结果表明,不同配送策略对配送中心物流总成本、各分项成本以及配送中心满意区位有重要影响。     

发动机装配线关键工位物料动态配送方法

目的 为降低装配车间线边物料配送及库存管理成本,提高配送环节对生产节拍变动的适应性,设计一种针对关键工位重点物料的动态配送方法.方法 首先,采用K-means算法考虑物料价值、包装体积、通用性进行初步分类,结合需求变异系数确定出关键工位重点物料;其次,结合发动机装配线生产特点,设计基于动态配送时间和数量的物料配送方法,并建立包括运输成本和线边库存成本的配送总成本模型;最后,通过Flexsim软件搭建生产物流仿真模型,分别模拟Z车间现有配送方法和所设计方法进行对比验证.结果 实行动态配送后,重点物料的平均库存容量和持有库存成本均得到有效降低,最大降幅分别为23.91％和23.96％.结论 结果表明所提方法能够提高车间物料管理的针对性,降低总配送成本,提升配送系统的柔性化.     

配送中心成本优化决策支持系统的开发

物资配送是物流管理的核心工作。合理选择一个配送中心，优化物流网络，减少配送成本是配送中心管理现代化的关键。作者研究开发的配送中心物流成本优化决策支持系统为配送管理提供了一个有力的支持工具。     

多配送中心冷链运输路径优化

目的 在降低配送成本的同时,保证生鲜配送的顾客满意度。方法 综合考虑运输距离、配送时间窗约束、生鲜变质等因素,以运输成本、制冷成本、车辆调用成本、配送中心开放和闲置成本及自提点对配送时间和生鲜品质的满意度为目标函数,建立双目标生鲜配送路径优化模型。结果 利用非支配排序遗传算法求得了不同需求情况下的生鲜配送方案。结论 在不同的需求状况下,合理选择开放配送中心能够在保证一定顾客满意度的情况下,有效降低了配送成本。     

考虑缺货的两阶段腐败一体化库存—路径模型

在一个生产商给多个零售商配送单一易腐品的供应链中,考虑了一体化库存路径决策问题,并且零售商为了减少库存和运输相关成本而采用缺货损失策略。产品使用同质车辆配送,而在运输及销售过程中产品具有不同的腐败速率(两阶段腐败)。通过构建数学模型以最小化包括生产、配送、腐败、库存、缺货成本在内的供应链总成本。针对供应链库存决策,用数学证明得到函数性质以确定零售商缺货时间、订货周期及生产商的生产周期。针对离散的路径决策,采用固定分区策略对零售商进行分组,在每个分组内采用遗传算法求解对应的运输策略。设计求解算法并编制matlab程序求解问题,结合数值算例和灵敏度分析说明了模型的有效性及相关管理启示。     

Just-in-time purchasing: an integrated inventory model involving deterministic variable lead time and quality improvement investment

Nowadays supply chain management is a popular practice in manufacturing systems, and just-in-time (JIT) production plays a crucial role in supply chain environments. Companies are using JIT production to gain and maintain a competitive advantage. The characteristics of JIT systems are consistent high quality, small lot sizes, frequent delivery, short lead time, and close supplier ties. This paper presents an integrated inventory model to minimize the sum of the ordering/setup cost, holding cost, quality improvement investment and crashing cost by simultaneously optimizing the order quantity, lead time, process quality and number of deliveries while the probability distribution of the lead time demand is normal. This integrated inventory model is useful particularly for JIT inventory systems where the vendor and the purchaser form a strategic alliance for profit sharing.     

考虑订单合批批量的在线制造商承诺交货期决策研究

承诺交货期策略即向所有顾客承诺一致的交货时间是在线制造商最常用的基于时间的竞争策略。制造商通过调整运营参数来改变交货时间以实现向顾客承诺的交货期。基于运营批量对生产提前期的影响,本文构建了考虑订单合批批量的承诺交货期决策模型,分析了各种参数对生产提前期与运输提前期决策的影响。研究表明,库存成本、单位产品的加工时间、顾客需求的价格敏感性和时间敏感性系数等参数对于在线制造商的生产提前期和运输提前期决策往往具有不同的影响。最后本文的算例验证了研究的结论。     

Designing and computing optimal policies on a production model for non-instantaneous deteriorating items with shortages

This paper discusses a production model for non-instantaneous Weibull deteriorating items with complete backlogging over an infinite time horizon and also extended to the finite time horizon. The model is studied under the production policy starting with shortages. The objective of this model is to minimise the total cost to the manufacturer which is the sum of the setup cost, production cost, holding cost and shortage cost. Finally, numerical examples are given to illustrate the real-life applications of the proposed model. Sensitivity analyses of the major inventory parameters are carried out. Some useful managerial applications are obtained by using sensitivity analysis.     

Simulating alternative production policies with sequence-dependent costs

This article studies various sequencing and inventory rules in a manufacturing environment with nonlinear technological coefficients and stochastic demand. Multiple products require setup on a single machine and setup time and setup cost decrease with repeated setups. Furthermore, setup operations for different products have common components and an item can benefit from the setup operation of another item. The single-level, multi-item lot size model is used to model the production environment. The learning curve is used to represent this decrease in setup time with repeated setups. The learning transmission between items affects the scheduling of the products and the resulting model considers simultaneous decisions about lot sizing and sequencing in a nonlinear formulation. The problem is formulated and various production policies are simulated. Two sequencing rules and four inventory rules are examined. A simulation experiment of 6400 runs is used to compare the schedules produced by simple policies and those produced by more involved ones. A statistical analysis of the simulation results indicates that the simple rules perform equally well and in some cases better than the computationally harder rules.     

Joint production and shipment lot sizing for a delivery price-based production facility

A joint economic production quantity (EPQ) and delivery quantity model for a production system is investigated in this paper. More specifically, an EPQ policy is implemented in the production system, while a smaller shipping quantity is periodically dispatched to the customer. The production system is also responsible for the shipment cost, i.e. a delivery price-based procurement from the customer. The considered cost includes setup cost to launch the batch production, inventory carrying cost, and transportation cost, where the transportation cost is a function of the delivery quantity. A per unit time cost model is developed and analysed to determine the optimal production and delivery quantities. Under some mild conditions, it can be shown that the joint cost function is convex with respect to the production quantity; and the number of delivery is an integer in each replenishment cycle. Computational study has demonstrated the significant impact of the joint decision model on the operating cost. In particular, the reduction in total cost can be more than 15% when inventory carrying costs, and/or transportation costs, are high.     

生物质直燃发电厂仓库规模动态规划

生物质直燃电厂仓库以及收储站的规模大小与库存水平,对于降低电厂的固定成本和运营成本至关重要。为解决这些问题,建立了电厂仓库、收储站规模与其库存控制联合动态规划优化模型。以30 MW生物质电厂为例,根据模型及其相关参数的敏感性分析,得到了一些结论,比如,在使用多种生物质燃料情形下,电厂仓库、收储站的最优规模分别为35 887 m2、24 147 m2;当收储站和电厂的安全库存分别在6 000 t以下和17 000 t以下时,总费用会有降低,否则会相反。使用单一生物质燃料有类似的结论。     

A mathematical model for the economic justification of setup time reduction

A justification model for setup time reduction is presented. The model includes inventory carrying cost, setup cost, storage cost, setup time reduction cost, and quality cost. The level of setup time reduction which yields the lowest total cost indicates the extent to which setup time reduction is economically justified. A case study is presented based on one machine and one product. Results indicate that unlimited setup time reduction is not always justified based on the analysis of one product. However, a greater level of reduction is justified if the lot size is reduced appropriately as setup time is reduced.     

Economic lot scheduling with lost sales and setup times

We introduce a new modeling framework for the classic economic lot scheduling problem that permits lost sales if they lead to higher profits. The model also accounts for setup times at a facility, but assumes no explicit incremental setup cost in the objective. Despite assumptions of deterministic demands, production rates and setup times, the cost of carrying inventory may make lost sales during a cycle economically attractive. Statistical analysis on randomly generated problems ranging in size from 100 to 1000 products indicates that the computation time grows by the square of the number of products.