价格影响需求的易变质产品动态定价模型

在产品销售价格影响需求的条件下,利用最优控制理论建立了易变质产品的动态定价模型,目标是最大化产品销售周期内总的销售利润。利用Pontryagin 最大值原理得到了产品销售价格的最优性条件。通过对模型的理论分析得出如果产品销售价格介于单位产品购买费用和产品销售价格上限之间,且产品库存在销售周期结束之前始终为正时,销售周期内各时刻的产品最优销售价格一定大于与相应时刻变质率和产品单位库存成本有关的一个下界,销售周期内各时刻的产品最优库存水平一定小于与相应时刻变质率和产品单位库存成本有关的一个上界。     

需求随机的变质性产品生产计划模型

研究了能力约束的有限计划展望期生产计划问题,各周期的需求随机,库存产品存在变质且变质率为常数。建立了问题的期望值模型,目标函数为极小化生产准备成本、生产成本、库存成本的期望值。提出了随机模拟、遗传算法和启发式算法相结合的求解算法。用数值实例对模型和算法进行了验证,优化结果表明模型和算法是有效的。     

需求依赖库存且有时变短缺拖后率的非立即变质物品库存模型

研究了时变短缺部分拖后条件下非立即变质性物品的库存补给模型,其中物品的变质率随时间变化而变化。当库存水平为正值时,市场需求受销售价格影响;当库存为负值时,不能满足的需求被部分拖后,拖后率与在缺货期间已经发生的缺货量有关。通过考虑短缺拖后率和变质率同时随时间变化对库存补给策略的影响,建立具有短缺量部分拖后的非立即变质性物品的库存模型,并且给出模型最优解存在的必要条件,得到一类更加符合实际情形的库存模型。最后,用数值算例说明模型的实际应用。     

一类考虑机器可靠性的变质性物品供应商管理库存

在考虑机器可靠性和通货膨胀的情形下,建立一个供应商和一个订货商,在允许订货商缺货且缺货量部分拖后的易变质产品的供应商管理库存(VMI,Vendor Managed Inventory)模型,给出了数值算例、最优解及主要参数的图形分析,结果表明参数对供应链库存成本均有一定程度的影响,为VMI模式下的库存管理系统提供一些理论依据.     

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.     

动态定价策略下的精确库存成本建模与优化

提出一种更接近实际的需求率公式,在式中同时考虑了价格和出厂时间对客户需求率的影响.基于新的需求率公式,建立了动态定价策略下的精确库存持有成本模型和库存商品的利润函数.注意到利润函数的复杂性,使用遗传算法分析了利润函数的性质,得出最优定价时间、定价价格和最大利润的关系,并分析了库存持有成本变化和消费者购买欲望变化对各定价参数的影响.     

库存水平影响需求下变质品订购、定价和保鲜技术投资的联合决策

研究变质品的订购、定价和保鲜技术投资联合决策问题.假设产品需求同时受价格和库存水平的影响,系统不允许缺货并放松期末库存水平为零的约束,零售商拥有有限的货架空间或存储空间,同时考虑零售商可以通过投资保鲜技术减低产品的变质率,以平均利润最大化为目标构建库存水平影响需求下变质品的订购、定价和保鲜技术投资联合决策模型.首先证明最优策略的存在性和唯一性,并给出零售商建立期末库存的条件;然后利用最优解的相关性质设计一个求解模型的多阶段迭代算法;最后通过具体算例验证展示模型和算法的可行性和实用性,并完成相关参数的敏感性分析,获得一定的管理启示.     

易变质产品的生产计划与作业排序集成优化研究

讨论了一类针对易变质产品生产批量计划与作业排序的集成优化问题,以最小化库存成本、变质成本、缺货成本、加班成本之和作为目标函数并建立了混合整数规划模型,采用协同进化遗传算法进行求解,即通过迁移算子把协同进化算法和遗传算法有机联系起来,加强算法的寻优能力和收敛性能,最后通过仿真实验,分析自身进化结果,同时与遗传算法对比结果,验证了算法的性能。     

协同供应链多级库存控制的多目标优化模型及其求解方法

在多级库存的协调控制过程中，只考虑成本的单目标优化模型对于提高供应链总体性能水平存在着局限，本文提出了考虑需求满足率、时间、成本的多目标协同优化模型，对于多品种、复杂拓扑结构，以及库容、生产能力受限的情况，提出了一种在外层对库存策略和内层对物流分配方案分别进行寻优的双层求解方法，并采用演化多目标优化技术构造了算法. 通过算例实验对模型的有效性进行了验证，实验结果表明，基于多目标模型的优化结果使得系统总体性能得到显著改善.     

变质品生产、库存控制与预防维修联合建模与优化

针对产品变质带来库存损耗的问题,在经济生产批量模型的基础上加入零库存时间,提出新的库存控制策略.该策略以预防维修周期和零库存时间为联合决策变量,利用更新定理建立了单位时间内总成本的期望模型.为了提高对此期望的计算速度,设计基于矩阵运算的蒙特卡罗仿真算法,并与遗传算法结合,大大提高了模型的优化速度.通过比较研究,验证了仿真算法的显著优势,并通过敏感性分析明确了相关参数的变化对最优策略的影响.     

A supplier-selection model with classification and joint replenishment of inventory items

Since inventory costs are closely related to suppliers, many models in the literature have selected the suppliers and also allocated orders, simultaneously. Such models usually consider either a single inventory item or multiple inventory items which have independent holding and ordering costs. However, in practice, ordering multiple items from the same supplier leads to a reduction in ordering costs. This paper presents a model in capacity-constrained supplier-selection and order-allocation problem, which considers the joint replenishment of inventory items with a direct grouping approach. In such supplier-selection problems, the following items are considered: a fixed major ordering cost to each supplier, which is independent from the items in the order; a minor ordering cost for each item ordered to each supplier; and the inventory holding and purchasing costs. To solve the developed NP-hard problem, a simulated annealing algorithm was proposed and then compared to a modified genetic algorithm of the literature. The numerical example represented that the number of groups and selected suppliers were reduced when the major ordering cost increased in comparison to other costs. There were also more savings when the number of groups was determined by the model in comparison to predetermined number of groups or no grouping scenarios.     

Developing a multi-objective,multi-item inventory model and three algorithms for its solution

We develop a multi-objective model in a multi-product inventory system.The proposed model is a joint replenishment problem(JRP) that has two objective functions.The first one is minimization of total ordering and inventory holding costs,which is the same objective function as the classic JRP.To increase the applicability of the proposed model,we suppose that transportation cost is independent of time,is not a part of holding cost,and is calculated based on the maximum of stored inventory,as is the case in many real inventory problems.Thus,the second objective function is minimization of total transportation cost.To solve this problem three efficient algorithms are proposed.First,the RAND algorithm,called the best heuristic algorithm for solving the JRP,is modified to be applicable for the proposed problem.A multi-objective genetic algorithm(MOGA) is developed as the second algorithm to solve the problem.Finally,the model is solved by a new algorithm that is a combination of the RAND algorithm and MOGA.The performances of these algorithms are then compared with those of the previous approaches and with each other,and the findings imply their ability in finding Pareto optimal solutions to 3200 randomly produced problems.     

Application of particle swarm optimisation for solving deteriorating inventory model with fluctuating demand and controllable deterioration rate

In most of the inventory models in the literature, the deterioration rate of goods is viewed as an exogenous variable, which is not subject to control. In the real market, the retailer can reduce the deterioration rate of product by making effective capital investment in storehouse equipments. In this study, we formulate a deteriorating inventory model with time-varying demand by allowing preservation technology cost as a decision variable in conjunction with replacement policy. The objective is to find the optimal replenishment and preservation technology investment strategies while minimising the total cost over the planning horizon. For any given feasible replenishment scheme, we first prove that the optimal preservation technology investment strategy not only exists but is also unique. Then, a particle swarm optimisation is coded and used to solve the nonlinear programming problem by employing the properties derived from this article. Some numerical examples are used to illustrate the features of the proposed model.     

具单周期特性的短生命周期产品随机IRP及算法

为优化企业物流系统,针对单周期,短生命周期产品的特点,将库存控制与配送路径安排决策集成,考虑随机需求、缺货成本、积压贬值成本、配送成本等,建立一个具有单周期特性的短生命周期产品随机IRP离散模型,目标是合理确定各零售门店的订购数量及配送路线使得系统成本最小。该问题属于NP-hard问题。对此,采用“报童模型”和差分法求解最佳订购量,将模型予以转化,并设计了一种遗传算法进行求解。算例结果表明所提算法能在较短时间内求解出不同客户数目组合的满意解。结论是：门店订购量宜采用组合选择方式;系统成本与单位行程运价正相关;车容量增大有助于降低系统成本。     

A Joint Replenishment Inventory-Location Model

We introduce a distribution center location model that incorporates joint replenishment inventory costs at the distribution centers. The model is formulated as a Fixed Charge Location Problem (FCLP) which objectively considers not only location specific costs but also inventory replenishment costs. In the joint replenishment problem we consider a single item and several distribution centers in different locations and apply a similar algorithm to the one used to solve the multi-item problem. We propose a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem.     

The integrated lot sizing and cutting stock problem with saw cycle constraints applied to furniture production

The integrated lot sizing and cutting stock problem is studied in the context of furniture production. The goal is to capture the interdependencies between the determination of the lot size and of the cutting process in order to reduce raw material waste and production and inventory costs. An integrated mathematical model is proposed that includes lot sizing decisions with safety stock level constraints and saw capacity constraints taking into account saw cycles. The model solution is compared to a simulation of the common practice of taking the lot size and the cutting stock decisions separately and sequentially. Given the large number of variables in the model, a column-generation solution method is proposed to solve the problem. An extensive computational study is conducted using instances generated based on data collected at a typical small scale Brazilian factory. It includes an analysis of the performance of the integrated approach against sequential approaches, when varying the costs in the objective function. The integrated approach performs well, both in terms of reducing the total cost of raw materials as well as the inventory costs of pieces. They also indicate that the model can support the main decisions taken and can bring improvements to the factory's production planning.     

A coordinated approach to hedge the risks in stochastic inventory-routing problem

The inventory routing problem (IRP) studied in this research involves repeated delivery of products from a depot to a set of retailers that face stochastic demands over a long period. The main objective in the IRP is to design the set of routes and delivery quantities that minimize transportation cost while controlling inventory costs. Traditional IRP focuses on risk-neutral decision makers, i.e., characterizing replenishment policies that maximize expected total net present value, or equivalently, minimize expected total cost over a planning horizon. In this research, for incorporating risk aversion, a hedge-based stochastic inventory-routing system (HSIRS) integrated with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Forward Option Pricing (FOP)model based on Black-Scholes model, from hedge point of view, is proposed to solve the multi-product multi-period inventory routing problem with stochastic demand. Computational results demonstrate the importance of this approach not only to risk-averse decision makers, but also to maximize the net present value at an acceptable service level. As a result, an optimal portfolio (R, s, S) system of product group can be generated to maximize the net present value under an acceptable service level in a given planning horizon. Meanwhile, the target group needed to be served and the relative transportation policy also can be determined accordingly based on the time required to be served as a priori partition to minimize the average transportation costs; hence, the routing assignment problem can be successfully optimized through a Predicting Particle Swarm Optimization algorithm.     

Optimal price and order size for a reseller under partial backordering

The problem of determining the optimal price and lot size for a reseller is considered in this paper. It is assumed that demand can be backlogged and that the selling price is constant within the inventory cycle. The backlogging phenomenon is modeled without using the backorder cost and the lost sale cost since these costs are not easy to estimate in practice. The case in which the selling price is fixed and therefore, demand is a known constant is also considered. Given the new way of modeling the backlogging phenomenon, the results for the case of constant demand are developed. Analysis is also presented for the reselling situation in which a nonperishable product is sold.Scope and purposePerishable products constitute a sizable component of inventories. A common question in a reselling situation involving a perishable (or a nonperishable) product is: What should be the size of the replenishment? If demand for the product is sensitive to price, then another question is: What should be the selling price? Although the ability to vary price within an inventory cycle is important, in many cases, the reseller may opt for a policy of constant selling price for administrative convenience. In this paper the pricing and/or lot sizing problem faced by a reseller is modeled assuming a general deterioration rate and a general demand function. The model allows for backlogging of demand. When a product is highly perishable, the reseller may need to backlog demand to contain costs due to deterioration. In this sense, perishability and backlogging are complementary conditions. Given that the problem entails revenue and costs, a natural objective function for the model is profit per period. The conventional approach to modeling the backlogging phenomenon requires the use of the backorder cost and the lost sale cost. These costs, however, are difficult to estimate in practice. A new approach is used in which customers are considered impatient. Hence the fraction of demand that gets backlogged at a given point in time is a decreasing function of waiting time. First the subproblem in which price is fixed is solved to determine the optimal inventory policy. The subproblem represents the important case in which the reseller has no flexibility to change the selling price. Then a procedure is developed for determining the optimal quantity and the selling price for the broader problem. The procedure can be implemented on a spreadsheet.