离散模糊需求报童问题的可信性模型研究

基于可信性理论,建立了确定离散模糊需求报童问题订货量的期望成本与利润模型,并与基于可能性理论的质心特征值分析模型进行了比较.数值研究结果表明:1)对应每一模型的最小模糊成本和最大利润的订货量不一致,且模糊期望模型与质心特征值模型确定的订货量不同;2)对应不同订货量,模糊可能性成本、利润之和及期望成本、利润之和均不为固定常数.由于在模糊环境下,与概率测度对应的模糊量描述是可信性测度,所以,相比而言,离散模糊需求报童问题的模糊期望值模型较模糊可能性模型好.     

随机模糊需求报童问题

考虑到需求的随机模糊性,建立了随机模糊需求报童的利润模型。利用可信性测度理论给出了其期望利润模型,并揭示了期望利润函数的凹性,证明了最优订货量的存在性和唯一性。结合随机模糊模拟技术和随机扰动近似算法设计了求解最优订货量的混合算法。最后,通过数值算例说明了算法的有效性。     

模糊随机需求下多产品报童问题的均衡策略

针对模糊随机需求下单制造商多零售商的分布控制型多产品报童问题, 建立了含资金约束的期望利润最大化两层规划模型.结合模糊随机模拟技术与遗传算法, 设计了求解模型的混合智能算法.该算法不仅可获得上层制造商的最优折扣批发价及下层零售商的最优订购量,亦可求得该折扣形式的起始折扣点(折扣区间).算例分析表明,当制造商采取最优数量折扣策略时:1)促使零售商订货量增加至资金约束上限;2)部分产品订货量可达模糊随机市场需求的最大可能值:3)零售商和制造商的利润均增加.     

基于数量折扣合同和缺货损失的EOQ模型的改进

经济订购批量模型假定需求率、单位持有成本、订货成本为常数下得到总成本最低的订购批量,这些参数常数化的假设在现实中通常难以满足.假定需求和订货费为不确定的、库存成本包括年固定成本(与订货量无关)和年可变成本(与订货量有关),用三角模糊数表示年需求和订货费,通过引入数量折扣合同来量化单位产品进价,分别在不允许缺货和考虑缺货损失两种情况下得到最佳订货量.最后的算例表明了模型的合理性.     

考虑随机模糊缺陷率且允许缺货的EOQ模型

针对实际库存管理中的产品缺陷问题,研究了含随机模糊缺陷率且允许缺货的经济订购批量(EOQ)模型,并运用随机模糊理论将其转化为确定模型,设计了随机模糊模拟仿真算法进而确定了其最优订购策略.数值算例分析了缺陷率对最优订货量和最优利润的影响.     

考虑消费者退货的双渠道订货量决策研究

在考虑消费者退货下,构建由制造商和零售商组成的二级供应链模型,研究了双方的订货量决策问题,并分析不同策略以及决策顺序对均衡结果和制造商开通直销渠道的影响。研究发现:在单渠道中,提供退款保证可提高订货量、制造商和零售商利润。当直销成本较大时,制造商将不开通直销渠道;提供退款保证有利于制造商开通直销渠道。在双渠道中,提供退款保证能够提高直销量、总订货量和制造商利润;直销量、总订货量、制造商利润和总利润都随直销成本递减;在顺序决策下,制造商利润和总利润更高。     

模糊随机需求报童问题的Stackelberg-Nash均衡策略

针对模糊随机需求下的分布控制型报童问题,建立了无数量折扣和有数量折扣情况下的利润最大化两层规划模型,并结合模糊随机模拟技术和遗传算法设计了模型求解的混合智能算法.解决了上层制造商制定包括折扣区间和折扣价格的最优数量折扣策略,以及下层多零售商确定各自的最优订货量的Stackelberg-Nash均衡策略问题.     

考虑消费者低碳偏好的可替代产品订货决策

基于产品的可替代性,考虑零售商有无能力限制两种情况,订购环境质量和价格不同的两种产品,建立利润最大化模型,确定两种产品的最优订货量和消费者低碳偏好对其的影响.理论分析表明:库存能力的提高使得订货量增大,订单数量和利润随环境质量的变化而变化;算例计算的结果验证了模型的合理性,数值分析表明:提高库存容量和订购替代率大的两种产品有利于利润的增加,当库存无容量限制时,订货量和销售商利润水平不再受容量变化的影响.     

两种渠道进入模式下信息获取与分享策略研究

考虑单制造商和单零售商组成的双渠道供应链，基于随机需求建立制造商可以在零售商确定订货量之前(先进模式)或与零售商同时(同进模式)确定直销渠道订货量、零售商可通过支付固定成本来获取真实市场需求的订货量博弈模型。首先在两种模式下研究了零售商愿意获取信息并分享给制造商的条件，结果表明当获取成本较高时，零售商不会获取信息；否则零售商会获取信息但不分享给制造商，此时制造商可通过成本分担和信息价值补贴合约来实现双方利润的帕累托改进。随后讨论了渠道进入模式选择策略，结果表明制造商总是选择先进模式，而只要需求的变异系数不是很小，同进模式对零售商更有利。     

需求信息非对称情况下零售商最优订货问题研究

考虑需求信息非对称环境下多个存在竞争的零售商的最优订货问题,每个零售商的需求分布函数未知,传统的通过最大化期望利润来求解最优订货量的方法此时不再适用,转而应用稳健优化中使用的方法-最小化最大后悔值方法.首先给出了零售商最优订货量的一般形式.其次考虑了问题的两种特殊情形:对称博弈、两个零售商的最优订货模型,得到:对称博弈存在Nash平衡点、竞争使得零售商订货量增大、信息不对称使得零售商订货量降低并且零售商的最优订货量在一定条件下会随回收残值的增大而增大;两个零售商最优订货模型下的最优订货量的具体形式.     

Multi-item inventory models with price dependent demand under flexibility and reliability consideration and imprecise space constraint: A geometric programming approach

In this paper, multi-item economic production quantity (EPQ) models with selling price dependent demand, infinite production rate, stock dependent unit production and holding costs are considered. Flexibility and reliability consideration are introduced in the production process. The models are developed under two fuzzy environments–one with fuzzy goal and fuzzy restrictions on storage area and the other with unit cost as fuzzy and possibility–necessity restrictions on storage space. The objective goal and constraint goal are defined by membership functions and the presence of fuzzy parameters in the objective function is dealt with fuzzy possibility/necessity measures. The models are formed as maximization problems. The first one—the fuzzy goal programming problem is solved using Fuzzy Additive Goal Programming (FAGP) and Modified Geometric Programming (MGP) methods. The second model with fuzzy possibility/necessity measures is solved by Geometric Programming (GP) method. The models are illustrated through numerical examples. The sensitivity analyses of the profit function due to different measures of possibility and necessity are performed and presented graphically.     

A stochastic inventory problem with fuzzy shortage cost

Up to now, many inventory models have been considered in the literature. Some assume stochastic demands and others consider the deterministic case. Though they include a shortage cost due to lost sales, it is usually assumed to be known concretely and a priori. This paper introduces fuzziness of shortage cost explicitly into the classical newsboy problem. That is, we investigate the so-called fuzzy newsboy problem where its shortage cost is vague and given by an L shape fuzzy number. Then the total expected profit function also becomes a fuzzy number. Finally, we find an optimal ordering quantity realizing the fuzzy max order of the profit function (fuzzy min order considering the profit function) and compare it with the optimal ordering quantity of the non-fuzzy newsboy problem.     

Fuzzy profit measures for a fuzzy economic order quantity model

Changing economic conditions make the selling price and demand quantity more and more uncertain in the market. The conventional inventory models determine the selling price and order quantity for a retailer’s maximal profit with exactly known parameters. This paper develops a solution method to derive the fuzzy profit of the inventory model when the demand quantity and unit cost are fuzzy numbers. Since the parameters contained in the inventory model are fuzzy, the profit value calculated from the model should be fuzzy as well. Based on the extension principle, the fuzzy inventory problem is transformed into a pair of two-level mathematical programs to derive the upper bound and lower bound of the fuzzy profit at possibility level α. According to the duality theorem of geometric programming, the pair of two-level mathematical programs is transformed into a pair of conventional geometric programs to solve. By enumerating different α values, the upper bound and lower bound of the fuzzy profit are collected to approximate the membership function. Since the profit of the inventory problem is expressed by the membership function rather than by a crisp value, more information is provided for making decisions.     

Fuzzy mixture two warehouse inventory model involving fuzzy random variable lead time demand and fuzzy total demand

This paper considers a two-warehouse fuzzy-stochastic mixture inventory model involving variable lead time with backorders fully backlogged. The model is considered for two cases—without and with budget constraint. Here, lead-time demand is considered as a fuzzy random variable and the total cost is obtained in the fuzzy sense. The total demand is again represented by a triangular fuzzy number and the fuzzy total cost is derived. By using the centroid method of defuzzification, the total cost is estimated. For the case with fuzzy-stochastic budget constraint, surprise function is used to convert the constrained problem to a corresponding unconstrained problem in pessimistic sense. The crisp optimization problem is solved using Generalized Reduced Gradient method. The optimal solutions for order quantity and lead time are found in both cases for the models with fuzzy-stochastic/stochastic lead time and the corresponding minimum value of the total cost in all cases are obtained. Numerical examples are provided to illustrate the models and results in both cases are compared.     

考虑模糊时间和成熟度的跨区域鲜果电商配送模型研究

基于现实配送过程中配送时间存在着不确定性,通过配送时间模糊化处理构建出跨区域鲜果电商配送模糊网络流模型,进而根据实际配送时间与期望配送时间的偏离建立顾客满意度函数,然后构建由满意度降低引起的惩罚成本函数,并根据鲜果成熟度变化特性建立带有模糊时间和成熟度的跨区域鲜果电商配送最小成本路线优化模型。在求解过程中利用三种去模糊化方法(重心法、积分法和α-cut法)对模糊时间进行处理,并结合现有的整数规划算法对模型进行求解,得出最佳配送路线并进行对比分析。结果表明:(1)根据鲜果成熟度水平选择适当的配送路线可以有效降低配送成本,保证鲜果品质;(2)在去模糊化方面α-cut法比重心法和积分法所得结果更具有一般性。     

Fuzzy inventory problems for perishable commodities

This paper considers an inventory control model for a single perishable product with a fuzzy shortage cost and a fuzzy outdating cost. This model is a single-period horizon model. Due to fuzziness of shortage and outdating costs, an expected profit function is represented with a fuzzy set. The purpose of this paper is to find the solution maximizing the expected profit function. After defining a nondominated ordering quantity based on fuzzy max order, we seek some of them and investigate an effect of the fuzziness on the obtained solutions.     

Fuzzy inventory model with two warehouses under possibility measure on fuzzy goal

Multi-item inventory model with stock-dependent demand and two-storage facilities is developed in fuzzy environment (purchase cost, investment amount and storehouse capacity are imprecise) under inflation and time value of money. Joint replenishment and simultaneous transfer of items from one warehouse to another is proposed using basic period (BP) policy. As some parameters are fuzzy in nature, objective (average profit) function as well as some constraints are imprecise in nature. Model is formulated as to optimize the possibility/necessity measure of the fuzzy goal of the objective function and constraints are satisfied with some pre-defined necessity. A genetic algorithm (GA) is developed with roulette wheel selection, binary crossover and mutation and is used to solve the model when the equivalent crisp form of the model is available. In other cases fuzzy simulation process is proposed to measure possibility/necessity of the fuzzy goal as well as to check the constraints of the problem and finally the model is solved using fuzzy simulation based genetic algorithm (FSGA). The models are illustrated with some numerical examples and some sensitivity analyses have been done.     

Two storage inventory model with fuzzy deterioration over a random planning horizon

An inventory model for a deteriorating item with stock dependent demand is developed under two storage facilities over a random planning horizon, which is assumed to follow exponential distribution with known parameter. For crisp deterioration rate, the expected profit is derived and maximized via genetic algorithm (GA). On the other hand, when deterioration rate is imprecise then optimistic/pessimistic equivalent of fuzzy objective function is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to maximize the optimistic/pessimistic return and finally fuzzy simulation-based GA is developed to solve the model. The models are illustrated with some numerical data. Sensitivity analyses on expected profit function with respect to distribution parameter λ and confidence levels α₁ and α₂ are also presented.     

Cost,Revenue and Profit Efficiency Models in Generalized Fuzzy Data Envelopment Analysis

One of the most important information given by data envelopment analysis models is the cost, revenue and profit efficiency of decision making units (DMUs). Cost efficiency is defined as the ratio of minimum costs to current costs, while revenue efficiency is defined as the ratio of maximum revenue to current revenue of the DMU. This paper presents a framework where data envelopment analysis (DEA) is used to measure cost, revenue and profit efficiency with fuzzy data. In such cases, the classical models cannot be used, because input and output data appear in the form of ranges. When the data are fuzzy, the cost, revenue and profit efficiency measures calculated from the data should be uncertain as well. Fuzzy DEA models emerge as another class of DEA models to account for imprecise inputs and outputs for DMUs. Although several approaches for solving fuzzy DEA models have been developed, numerous deficiencies including the $\alpha$-cut approaches and types of fuzzy numbers must still be improved. This scheme embraces evaluation method based on vector for proposed fuzzy model. This paper proposes generalized cost, revenue and profit efficiency models in fuzzy data envelopment analysis. The practical application of these models is illustrated by a numerical example.     

Fuzzy pricing,marketing and service planning in a fuzzy inventory model: A geometric programming approach

In the real world markets, demand is influenced by different parameters. Recently, many researchers have been interested in integrated production and marketing planning strategies in inventory models where demand depends on different parameters such as price and/or marketing expenditure. The quality of services that are offered to customers of a product is one of the most important parameters that affects demand in the real markets and has not been considered in development of inventory models. On the other hand, the cost parameters in real inventory systems and other parameters such as price, marketing and service elasticity to demand are imprecise and uncertain in nature. So, the notion of fuzziness can be applied to cope with this uncertainty. In this paper, a new fuzzy profit maximization inventory model with shortages is proposed. The demand is considered as a power function of price, marketing expenditure and service expenditure. Furthermore, unit cost is determined as a power function of order quantity. Since the proposed model is in a fuzzy environment, a fuzzy decision should be made to meet the decision criteria, and the results should be fuzzy. Therefore, the proposed model is formulated and solved using geometric programming and fuzzy optimization techniques to derive an approximation of the results’ membership functions. The model is illustrated with a numerical example and finally a case study is provided for evaluation and validation of the results of model.