供应链需求替代与生产流程柔性联合决策研究

通过在供应端引入生产流程柔性或在需求端针对顾客需求采用产品替代均能一定程度上应对需求不确定性对企业运营带来的负面影响,在考虑存在生产流程柔性以及需求替代成本,且只有一定比例的消费者接受产品替代的情形下,文章分析了刚性结构、需求替代、需求替代与部分生产流程柔性以及生产流程全柔性4种情境,研究了生产流程柔性与需求替代两者之间的相互影响及企业最优柔性结构的选择问题,并通过数值分析给出了相关参数设置下的最优选择结果,为现实中企业的柔性结构选择提供了一定的理论基础.     

我国水泥工业的市场统计分析与预测

本通过建国后水泥每年总产量及其相关数据的统计分析,找出了水泥需求制约和影响的主要因素,对水泥产量进行短期预测,并参照世界发达国家的水泥生产发展过程,结合我国实际,对我国的水泥总产量建立数学模型,进行中长期预测。     

消费者低碳偏好下的组合产品供应链定价策略

考虑低碳偏好消费者与普通消费者的差异性支付意愿和消费效用,研究低碳产品和普通产品组合的定价策略问题.基于消费者效用构建出低碳产品和普通产品的需求函数,分别建立了供应链一体化决策和分散决策下的两类产品组合定价模型,研究了两类产品定价组成结构特点,比较了供应链一体化决策与分散决策下的产品定价和供应链利润.研究发现,两类产品定价都呈现出可分离的结构特点,低碳消费者比例及其对普通产品接受程度水平对产品定价和供应链利润都产生影响.     

回购契约下双资源渠道供应链协调

分析了零售商既可以从供应商处采购产品同时也可以自己生产的供应链协调问题.提出了回购契约协调该供应链.研究发现,协调回购契约的结构与零售商的需求和产量的随机特征无关,零售商生产和不生产两种情况下的协调回购契约的结构也相同.进一步在假设零售商的需求和产量均为均匀分布下,分析了协调回购契约下零售商的随机产量的价值、零售商的利润和供应商的利润关于批发价和供应商的单位生产成本的变化趋势.     

需求不确定情形下采用延迟定价的延迟生产系统最优生产决策

本文在考虑采用延迟生产的大规模定制企业能够实施延迟定价的环境下,建立了需求不确定情形下关于替代产品的延迟生产模型(企业在需求不确定的情况下生产通用中间产品,等待需求出现后再按客户需求定制最终产品并定价销售)和无延迟生产模型(企业在需求不确定的情况下按预测直接生产最终产品,等待需求出现后再定价并销售),通过对模型的推理分析,给出了企业采用延迟生产策略的适用条件以及当采用延迟生产策略时通用中间产品单位生产成本、延迟定制成本、产品之间的替代性、客户需求的方差和相关性与通用中间产品的最优生产数量和利润之间的关系,为企业在决策是否采用延迟生产策略以及通用中间产品生产数量提供理论支持。     

产品服务能力与定价联合优化策略机制研究——基于动态性和非线性视角

为了快速响应客户需求的动态变化和提高产品服务能力的管理效率,运用状态相关需求率函数和成本收益率函数,建立用以解决产品服务需求动态性和非线性难题的排队优化模型。通过考虑产品服务化过程中客户需求变化特征,从服务能力和价格两个维度将客户划分为实惠型、经济型、专业型和品质型四个不同类别。依据客户产品服务需求动态演变阶段,针对服务能力与价格的内在关联性,运用排队优化模型分别构建了成本领先策略、服务能力领先策略、价格领先策略和产品服务能力定价联合策略。最后,借助南阳泵业企业运营实例,验证了产品服务能力定价联合策略的优越性,并给出产品服务能力定价联合策略演化路径及其实施对策。     

基于Vague集理论一维属性——需求匹配的知识推荐算法研究

论文从推荐系统中知识推荐算法的核心——产品属性与用户需求的匹配出发,首先探讨了如何采用形式化的Vague集语言描述用户的需求和产品的属性问题。之后分别运用用户兴趣度模型和产品特征模型搭建了用户需求和产品属性的Vague集模型。在模型融合的过程中采用Vague集理论中成熟的相似度计算公式,实现了用户需求与产品属性的匹配计算。最后,从爱奇艺中任意提取了5名注册用户和5部2019年新上映的电影,按照搭建的模型进行了数据计算,得到了可靠的计算结果,同时也构造了用户和产品之间的知识库,为后期知识推荐规则的形成奠定了基础。     

考虑季末调整的季节性产品(Qi,Ri)库存模型与管理

考虑季节性产品需求的季节性周期变化、季节内需求不确定性双重波动对产品需求的影响,为季节性产品进行综合需求预测,制定库存管理策略提供参考.本研究综合Holt-Winter需求预测模型和(Q,R)库存模型进行订货点和订货量的决策,并针对季节性产品特点提出了季节间转换时期的订货策略.最后,利用上述模型和某化工企业多年实际数据,进行实证研究,对化工产品的订货量和订货点进行优化,验证了本研究提出的模型与方法可以大幅降低季节性产品库存成本和缺货率.研究对季节性产品库存管理有着重要的学术与实践意义.     

具有随机需求的多种差异产品供应链网络均衡模型研究

针对产品的品牌和产地存在的差异性,研究了供应链网络的均衡模型,构建了具有随机需求的多种差异产品的供应链网络均衡模型.运用随机效用理论和多项式logit模型分析了需求市场上产品的随机选择问题,利用变分不等式的形式给出了制造商、零售商,需求市场以及整个供应链网络的均衡条件,并给出了经济解释.最后,通过算例验证了模型的合理性.     

产品需求种类不确定条件下制造柔性价值研究

将Karsak＆Ozogul的产品需求数量不确定条件下的制造柔性价值模型推广为产品需求种类不确定性条件下的制造柔性价值模型，并把不确定源服从几何布朗运动的假设条件放松到服从随机微分方程的一般情形。通过构建产品需求种类不确定性条件下制造柔性价值的数学模型，对不确定性因素进行了比较静态分析，并给出了一个实例分析。结果表明：制造柔性价值随着产品需求种类不确定性增加而增加。     

Stochastic optimization models for a single-sink transportation problem

In this paper, we study a single-sink transportation problem in which the production capacity of the suppliers and the demand of the single customer are stochastic. Shipments are performed by capacitated vehicles, which have to be booked in advance, before the realization of the production capacity and the demand. Once the production capacity and the demand are revealed, there is an option to cancel some of the booked vehicles against a cancellation fee; if the quantity shipped from the suppliers using the booked vehicles is not enough to satisfy the demand, the residual quantity is purchased from an external company. The problem is to determine the number of vehicles to book in order to minimize the total cost. We formulate a two-stage and a multistage stochastic mixed integer linear programming models to solve this problem and test them on a real case provided by Italcementi, the primary Italian cement producer and the fifth largest cement producer in the world. We test the influence of different scenario-tree structures on the solutions of the problem, as well as sensitivity of the results with respect to the cancellation fee.     

随机需求下考虑半成品库存的多周期生产决策优化

为了更好地应对需求的不确定性,在需求实现之前,企业既可以生产成品直接满足需求,亦可生产部分半成品,在观察到实际需求之后短时间内迅速完成剩余生产环节以满足需求。未加工的半成品和未售出的成品可用于满足后续周期的需求。作为一种提高生产灵活性的手段,分阶段生产的方式会产生更高的成本。企业需要在成本和灵活性之间作出权衡,优化生产决策。模型通过动态规划的方法,研究需求不确定情况下考虑半成品库存的多周期生产决策问题,通过分析目标函数以及最优值函数的结构性质,推导出最优的多周期生产策略为修正的目标库存策略,并且分析了不同参数对最优策略的影响。     

Production-inventory control policy under warm/cold state-dependent fixed costs and stochastic demand: partial characterization and heuristics

The motivation for our study comes from some production and inventory systems in which ordering/producing quantities that exceed certain thresholds in a given period might eliminate some setup activities in the next period. Many examples of such systems have been discussed in prior research but the analysis has been limited to production settings under deterministic demand. In this paper, we consider a periodic-review production-inventory model under stochastic demand and incorporate the following fixed-cost structure into our analysis. When the order quantity in a given period exceeds a specified threshold value, the system is assumed to be in a “warm” state and no fixed cost is incurred in the next period regardless of the order quantity; otherwise the system state is considered “cold” and a positive fixed cost is required to place an order. Assuming that the unsatisfied demand is lost, we develop a dynamic programming formulation of the problem and utilize the concepts of quasi-K-convexity and non-K-decreasing to show some structural results on the optimal cost-to-go functions. This analysis enables us to derive a partial characterization of the optimal policy under the assumption that the demands follow a Pólya or uniform distribution. The optimal policy is defined over multiple decision regions for each system state. We develop heuristic policies that are aimed to address the partially characterized decisions, simplify the ordering policy, and save computational efforts in implementation. The numerical experiments conducted on a large set of test instances including uniform, normal and Poisson demand distributions show that a heuristic policy that is inspired by the optimal policy is able to find the optimal solution in almost all instances, and that a so-called generalized base-stock policy provides quite satisfactory results under reasonable computational efforts. We use our numerical examples to generate insights on the impact of problem parameters. Finally, we extend our analysis into the infinite horizon setting and show that the structure of the optimal policy remains similar.     

Integrated capacity and inventory management with capacity acquisition lead times

We model a make-to-stock production system that utilizes permanent and contingent capacity to meet non-stationary stochastic demand, where a constant lead time is associated with the acquisition of contingent capacity. We determine the structure of the optimal solution concerning both the operational decisions of integrated inventory and flexible capacity management, and the tactical decision of determining the optimal permanent capacity level. Furthermore, we show that the inventory (either before or after production), the pipeline contingent capacity, the contingent capacity to be ordered, and the permanent capacity are economic substitutes. We also show that the stochastic demand variable and the optimal contingent capacity acquisition decisions are economic complements. Finally, we perform numerical experiments to evaluate the value of utilizing contingent capacity and to study the effects of capacity acquisition lead time, providing useful managerial insights.     

The performance evaluation of a multi-stage JIT production system with stochastic demand and production capacities

This paper discusses a single-item, multi-stage, serial Just-in-Time (JIT) production system with stochastic demand and production capacities. The JIT production system is modeled as a discrete-time, M/G/1-type Markov chain. A necessary and sufficient condition, or a stability condition, under which the system has a steady-state distribution is derived. A performance evaluation algorithm is then developed using the matrix analytic methods. In numerical examples, the optimal numbers of kanbans are determined by the proposed algorithm. The optimal numbers of kanbans are robust for the variations in production capacity distribution and demand distribution.     

Inventory Models for Increasing Demand Followed by Level Demand

This paper considers a product subject to a period of increasing demand, according to a general power law, followed by a period of level demand. By assuming that the Economic Batch Quantity (EBQ) is used as soon as level demand is reached, we derive the optimal production/purchasing policy for use at any stage during the period of growth. The optimal approach is compared with that of the Silver-Meal heuristic.     

不确定需求下页岩气开发项目投资时机与钻井数量选择研究

基于工程化实现视角,结合规划产能与实际产出的不一致性和产量递减性,构建市场需求不确定情境下页岩气开发项目的投资时机与钻井数量决策模型,给出最佳投资时机和最优钻井数量的解析解,并探讨了不确定性、产量递减率、钻井成功率以及钻井成本变动对最佳投资时机和最优钻井数量的影响。结果表明最佳投资时机受单位有效钻井成本影响却与最优钻井数量多少无关,最优钻井数量受市场需求的期望增长率和波动影响而与市场需求量大小无关;不确定性、产量递减率或钻井成本的增大,将引起投资延迟和钻井数量增加,而钻井成功率的增大则会起到相反的作用;并发现投资阈值与最优钻井数量呈同向变动规律。     

The finite multiple lot sizing problem with interrupted geometric yield and holding costs

We consider the multiple lot sizing problem in production systems with random process yield losses governed by the interrupted geometric (IG) distribution. Our model differs from those of previous researchers which focused on the IG yield in that we consider a finite number of setups and inventory holding costs. This model particularly arises in systems with large demand sizes. The resulting dynamic programming model contains a stage variable (remaining time till due) and a state variable (remaining demand to be filled) and therefore gives considerable difficulty in the derivation of the optimal policy structure and in numerical computation to solve real application problems. We shall investigate the properties of the optimal lot sizes. In particular, we shall show that the optimal lot size is bounded. Furthermore, a dynamic upper bound on the optimal lot size is derived. An O(nD) algorithm for solving the proposed model is provided, where n and D are the two-state variables. Numerical results show that the optimal lot size, as a function of the demand, is not necessarily monotone.     

Control Policy for a Manufacturing System with Random Yield and Rework

We develop a production policy that controls work-in-process (WIP) levels and satisfies demand in a multistage manufacturing system with significant uncertainty in yield, rework, and demand. The problem addressed in this paper is more general than those in the literature in three aspects: (i) multiple products are processed at multiple workstations, and the capacity of each workstation is limited and shared by multiple operations; (ii) the behavior of a production policy is investigated over an infinite-time horizon, and thus the system stability can be evaluated; (iii) the representation of yield and rework uncertainty is generalized. Generalizing both the system structure and the nature of uncertainty requires a new mathematical development in the theory of infinite-horizon stochastic dynamic programming. The theoretical contributions of this paper are the existence proofs of the optimal stationary control for a stochastic dynamic programming problem and the finite covariances of WIP and production levels under the general expression of uncertainty. We develop a simple and explicit sufficient condition that guarantees the existence of both the optimal stationary control and the system stability. We describe how a production policy can be constructed for the manufacturing system based on the propositions derived.     

需求中断下具零售商激励的双渠道供应链价格与生产决策

研究了由一个制造商和一个零售商所组成的两周期双渠道供应链.在第一周期需求实现之前,建立了生产量;在第二周期,基于第一周期的生产量和需求的实现确定了最优销售价格,进而确定了最优的生产量,实现了生产量的优化.通过需求中断下制造商和零售商的垂直整合,讨论了中断情形下价格和生产量对利润的影响.实证结果证实,集中式供应链的最优价格决策受零售渠道顾客偏好和市场规模变化的影响较为显著.