A service level model for the control wafers safety inventory problem

This paper considers the control wafers safety inventory problem (CWSIP) in the wafer fabrication photolithography area. The objective is to minimize the total cost of control wafers, where the cost includes new wafers cost, re-entrant cost and holding cost while maintaining the same level of production throughput. For the problem under pulling control production environment, a nonlinear programming model is presented to set safety inventory levels so as to minimize total cost of control wafers. A numerical example is given to illustrate the practicality of the model. The results demonstrate that the proposed model is an effective tool for determining the service level of safety inventory of control wafers for each grade.     

A linear programming model for the control wafers downgrading problem

This paper considers the control wafers downgrading problem (CWDP) in the wafer fabrication photolithography area. The objective of the research is to minimize the total cost of control wafers, while maintaining the same level of production throughput. For the problem under a pulling control production environment, a linear programming model is presented to set the supply rate of new control wafers and the recycle and downgrading rates so as to minimize the total cost of control wafers. A numerical example is given to illustrate the practicality of the model. The sensitivity of the linear programming model solution to changes in the underlying parameter values is also investigated. The results demonstrate that the proposed model is an effective tool for determining the control wafers downgrading policy.This paper presents a linear programming model that considers the cost of new control wafers, recycle and downgrading control wafers in wafer fabrication. The proposed model improved the performance of control wafers management, and served as the basis for setting the usage rates of control wafers.     

Optimal replenishment policies for deteriorating control wafers inventory

This paper considers a control wafers replenishment problem with inventory deterioration. In the process, control wafers begin to deteriorate after the pre-disposition. The objective of this research is to minimize the total cost of control wafers, where the cost includes ordering cost, holding cost and purchase cost. We first formulate the control wafers inventory problem as a dynamic programming model. We then transform the control wafers’ inventory problem into the mixed 0–1 linear programming model. An illustrative example with four cases is used to illustrate the practicality of these models, and sensitivity analysis is applied to understand the impact of parameters to these models. The results demonstrate that the proposed mixed 0–1 linear programming model is an effective tool for determining the replenishment of control wafers for multi-periods.     

A single-period inventory model with inventory update decision: the newsboy problem extension

Inventory models use a set of parameters to establish their respected optimal policy. Such parameters are information items that have to be generated (obtained) and, therefore, there is a cost for obtaining them. Traditional inventory models simply ignore the existence of this cost and, therefore, the established optimal policies by these models may not be necessarily optimal in practice. In this paper, based on the requirements dictated by a real-world project, we analyze the impact of information update decisions for a specific class of inventory model (a single-period inventory model). The main difference between the presented model in this paper and newsboy problem is the presence of an inventory update decision and its effect on other inventory decisions. That is to decide when inventory-related information should be updated, how much should be ordered, and what is the maximum level of demand that can be promised to the customers. We formulate these decisions and develop mathematical methods to find the corresponding optimal policies.     

A heuristic method for the combined location routing and inventory problem

The combined location routing and inventory problem (CLRIP) is used to allocate depots from several potential locations, to schedule vehicles’ routes to meet customers’ demands, and to determine the inventory policy based on the information of customers’ demands, in order to minimize the total system cost. Since finding the optimal solution(s) for this problem is a nonpolynomial (NP) problem, several heuristics for searching local optima have been proposed. However, the solutions for these heuristics are trapped in local optima. Global search heuristic methods, such as tabu search, simulated annealing method, etc., have been known for overcoming the combinatorial problems such as CLRIP, etc. In this paper, the CLRIP is decomposed into two subproblems: depot location-allocation problem, and routing and inventory problem. A heuristic method is proposed to find solutions for CLRIP. First of all, an initial solution for CLRIP is determined. Then a hybrid heuristic combining tabu search with simulated annealing sharing the same tabu list is used to improve the initial solution for each subproblem separately and alternatively. The proposed heuristic method is tested and evaluated via simulation. The results show the proposed heuristic method is better than the existing methods and global search heuristic methods in terms of average system cost.     

A model for wafer rotational nanogrinding of soft-brittle CdZnTe wafers

Nanogrinding was conducted on soft-brittle cadmium zinc telluride (CdZnTe or CZT) wafers using a ceramic bond ultrafine diamond wheel. Surface roughness R _a and PV achieved by nanogrinding are 1.5 and 12.2 nm, respectively. The highest feed rate was 83.3 nm/r, which is lower than 89 nm that is the critical value of brittle-ductile transition for CZT. As a result, ductile grinding was obtained. A novel model for wafer rotational grinding of undeformed chip thickness was proposed, based on the kinematics and geometric characteristics between wheel and workpiece. The undeformed chip thickness simulated was used to elucidate the fundamental mechanism for nanogrinding conditions, and it is in good agreement with the results found experimentally.     

Pull system for control and dummy wafers

Various C\D wafers (Control and Dummy wafers) are used in wafer fabrication to enhance product quality and process stability. Most wafer fabs downgrade used C\D wafers into other functional ones and then recycle them to reduce costs. Wafer costs increase with wafer diameter, and thus the downgrading mechanisms and inventory control of C\D wafers have become increasingly important to fab performance. Wafer fabs now are willing to seriously consider C\D wafer management but C\D wafer management remains neglected in the literature. This study presents a novel pull system focusing on inventory and downgrading management of C\D wafers. The proposed system in this study intends to reduce the WIP level of C\D wafers, increase recycled usages (number of times C\D-wafer lots are recycled) before scraping, and reduce machine delay ratio without lowering the product wafer throughput rate.     

分布式服务共享的访问控制技术

为实现服务共享,需要在异构访问控制模型之间建立分布式的访问控制机制.以ARBAC97访问控制模型为基础,引入代理系统,在不同的访问控制架构之间,建立分布式角色定义框架,给出了构建与共享服务相关的代理角色的方法,提供代理管理角色完整性的验证工具,从而避免了权限泄漏,细化了授权粒度,解决了分布式角色系统工程中的管理问题.最后,讨论了代理系统的实现机制,并在网络化制造服务平台集成中得到应用.     

A multi-product multi-period inventory control problem under inflation and discount: a parameter-tuned particle swarm optimization algorithm

In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items, and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a particle swarm optimization (PSO) algorithm is proposed to find a near-optimal solution. Since there is no bench mark available in the literature to justify and validate the results, a genetic algorithm is presented as well. In order to compare the performances of the two algorithms in terms of the fitness function and the required CPU time, they are first tuned using the Taguchi approach, in which a metric called “smaller is better” is used to model the response variable. Then, some numerical examples are provided to demonstrate the application and to validate the results obtained. The results show that, while both algorithms have statistically similar performances, PSO tends to be the better algorithm in almost all problems.     

A fuzzy reinforcement learning algorithm for inventory control in supply chains

In the real world, applications with very large state and action spaces and unknown state transition probability, classical reinforcement learning algorithms usually show poor performance. One way to address the performance problem is to approximate the policy or value function. Fuzzy rule-based systems are amongst the well-known function approximators. This paper presents a Flexible Fuzzy Reinforcement Learning algorithm, in which value function is approximated by a fuzzy rule-based system. The proposed algorithm has a separate module for tuning the structure of fuzzy rules. Moreover, the parameters of the system are tuned during the learning phase. Next, the proposed algorithm is applied to the problem of inventory control in supply chains. In this problem, a fuzzy agent (supplier) should determine the amount of orders for each retailer based on their utility for supplier, by considering its limited supply capacity. Finally, a simulation is performed to show the capability of the proposed algorithm.     

炼油厂综合库存管理优化问题研究

针对需求不确定条件下的炼油厂各级库存的综合优化问题,以在线连续调和技术为背景,建立了一个系统视角下的全厂库存管理的过程模型。求解时先进行多种产品需求的预测,根据预测值,采用实数编码的遗传算法,在局部优化控制器中以成品油非线性调和属性方程和混炼原油的线性调和属性方程为约束,计算出所需原油和各侧线产出率允许范围内的组分油流速及相应各级库存的优化值。最后,用广义预测控制算法,以局部优化结果为预先设定目标,考虑仿真模型实际运行过程中产生的模型失配、时变和干扰等不确定因素的影响,及时修改模型数据,在线滚动计算全厂库存仿真周期内的综合优化值。     

一类库存控制系统的稳定性分析

为了克服供应链系统存在的供货提前期、供货数量、价格等不确定以及市场环境多变的困难,保证供应链系统的库存稳定,建立了在一类改进的最大库存策略下一级库存控制系统的状态空间模型,针对不同的提前期,根据离散控制系统原理结合非线性系统的分析方法,给出了使系统稳定的订货策略参数的取值范围,并对之进行了仿真试验。结果表明,在最大库存策略下,系统的结构稳定性主要由订货决策的参数决定,与外部需求无关,而决策参数选取不当会使系统行为出现混沌等复杂现象。仿真显示,这些现象可使系统有较大的库存偏差,从而导致库存及相关费用的增加,表明稳定性对降低供应链系统的费用、提高其性能有重要意义。     

Downgrade decision for control/dummy wafers in a fab

Control and dummy (C/D) wafers are indispensable materials used in a semiconductor fab. C/D wafers stored in a high-grade buffer can be downgraded to several low-grade buffers. The downgrade decision is to determine the amount to downgrade for each of these low-grade buffers. Previous literature solves the downgrade decision by considering only the instantaneous WIP information, which is a short-term approach and may not yield the optimum solution in the long run. This paper presents an LP model to solve the downgrade decision problem, which aims to minimize the long-term daily usage of brand-new C/D wafers in a fab. The formulated problem assumes that the storage cost of C/D wafers is much less than the usage cost. This assumption has been justified by analyzing the cost structure of C/D wafers in a typical fab site.     

Downgrade decision for control/dummy wafers in a fab

Control and dummy (C/D) wafers are indispensable materials used in a semiconductor fab. C/D wafers stored in a high-grade buffer can be downgraded to several low-grade buffers. The downgrade decision is to determine the amount to downgrade for each of these low-grade buffers. Previous literature solves the downgrade decision by considering only the instantaneous WIP information, which is a short-term approach and may not yield the optimum solution in the long run. This paper presents an LP model to solve the downgrade decision problem, which aims to minimize the long-term daily usage of brand-new C/D wafers in a fab. The formulated problem assumes that the storage cost of C/D wafers is much less than the usage cost. This assumption has been justified by analyzing the cost structure of C/D wafers in a typical fab site.     

钢铁集团原料协同库存控制模型研究与应用

为提高我国钢铁集团经济效益,实现集团生产资源优化配置和子公司之间库存资源协同共享,提出了钢铁集团原料协同库存控制模型.该模型把集团各子公司仓库系统和战略供应商作为整体逻辑库存,以集团整体采购供应成本最小和供货价值最大为目标,利用网络层次分析法和遗传箅法整合的方法,解决了供货仓库选择及供货量分配问题.经东北某钢铁集团的实际应用表明了该协同库存控制模型的有效性和实用性.     

应用服务提供商模式下的系统访问控制模型

针对应用服务提供商系统的结构特点,分析了应用服务提供商系统的访问控制权限管理的需求,基于角色的访问控制模型和相关技术,引入全局角色的概念,使用域标签技术,设计了"1 n"管理格局的应用服务提供商系统访问控制模型,并使用ARBAC97中定义的指派函数来表征应用服务提供商一基于角色的访问控制模型的初始化授权策略和上层授权策略.该模型可以实现各个逻辑子系统的细粒度权限管理,亦可以实现各子系统功能差异化配置.以应用服务提供商模式的产品信息服务系统的访问控制权限管理为例,说明了应用服务提供商--基于角色的访问控制的应用方式.     

Adaptive inventory control models for supply chain management

Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, we propose two adaptive inventory-control models for a supply chain consisting of one supplier and multiple retailers. The one is a centralized model and the other is a decentralized model. The objective of the two models is to satisfy a target service level predefined for each retailer. The inventory-control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory-control approaches, modelling the uncertainty of customer demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action-value method, the control parameters are designed to adaptively change as customer-demand patterns changes. A simulation-based experiment was performed to compare the performance of the two inventory-control models.     

Two metaheuristics to solve a multi-item multiperiod inventory control problem under storage constraint and discounts

In this paper, a multi-item multiperiod inventory control problem with all-unit and/or incremental quantity discount policies under limited storage capacity is presented. The independent random demand rates of the items in the periods are known and the items are supplied in distinct batch sizes. The cost consists of ordering, holding, and purchasing. The objective is to find the optimal order quantities of all items in different periods such that the total inventory cost is minimized and the constraint is satisfied. A mixed binary integer programming model is first developed to model the problem. Then, a parameter-tuned genetic algorithm (GA) is employed to solve it. Since there is no benchmark available in the literature, a memetic algorithm (MA) is utilized as well to validate and verify the results obtained. The model implementation is next presented using some numerical examples and finally the performances of the proposed GA and MA are compared using two statistical tests and a simple additive weighting method. The results show that GA has better performance than MA in terms of average objective function value and average run time using the two comparison procedures.