Simultaneous resource portfolio planning under demand and technology uncertainty in the semiconductor testing industry

One of the most challenging issues for the semiconductor testing industry is how to deal with capacity planning and resource allocation simultaneously under demand and technology uncertainty. In addition, capacity planners require a tradeoff among the costs of resources with different processing technologies, while simultaneously considering resources to manufacture products. The need for exploring better solutions further increases the complexity of the problem. This study focuses on the decisions pertaining to (i) the simultaneous resource portfolio/investment and allocation plan accounting for the hedging tradeoff between the expected profit and risk, (ii) the most profitable orders from pending ones in each time bucket under demand and technology uncertainty, (iii) the algorithm to efficiently solve the stochastic and mixed integer programming problem. Due to the high computational complexity of the problem, this study develops a constraint-satisfaction based genetic algorithm, in conjunction with a chromosome-repair mechanism and sampling procedure, to resolve the above issues simultaneously. The experimental results indicate that the proposed mathematical model can accurately represent the resource portfolio planning problem of the semiconductor testing industry, and the solution algorithm can solve the problem efficiently.     

Exploring scientific information for policy making under deep uncertainty

Each actor evaluating potential management strategies brings her/his own distinct set of objectives to a complex decision space of system uncertainties. The diversity of these objectives and uncertainties requires detailed and rigorous analyses that respond to multifaceted challenges. The utility of this information depends on the accessibility of scientific information to decision makers. This paper demonstrates data visualization tools for presenting scientific results to decision makers in two case studies, La Paz/El Alto, Bolivia, and Yuba County, California. Visualization output from the case studies combines spatiotemporal, multivariate and multirun/multiscenario information to produce information corresponding to the objectives and uncertainties described by key actors. These tools can manage complex data and distill scientific information into accessible formats. Using the visualizations, scientists and decision makers can navigate the decision space and potential objective trade-offs to facilitate discussion and consensus building. These efforts can help identify stable negotiated agreements between different stakeholders.     

The effect of system configuration and ramp-up time on manufacturing system acquisition under uncertain demand

The objective of this paper is to investigate the optimal allocation of capacity investments at the tactical decision-making level by incorporating the configuration characteristics of selected system alternatives comprising Dedicated Manufacturing Systems (DMS) and Reconfigurable Manufacturing Systems (RMS). Particularly, sequencing of stages in a series or a parallel configuration impacts the responsiveness in addressing capacity change requirements. We analyze what type of configuration is more suitable for a manufacturer in terms of service level and cost. We propose a mixed integer programming model by incorporating various ramp-up time patterns, which define system scalability lead time. By solving the MIP model to optimality, we aim to see how capacity is allocated to RMS and DMS based on system cost, system responsiveness, and reconfiguration speed. A discrete event simulation model is used to validate the MIP results under uncertain demand scenarios.     

行业版ERP软件研发中的计划管理方法

把MRP的理念引入到基于系统化软件复用的行业版ERP软件研发的计划管理领域中。对有形产品与ERP软件产晶的相似性和差异性进行了分析,研究了在行业版ERP软件研发中引入MRP方法的可行性,提出了一种MRP和GT相结合的、针对行业版ERP软件研发中计划管理的解决方案。     

On stochastic dynamic programming for solving large-scale planning problems under uncertainty

The stochastic dynamic programming approach outlined here, makes use of the scenario tree in a back-to-front scheme. The multi-period stochastic problems, related to the subtrees whose root nodes are the starting nodes (i.e., scenario groups), are solved at each given stage along the time horizon. Each subproblem considers the effect of the stochasticity of the uncertain parameters from the periods of the given stage, by using curves that estimate the expected future value (EFV) of the objective function. Each subproblem is solved for a set of reference levels of the variables that also have nonzero elements in any of the previous stages besides the given stage. An appropriate sensitivity analysis of the objective function for each reference level of the linking variables allows us to estimate the EFV curves applicable to the scenario groups from the previous stages, until the curves for the first stage have been computed. An application of the scheme to the problem of production planning with logical constraints is presented. The aim of the problem consists of obtaining the planning of tactical production over the scenarios along the time horizon. The expected total cost is minimized to satisfy the product demand. Some computational experience is reported. The proposed approach compares favorably with a state-of-the-art optimization engine in instances on a very large scale.     

Two techniques of sensitivity and uncertainty analysis of fuzzy expert systems

Problems characterized by qualitative uncertainty described by expert judgments can be addressed by the fuzzy logic modeling paradigm, structured within a so-called fuzzy expert system (FES) to handle and propagate the qualitative, linguistic assessments by the experts. Once constructed, the FES model should be verified to make sure that it represents correctly the experts’ knowledge. For FES verification, typically there is not enough data to support and compare directly the expert- and FES-inferred solutions. Thus, there is the necessity to develop indirect methods for determining whether the expert system model provides a proper representation of the expert knowledge. A possible way to proceed is to examine the importance of the different input factors in determining the output of the FES model and to verify whether it is in agreement with the expert conceptualization of the model. In this view, two sensitivity and uncertainty analysis techniques applicable to generic FES models are proposed in this paper with the objective of providing appropriate tools of verification in support of the experts in the FES design phase. To analyze the insights gained by using the proposed techniques, a case study concerning a FES developed in the field of human reliability analysis has been considered.     

Minimizing total weighted flow time under uncertainty using dominance and a stability box

We consider an uncertain single-machine scheduling problem, in which the processing time of a job can take any real value on a given closed interval. The criterion is to minimize the total weighted flow time of the n jobs, where there is a weight associated with a job. We calculate a number of minimal dominant sets of the job permutations (a minimal dominant set contains at least one optimal permutation for each possible scenario). We introduce a new stability measure of a job permutation (a stability box) and derive an exact formula for the stability box, which runs in O(n log n) time. We investigate properties of a stability box. These properties allow us to develop an O(n²)-algorithm for constructing a permutation with the largest volume of a stability box. If several permutations have the largest volume of a stability box, the developed algorithm selects one of them due to a simple heuristic. The efficiency of the constructed permutation is demonstrated on a large set of randomly generated instances with 10≤n≤1000.     

网络流量技术应用与分析

随着因特网的高速发展，IP网络承载的数据流量越来越大，需要了解IP网络的整体性能及流量状况来适应业务的发展。本文阐述当前主要的流量技术NETFLOW与RMON2的原理、结构和关键技术，提出了基于NETFLOW技术的应用分析，并对RMON2与NETFLOW的应用进行了比较分析。     

需求不确定下考虑网络营销的供应链决策模型

利用报童理论和退货政策模型,研究了传统市场与电子商务市场的内在关系,建立了供应商参与两种市场联合营销情况下的供应链管理模型.重点分析了供应商如何在电子商务环境下组织生产,如何通过实施协作提高供应链总利润,如何评估两种市场上的销售策略.最后采用算例分析的方法,证明了模型和算法的有效性和可行性.     

Fuzzy two-stage quadratic programming for planning solid waste management under uncertainty

In this study, a fuzzy two-stage quadratic programming (FTSQP) method is developed for planning waste-management systems under uncertainty. It incorporates approaches of fuzzy quadratic programming and two-stage stochastic programming within a general optimization framework, to better reflect uncertainties expressed as probability-density and fuzzy-membership functions. The FTSQP can be used for analyzing various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. Moreover, using fuzzy quadratic terms rather than linear ones, the proposed method can improve upon the existing fuzzy linear programs through (a) more effectively optimizing the general satisfaction of the objective and constraints, (b) minimizing the variation of satisfaction degrees among the constraints and leading to more robust solutions, and (c) reflecting the trade-off between the system cost and the constraint-violation risk. The developed method is applied to a case study of municipal solid waste management. The results indicate that reasonable solutions have been generated. They will allow in-depth analyses of trade-offs between environmental and economic objectives as well as those between system cost and decision-maker's satisfaction degree.     

A branch-and-bound algorithm for shift scheduling with stochastic nonstationary demand

Many shift scheduling algorithms presume that the staffing levels, required to ensure a target customer service, are known in advance. Determining these staffing requirements is often not straightforward, particularly in systems where the arrival rate fluctuates over the day. We present a branch-and-bound approach to estimate optimal shift schedules in systems with nonstationary stochastic demand and service level constraints. The algorithm is intended for personnel planning in service systems with limited opening hours (such as small call centers, banks, and retail stores). Our computational experiments show that the algorithm is efficient in avoiding regions of the solution space that cannot contain the optimum; moreover, it requires only a limited number of evaluations to encounter the estimated optimum. The quality of the starting solution is not a decisive factor for the algorithm׳s performance. Finally, by benchmarking our algorithm against two state-of-the-art algorithms, we show that our algorithm is very competitive, as it succeeds in finding a high-quality solution fast (i.e., with a limited number of simulations required in the search phase).     

Local Area Management and Planning (LAMP) in India

To assist decentralised planning, the Department of Science and Technology, Government of India has taken the initiative to develop a computer-based spatial information system for planning and management of the natural resources at the district and sub-district level. At present ten district level database centres called Natural Resources Data Management System (NRDMS) have been functioning in different geo-environments in India.
The present paper discusses the experience of the Centre for Applied Systems Analysis in Development (CASAD). CASAD is involved in managing the NRDMS database centre at Bankura district and was associated with the development of DSS models in Kheda district. Finally, a few case studies on the use of Decision Support Systems (DSS) for decentralised decision making at the district level are discussed.     

时间规划问题中R_时刻表及其应用

为了处理实际生活中出现的对有时间先后关系约束的事件进行排序的问题,诸如学校排课问题、运动会会场安排问题等一系列资源分配问题,文中实现了一种解决该问题的算法。该算法以文献[1]中的关系矩阵的方法为基础,构造以时间关系约束为条件的时刻表———R-时刻表的一种算法。利用该算法对运动会会场项目进行安排,结果表明在仅为时间关系约束的前提下取得了较好的效果,从而实现了对该类问题的一种新的解决方法。     

Ordering,wholesale pricing and lead-time decisions in a three-stage supply chain under demand uncertainty

This paper develops a game theoretic model of a three-stage supply chain consisting of one retailer, one manufacturer and one subcontractor to study ordering, wholesale pricing and lead-time decisions, where the manufacturer produces a seasonal/perishable product. We explicitly model the effects of the lead-time and the length of selling season on both demand uncertainty and inventory-holding costs. We present the equilibrium outcome of the decentralized supply chain. When the lead-time increases, we find that the retailer increases the order quantity, the manufacturer offers a lower unit-wholesale price and the subcontractor decreases its unit-wholesale price if the manufacturer subcontracts part of the retailer’s order. In the endogenous lead-time setting, we illustrate the effects of some factors such as unit holding cost and capacity on the equilibrium outcome. We find that a higher unit holding cost implies a lower optimal lead-time and order quantity while higher unit-wholesale prices; the basic demand uncertainty increases the optimal lead-time and order quantity while decreases the unit-wholesale prices. The effects of distribution form on equilibrium outcome/profits are investigated by employing a numerical example. The profit loss of decentralization decreases (increases) with the basic demand uncertainty and manufacturer’s capacity (mean demand).     

Fuzzy parametric programming model for multi-objective integrated solid waste management under uncertainty

Solid waste management is increasingly becoming a challenging task for the municipal authorities due to increasing waste quantities, changing waste composition, decreasing land availability for waste disposal sites and increasing awareness about the environmental risk associated with the waste management facilities. The present study focuses on the optimum selection of the treatment and disposal facilities, their capacity planning and waste allocation under uncertainty associated with the long-term planning for solid waste management. The fuzzy parametric programming model is based on a multi-objective, multi-period system for integrated planning for solid waste management. The model dynamically locates the facilities and allocates the waste considering fuzzy waste quantity and capacity of waste management facility. The model addresses uncertainty in waste quantity as well as uncertainties in the operating capacities of waste management facilities simultaneously. It was observed that uncertainty in waste quantity is likely to affect the planning for waste treatment/disposal facilities more as compared with the uncertainty in the capacities of the waste management facilities. The relationship between increase in waste quantity and increase in the total cost/risk involved in waste management is found to be nonlinear. Therefore, it is possible that a marginal change in waste quantity could increase the total cost/risk substantially. The information obtained from the analysis of modeling results can be effectively used for understanding the effect of changing the priorities and objectives of planning decisions on facility selections and waste diversions.     

Engineering computation under uncertainty - Capabilities of non-traditional models

This paper provides a review of various non-traditional uncertainty models for engineering computation and responds to the criticism of those models. This criticism imputes inappropriateness in representing uncertain quantities and an absence of numerically efficient algorithms to solve industry-sized problems. Non-traditional uncertainty models, however, run counter to this criticism by enabling the solution of problems that defy an appropriate treatment with traditional probabilistic computations due to non-frequentative characteristics, a lack of available information, or subjective influences. The usefulness of such models becomes evident in many cases within engineering practice. Examples include: numerical investigations in the early design stage, the consideration of exceptional environmental conditions and socio-economic changes, and the prediction of the behavior of novel materials based on limited test data. Non-traditional uncertainty models thus represent a beneficial supplement to the traditional probabilistic model and a sound basis for decision-making. In this paper non-probabilistic uncertainty modeling is discussed by means of interval modeling and fuzzy methods. Mixed, probabilistic/non-probabilistic uncertainty modeling is dealt with in the framework of imprecise probabilities possessing the selected components of evidence theory, interval probabilities, and fuzzy randomness. The capabilities of the approaches selected are addressed in view of realistic modeling and processing of uncertain quantities in engineering. Associated numerical methods for the processing of uncertainty through structural computations are elucidated and considered from a numerical efficiency perspective. The benefit of these particular developments is emphasized in conjunction with the meaning of the uncertain results and in view of engineering applications.     

需求不确定条件下柔性供应链生产决策模型及优化

研究需求不确定条件下柔性供应链生产决策优化问题，以供应链期望总成本最小化为目标函数，考虑生产柔性为约束条件，建立了柔性供应链生产决策模型；通过优化分析，给出了供应链生产柔性有效边界的定义和经济意义，在此基础上引入成本柔性系数最小化的判断标准，从而得到最优方案的确定方法，最后通过算例对模型进行了验证。     

Advanced Resource Planning

Advanced Resources Planning hits the bottom of what we know as aggregate planning. This approach differs from other approaches in that it explicitly recognizes the stochastic nature of manufacturing systems. Therefore, it is an ideal high-level tuning and planning tool which can be used in various planning environments like MRP, ERP, JIT, Load-Oriented Planning, Theory of Constraints, Finite Scheduling, POLCA systems, and perhaps many more. The main purpose is to set aggregate planning parameters right before diving into any other operational planning decision. In this sense, we opt to offer realistic lead time estimations, lot sizes, utilization levels, customer service levels and quoted delivery times.The underlying approach is a waiting line network, which is heavily adapted in order to make it useful for planning purposes. The main feature is that both input parameters and output parameters are considered as stochastic variables. In this way it allows us to model manufacturing environments in a more realistic and intuitive way, including all kinds of uncertainty and variability. As a consequence, the output of the planning effort is also a stochastic variable: it has an average, a variance and the entire lead time distribution. The latter makes it possible to obtain high customer service levels or to establish realistic delivery times, which can be met with a high probability.This mathematical approach as such is not suited for people operating a manufacturing system. We illustrate the approach with software, named i-CLIPS, and we review some implementations and their results.     

电网规划基础数据管理相关问题解析

在实际的电网规划基础数据管理工作中,由于没有建立明确的数据管理流程,难以保证数据的有效性,再加上电网数据统计方式比较落后,导致基础数据管理的信息化和智能化程度较低。针对这些问题,电力企业要做好电网规划基础数据的管理工作,建立电网规划基础数据库,加快电网信息化建设的进程。主要分析、探讨了电网规划基础数据管理的相关问题。