大规模定制背景下可控提前期通用件安全库存优化算法

随着买方市场的形成,大规模定制生产模式已为企业广为采用,其中基于时间的竞争成为企业保持持续竞争优势的关键.通用件作为一种生产运作方式,它在降低企业内部的零部件多样性的同时,也促使企业向市场提供大量多样化的产品,提高响应市场的速度.合理控制提前期则能够减少安全库存和缺货损失、提高顾客服务水平和企业的竞争力.研究结合大规模生产特点,探索了模式下零件的组配流程及生产阶段,对可控提前期通用件安全库存管理方法进行了深入分析,构建了一个基于可控提前期的通用件安全库存模型及算法,并通过算例验证求得使提前期成本最低时的最优安全库存量.     

多阶段生产系统最优生产批量和原材料订购决策模型

Sarker和Parija(1996)建立了生产系统最优生产批量和原材料订购决策模型。然而他们的模型仅局限于单阶段生产系统,本文将他们的模型扩展到多阶段生产系统,我们首先建立了使整个多阶段生产系统总成本最小的各阶段最优生产批量、原材料订购批量及阶段之间的运输批量模型,然后分析了原材料订购费、半成品运费及设备安装费的敏感性。最后,我们结合实例综合分析了原材料订购费、半成品运输费和设备安装费的变化及最小值点取整后对原材料订购决策、最优生产批量和总成本的影响。     

有多种运输方式的供应链排序问题

本文考虑了由一个制造商和多个客户组成的供应链系统。每个客户有一个订单交给制造商加工,每个订单都有一个强制交货期。工厂采用承诺到货时间的发货方式,目标是在满足客户强制交货期的情况下,合理的安排订单的加工顺序,以极小化总的运输费用。本文考虑了多种情况,分别给出了相应的算法。     

基于时间敏感需求及随机完工期的承诺交货期决策

在新的经济形势和高新技术指引下,产品更新换代的速度加快,基于时间的竞争成为供应链竞争的焦点.在制造商为核心的供应链中,产品需求除了与价格有关外,与承诺交货期也有一定关联,且部分客户愿意为快速交货而支付更高价格.当需求与时间及价格具有敏感性且实际完工期服从一定的随机分布时,建立利润最大化及服务水平约束的承诺交货期决策模型,并对模型进行讨论及优化分析.通过算例验证了模型的有效性,通过参数敏感性分析得出的结论是:当客户服务水平达到一定阈值时,最优承诺交货期将发生改变;价格及交货期敏感系数不影响承诺交货期,但影响产品需求及最终利润;最优承诺交货期与单位提早完工成本是反向变动的关系而与单位延迟完工成本是正向变动的关系;随着完工期均值及标准差的不断增大,最优承诺交货期呈上升趋势,利润、市场需求及价格不断下降.     

ERW钢管考虑动态库存和交货期的多阶段生产计划优化方法研究

多阶段生产计划在钢管制造企业中起着重要的作用,在学术研究中也具有一定的复杂性.由于企业库存与生产及计划密切相关、订单交货期对生产和库存又具有很强的关联性,在研究中将库存的动态性和交货期纳入多阶段生产计划中,试图寻求找到优化的生产计划的方法.根据ERW直缝焊管企业的生产特点,对其生产计划的制订方式进行了优化.针对多阶段生产计划建立了考虑库存和交货期的提前/拖期惩罚数学模型,通过遗传算法对其进行求解及优化,从而得到生产计划的较优解.     

ERW钢管考虑动态库存和交货期的多阶段生产计划优化方法研究北大核心

多阶段生产计划在钢管制造企业中起着重要的作用,在学术研究中也具有一定的复杂性.由于企业库存与生产及计划密切相关、订单交货期对生产和库存又具有很强的关联性,在研究中将库存的动态性和交货期纳入多阶段生产计划中,试图寻求找到优化的生产计划的方法.根据ERW直缝焊管企业的生产特点,对其生产计划的制订方式进行了优化.针对多阶段生产计划建立了考虑库存和交货期的提前/拖期惩罚数学模型,通过遗传算法对其进行求解及优化,从而得到生产计划的较优解.     

不确定条件下双目标组批生产的交货期设置研究

交货期是调度方法的函数,因而具有不确定性.研究变批量、变批次、变生产能力下,单阶段、双目标有条件相容组批的交货期设置问题,将它转化为订单投放策略和调度模式研究.建立了一个基于目标的双目标订单投放策略数学模型.采用目标序列优先方法进行双目标求解,用两种调度模式求出区间值,进行最优交货期逼近.模式1:松弛掉产品加工约束条件,基于负荷考虑、给出离散生产模式下订单完工率最大的订单排序算法,算法综合考虑了任务紧急程度、可调度性、重要度和流程时间最短四个方面,得到区间的一个端点.模式2是有条件相容的启发式组批调度算法,即通过聚类计算将订单安排问题转化为多队列调度问题,将新来订单的投放转化为某个队列的插单和批量分割问题,不同队列中批的投产顺序由批中优先级最高的订单决定,并在能力约束下进行批量分割计算,得到区间的另一个端点,结合流程可靠性求出区间.实例证明,模式2的交货期设置小,订单完工率和生产率高.     

易变质产品的可替代库存模型的最优订购策略研究

研究了易变质产品的可替代库存模型.在有限计划期内,供应商面临两种不同产品的需求,当一种产品发生短缺时,另一种产品可以以一定的替代率代替短缺产品.通过分析系统的总成本函数的性质,提出了最优订购策略.最后通过算例验证了算法的最优性.     

考虑质量偏好的两阶段服务供应链质量控制策略

研究了一个集成商和一个提供商所组成的服务供应链的质量控制问题。考虑集成商的质量缺陷承诺和提供商的质量偏好,并将提供商的质量活动区分为质量改进和质量保持两个阶段,分别建立了服务供应链成员的效用函数,同时考虑服务供应链成员的竞争与合作,运用博弈理论,优化得到单阶段与两阶段时,集成商的最优服务价格和质量缺陷承诺,以及提供商的最优服务质量改进程度。最后,通过对比分析及数值仿真发现,考虑两阶段质量控制较单阶段情形,更有利于服务供应链较长期限内获得更多效用。其次,服务提供商质量偏好对服务供应链单阶段与两阶段下的最优效用和质量控制策略均产生一定影响。     

一个带有增加量价格折扣的两货栈库存模型

已有的确定性两货栈(其中一个是自己货栈(OW);另一个是租用货栈(RW))库存模型通常不考虑增加量价格折扣,然而在实际生活中,增加量价格折扣却是促使库存管理者加大订货量的一个重要原因.本文通过考虑增加量价格折扣而将两货栈系统作了进一步扩展,在采用间隔式运输模式运送RW的物品到OW的情形下,建立了一个带有增加量价格折扣并允许短缺的两货栈库存模型,提供了一种寻求最优库存策略的简单方法.     

Integrating operations and marketing decisions using delayed differentiation of products and guaranteed delivery time under stochastic demand

In this research, we integrate the issues related to operations and marketing strategy of firms characterized by large product variety, short lead times, and demand variability in an assemble-to-order environment. The operations decisions are the inventory level of components and semi-finished goods, and configuration of semi-finished goods. The marketing decisions are the products price and a lead time guarantee which is uniform for all products. We develop an integrated mathematical model that captures trade-offs related to inventory of semi-finished goods, inventory of components, outsourcing costs, and customer demand based on guaranteed lead time and price.The mathematical model is a two-stage, stochastic, integer, and non-linear programming problem. In the first stage, prior to demand realization, the operation and marketing decisions are determined. In the second stage, inventory is allocated to meet the demand. The objective is to maximize the expected profit per-unit time. The computational results on the test problems provide managerial insights for firms faced with the conflicting needs of offering: (i) low prices, (ii) guaranteed and short lead time, and (iii) a large product variety by leveraging operations decisions.     

A multi-product continuous review inventory system with stochastic demand,backorders, and a budget constraint

Common characteristics of inventory systems include uncertain demand and restrictions such as budgetary or storage space constraints. Several authors have examined budget constrained multi-item stochastic inventory systems controlled by continuous review policies without considering marginal shortage costs. Existing models assume that purchasing costs are paid at the time an order is placed, which is not always the case since in some systems purchasing costs are paid when orders arrive. In the latter case the maximum investment in inventory is random since the inventory level when an order arrives is a random variable. Hence payment of purchasing costs on delivery yields a stochastic budget constraint for inventory. This paper models a multi-item stochastic inventory system with backordered shortages when estimation of marginal backorder cost is available, and payment is due upon order arrival. The budget constraint can easily be converted into a storage constraint.     

The costs and benefits of commonality in assemble-to-order systems with a (Q,r)-policy for component replenishment

In assemble-to-order systems, it has been shown that replacing a number of specific components by a smaller number of general-purpose, common components can reduce required safety stock levels due to the benefits of risk pooling. Previous research using single-period models has shown that even if the common components are somewhat more expensive than the unique components they replace, the benefits of risk pooling often outweigh the added purchasing costs. However, this has been shown often not to be the case with multiple-period models – in the long run, the added purchasing costs dominate the benefits of risk pooling. However, both single- and multiple-period models ignore one of the benefits of commonality – order pooling. With commonality, demand is pooled into a smaller number of components, reducing the required number of orders (or setups). After re-optimizing the order quantities and order intervals, both the ordering costs and cyclic carrying costs are reduced. This paper develops a model to consider the assemble-to-order environment where components are replenished according to a (Q,r)-policy. Results show that order pooling is a significant benefit; in many cases it is much more important than the risk-pooling benefit. In contrast to multiple-period models, there is often a total cost benefit with commonality, even when the common component is several percent more expensive than the unique components it replaces.     

A multi-product continuous review inventory system in stochastic environment with budget constraint

Common characteristics of inventory systems include uncertain demand and restrictions such as budgetary and storage space constraints. Several authors have examined budget constrained multi-item stochastic inventory systems controlled by continuous review policies without considering marginal review shortage costs. Existing models assume that purchasing costs are paid at the time an order is placed, which is not always the case since in some systems purchasing costs are paid when order arrive. In the latter case the maximum investment in inventory is random since the inventory level when an order arrives is a random variable. Hence payment of purchasing costs on delivery yields a stochastic budget constraint for inventory. In this paper with mixture of back orders and lost sales, we assume that mean and variance of lead time demand are known but their probability distributions are unknown. After that, we apply the minimax distribution free procedure to find the minimum expected value of the random objective function with budget constraint. The random budget constraint is transformed to crisp budget constraint by chance-constraint technique. Finally, the model is illustrated by a numerical example.     

How useful is commonality? Inventory and production decisions to maximize survival probability in start-ups

This paper deals with component commonality in start-up manufacturingfirms. We present a two-product Markov decision model that examinesthe implications of the inventory and production strategiesfor the survival probability of the firm. The advantage of usingcomponent commonality is studied for varying costs, demand correlationsand order replenishment lead times. Optimal policies are derived,and minimum stock levels for survival are obtained. Moreover,we state the conditions under which simplified production decisionscan be made. It is shown that commonality is not only usefulas a way of dealing with demand uncertainty, but that its increaseduse is preferred for strongly substitutable products, and shorterreplenishment lead times.     

Disassembly EOQ models with price-sensitive demands

The disassembly economic order quantity problem is to determine the quantities of a product to be disassembled at different times over an infinite planning horizon by considering ordering, operation, and inventory costs. The demands for the components are independent, which can lead to accumulations of unnecessary inventories over time. This article proposes the models which integrate price-sensitive demands and disposal decisions in disassembly economic order quantity problems to maximize the profit of disassembly systems without inventory accumulations. Three models are developed and analyzed to obtain solution approaches that give prices, the replenishment cycle time (or, equivalently, the order quantity), and the disposal quantity. The inventory policy integrating both pricing and disposal decisions allows higher profits to be achieved. A numerical experiment shows its efficiency and highlights its potential implementation in practical cases.     

Cost Comparisons for Out-of-Phase Inventory Models

Inventory costs for a fixed time period have traditionally been determined by allocating total costs per cycle uniformly throughout that cycle as well as any partial cycles. This procedure for cost allocation has led to the solution of numerous inventory problems, most notable of which is the anticipated price-increase model. When comparing two out-of-phase inventory models, if costs are accounted for when they occur over a fixed planning horizon, inventory policies should be changed to reflect the impact of this different cost-allocation procedure. For the anticipated price-increase model, the ‘optimal’ order quantity as well as the implied savings in inventory costs will be different when cost models are developed based on these different cost-allocation methods. If the objective is to maximize over a fixed planning horizon the actual savings in inventory costs as they occur, the cost models presented here should be used.     

Analysis of an industrial component commonality problem

We discuss a case study of an industrial production-marketing coordination problem involving component commonality. For the product line considered, the strategic goal of the company is to move from the current low volume market to a high volume market. The marketing department believes that this can be achieved by substantially lowering the end products’ prices. However, this requires a product redesign to lower production costs in order to maintain profit margins. The redesign decision involves grouping end products into families. All products within one family use the same version of some components. This paper fits in the stream of recent literature on component commonality where the focus has shifted from inventory cost savings to production and development cost savings. Further, we consider both costs and revenues, leading to a profit maximization approach. The price elasticity of demand determines the relationship between the price level and number of units sold. Consequently, we integrate information from different functional areas such as production, marketing and accounting. We formulate the problem as a net-present-value investment decision. We propose a mixed integer nonlinear optimization model to find the optimal commonality decision. The recommendation based on our analysis has been implemented in the company. In addition, the application allows us to experimentally validate some claims made in the literature and obtain managerial insights into the trade-offs.     

A branch and bound algorithm for disassembly scheduling with assembly product structure

This paper considers a production planning problem in disassembly systems, which is the problem of determining the quantity and timing of disassembling end-of-use/life products in order to satisfy the demand of their parts or components over a planning horizon. The case of single product type without parts commonality is considered for the objective of minimizing the sum of setup and inventory holding costs. To show the complexity of the problem, we prove that the problem is NP-hard. Then, after deriving the properties of optimal solutions, a branch and bound algorithm is suggested that incorporates the Lagrangean relaxation-based upper and lower bounds. Computational experiments are performed on a number of randomly generated problems and the test results indicate that the branch and bound algorithm can give optimal solutions up to moderate-sized problems in a reasonable computation time. A Lagrangean heuristic for a viable alternative for large-sized problems is also suggested and compared with the existing heuristics to show its effectiveness.     

Optimal inventory policies with two modes of freight transportation

Theoretical inventory models with constant demand rate and two transportation modes are analyzed in this paper. The transportation options are truckloads with fixed costs, a package delivery carrier with a constant cost per unit, or using a combination of both modes simultaneously. Exact algorithms for computing the optimal policies are derived for single stage models over both an infinite and a finite planning horizon.