多品种集约生产计划问题的模糊方法

提出具有模糊需求量和模糊能力约束以及资本水平约束的多品种类集约生产计划问题 的模糊优化模型及模糊解方法. 通过对模糊需求量和模糊等式的描述,提出了模糊需求环境下 生产- 库存平衡方程的两种等价的描述方法, 并给出了模糊等式的实用解释. 建立了具有模 糊需求量和模糊能力约束集约生产计划问题的优化模型FMAPP ,并给出了求解模型的参数规 划方法.     

Simulation-based performance assessment of master planning approaches in semiconductor manufacturing

Semiconductor manufacturing is confronted with a large number of products whose mix is changing over time, heterogeneous fabrication processes, re-entrant flows of material, and different sources of environmental and system uncertainty. In this context, the mid-term production planning approach, i.e., master planning, typically does not capture the entire complexity of the shop-floor. It deals with an aggregated representation of the production system. There is a need for evaluating the planning algorithm in use while taking the execution level into account. Therefore, we introduce in this paper a simulation-based framework that allows for modeling the behavior of the market demand and the production system. An appropriate performance assessment methodology is proposed. The performance of two heuristic approaches for master planning in semiconductor manufacturing, a genetic algorithm and a rule-based assignment procedure, is evaluated within a rolling horizon setting while considering demand and execution uncertainty. A reduced discrete-event simulation model is used to mimic a one-stage network of wafer fabrication facilities. The results of simulation experiments are discussed.     

Forecasting Product Returns for Remanufacturing Operations

Driven by legislative pressures, an increasing number of manufacturing companies have been implementing comprehensive recycling and remanufacturing programs. The accurate forecasting of product returns is important for procurement decisions, production planning, and inventory and disposal management in such remanufacturing operations. In this study, we consider a manufacturer that also acts as a remanufacturer, and develop a generalized forecasting approach to determine the distribution of the returns of used products, as well as integrate it with an inventory model to enable production planning and control. We compare our forecasting approach to previous models and show that our approach is more consistent with continuous time, provides accurate estimates when the return lags are exponential in nature, and results in fewer units being held in inventory on average. The analysis revealed that these gains in accuracy resulted in the most cost savings when demand volumes for remanufactured products were high compared to the volume of returned products. Such situations require the frequent acquisition of cores to meet demand. The results show that significant cost savings can be achieved by using the proposed approach for sourcing product returns.     

Concurrent consideration of product design,process planning and production planning activities

In manufacturing engineering, product design, process planning and production planning activities are often considered independently. However, in order to effectively respond to changes in business situations, such as changes in demand forecast, product mix and technology, it is desirable to consider them concurrently. For this purpose, a large-scale linear programming model has been developed. The model considers minimization of the sum of processing cost, late shipment cost and inventory holding cost as the objective, and concurrently selects product designs, and generates process plans and production plans. The number of columns in the formulation can be large and, hence, an efficient column generation scheme is developed to solve the model. The model and solution procedure are illustrated with examples.     

A note on supply risk and inventory outsourcing

To fully accommodate the correlations between semiconductor product demands and external information such as the end market trends or regional economy growth, a linear dynamic system is introduced in this paper to improve the forecasting performance in supply chain operations. In conjunction with the generic Gaussian noise assumptions, the proposed state-space model leads to an expectation-maximisation (EM) algorithm to estimate model parameters and predict production demands. When the dimension of external indicators is high, principal component analysis (PCA) is applied to reduce the model order and corresponding computational complexity without loss of substantial statistical information. Experimental study on some real electronic products demonstrates that this forecasting methodology produces more accurate predictions than other conventional approaches, which thereby helps improve the production planning and the quality of semiconductor supply chain management.     

Incentive Schemes for Semiconductor Capacity Allocation: A Game Theoretic Analysis

We study incentive issues that arise in semiconductor capacity planning and allocation. Motivated by our experience at a major U. S. semiconductor manufacturer, we model the capacity‐allocation problem in a game‐theoretic setting as follows: each product manager (PM) is responsible for a certain product line, while privately owning demand information through regular interaction with the customers. Capacity‐allocation is carried out by the corporate headquarters (HQ), which allocates manufacturing capacity to product lines based on demand information reported by the PMs. We show that PMs have an incentive to manipulate demand information to increase their expected allocation, and that a carefully designed coordination mechanism is essential for HQ to implement the optimal allocation. To this end, we design an incentive scheme through bonus payments and participation charges that elicits private demand information from the PMs. We show that the mechanism achieves budget‐balance and voluntary‐participation requirements simultaneously. The results provide important insights into the treatment of misaligned incentives in the context of semiconductor capacity‐allocation.     

基于同质设备多台配置的独立生产单元构建决策

单元化生产作为一种较好实现生产柔性与生产效率融合的先进生产方式,在变种变量需求环境下它已被大量生产企业特别是装配式生产企业所采用。生产单元构建问题是单元化生产系统设计的关键问题和首要问题,也是单元化生产研究领域的一个热点。本文研究设备易复制情形下,通过配置多台同质设备来实现生产单元间无物料移动,并保证生产单元间工作量均衡的独立生产单元构建问题。本文在综合考虑换装时间、加工顺序、设备生产能力、产品需求量等多个实际生产要素的基础上,建立了以平均总流程时间和各生产单元总流程时间与平均总流程时间偏差最小为目标的数学模型,并开发了一个启发式算法来求解数学模型,最后通过数值算例验证了模型和算法的有效性。     

Development and analysis of mathematical and simulation models of decision-making tools for remanufacturing

This article presents decision-making tools for remanufacturing. The first decision-making tool was used to address inventory lot-sizing problems in a hybrid remanufacturing–manufacturing system with varying remanufacturing fraction. In this article, the new inventory lot-sizing model with variable remanufacturing lot sizes has been shown to exhibit better performance than the benchmark model with fixed remanufacturing lot sizes. The new inventory lot-sizing model is anticipated to become a valuable decision-making tool in companies that are planning to adopt remanufacturing. The second decision-making tool was applied to address a production and inventory planning problem in a remanufacturing system considering different remanufacturing policies for a given remanufacturing strategy. For a remanufacture-to-stock system with two quality remanufacturables groups four alternative policies were examined, a policy which specifies simultaneous processing utilising dedicated resources was shown to be the best policy to achieve a shorter remanufacturing cycle time. For a remanufacture-to-order system with two quality remanufacturables groups, the three relevant policies of the four alternative policies were examined, a policy which specifies sequential processing and switching between various quality remanufacturables groups was shown to be the best policy to achieve a shorter remanufacturing cycle time. The production and inventory planning simulation models in a remanufacturing system are expected to become significant decision-making tools in remanufacturing operations.     

Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives

Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.     

EXPLAINING PLANT-LEVEL DIFFERENCES IN MANUFACTURING OVERHEAD: STRUCTURAL AND EXECUTIONAL COST DRIVERS IN THE WORLD AUTO INDUSTRY

Using data from 62 automobile assembly plants worldwide, we examine the extent to which various structural cost drivers (plant scale, automation, and product mix complexity) and exe-cutionalcost drivers (product manufacturability, management policies, and production practices) account for plant-level differences in manufacturing overhead. Our analysis of structural drivers finds that overhead hours per vehicle are negatively associated with production volumes, consistent with economies of scale in overhead functions. However, automation appears to have little influence on overhead levels. Product mix complexity's effect on overhead requirements varies with the complexity's form and the plant's manufacturing capabilities. Option and parts complexity (measures of peripheral and intermediate product variety) both exhibit adverse effects on overhead, reflecting the considerable logistical, coordination, and supervisory challenges that accompany an increased number of parts and more complex manufacturing tasks. In contrast, model mix complexity (a measure of fundamental variety) appears to have little impact on direct or overhead labor requirements in auto assembly plants. Our analysis of executional drivers provides empirical support for the claim that advanced manufacturing practices such as the reduction of buffers, multiskilled production workers, and the use of teams can lower overhead costs. We also find preliminary evidence that the lower overhead costs in Japanese auto assembly plants are due primarily to the use of multiskilled work teams and the shifting of traditional overhead activities to production workers. Overall, our results indicate that manufacturing overhead is a function not only of the structural cost drivers that have dominated the academic literature but also of executional cost drivers that are harder to duplicate and therefore potentially more valuable for achieving competitive advantage.     

A neural network clustering model for miscellaneous components production planning

The growing importance of providing service to customers, e.g. post-sale assistance, supplying of spare parts, upgrading and integration of new elements in installed systems, enhances the importance of planning and management of upgrading parts in most manufacturing industries. These parts are generally characterized by high technical heterogeneity and have a highly variable and difficult to forecast demand. In some areas (especially the most dynamic, e.g. high-tech products), these kinds of components are quite common, and represent a very strong relation between the manufacturing firm and the market. These parts are generally too many to be efectively supported on a planning database system with individual records and too heterogeneous ( and sometimes with a too high value) to be supported all together in a single record. In this paper, we want to study the application of adaptive techniques for the clustering of these components in classes based on the similarities in their market behaviour in order to build an optimal database for planning production and supplying of these components.     

The Production of Several Items in a Single Facility with Linearly Changing Demand Rates

In this paper we extend the ELSP model to allow for linearly changing demand rates over a fixed planning horizon. This extension of the ELSP research provides a model that can be used in coordinating the production and marketing planning activities in a firm. The model allows the user to evaluate the impact of changes in product demand on production costs and customer service. We solve the model using a standard nonlinear programming package (MINOS) and show through examples based on actual production data how the model can be used to support coordinated production and marketing planning.     

Coping with Group Behaviors in Uncertain Quality Function Deployment

Quality function deployment (QFD) is a planning and problem‐solving tool gaining wide acceptance for translating customer needs (CNs) into technical attributes (TAs) of a product. It is a crucial step to derive the prioritization of TAs from CNs in QFD. However, it is not so straightforward to prioritize TAs due to two types of uncertainties: human subjective perception and user variability. The main focus of this article is to propose a group decision‐making approach to uncertain QFD with an application to a flexible manufacturing system design. The proposed approach performs computations solely based on the order‐based semantics of linguistic labels to eliminate the burden of quantifying qualitative concepts in QFD. Moreover, it incorporates the importance weights of users and the concept of fuzzy majority into aggregations of individual fuzzy preference relations of different TAs in order to model the group behaviors in QFD. Finally, based on a quantifier‐guided net flow score procedure, the proposed approach derives a priority ranking with a classification of TAs into important and unimportant ones so as to provide a better decision‐support to the decision‐maker. Due to the easiness in articulating preferential information, our approach can reduce the cognitive burden of QFD planning team and give a practical convenience in the process of QFD planning.     

A framework for production rescheduling in sociotechnical manufacturing environments

No plan survives contact with reality. Despite the rich research base regarding handling uncertainty in production planning and control systems, there is an intellectual gap between theory and practice with regard to handling unforeseen events generated by internal and external factors, such as unforeseen machine downtimes and changes in demand. Motivated by longitudinal observations in two industrial settings and an analysis of the relevant literature, a framework for rescheduling decision-making in the face of unforeseen production events is proposed. In practical settings, the effectiveness of decisions depends on a set of situational factors. The findings of this research can be utilised further to provide guidelines for developing effective decision support principles and systems, addressing the needs of organisational decision-makers.     

The marketing approach to purchasing

The determination of supply and demand is a complex problem to which both sides of the equation must contribute. Although marketing departments have organized themselves to provide such a contribution to data, purchasing has done little to promote its contribution to demand forecasting. Until it does the equation will remain not merely complex but insoluble.A continuous inter-change between marketing and purchasing, supplier and customer, is essential for improving the accuracy of longer range forecasting and for logistical co-ordination of investment planning. The corporate plans of manufacturing organizations, at least, should reflect these considerations.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.     

On flexible product-mix decision problems under randomness and fuzziness

This paper considers several models of product-mix decision problems and production planning problems under uncertain conditions, and shows that these are extensional and versatile models for resolving previous product-mix problems. These proposed models include randomness derived from statistical analysis based on historical data, ambiguity of decision maker's intuition and the quality of received information, and flexibility in accomplishing the original plan. Furthermore, given that the upper limit values of some constraints have flexibility, and given a decision maker's level of satisfaction, we propose a flexible product mix of problems using the theory of constraints (TOC), and develop an efficient solution method. We then provide a numerical example that compares our models with some previous basic models. Efficiency of flexibility is obtained when our proposed models are applied to several conditions, such as measurable changes from the expected value of future returns.     

Product mix in the TFT-LCD industry

As operational costs and equipment depreciation in the TFT-LCD (thin film transistor-liquid crystal display) industry are a high percentage of the total cost, most manufacturers usually fully utilise their production capacity to reduce the average unit cost. However, when the market demand is less than the supply the stock of panels increases; this forces manufacturers to instigate a price war to reduce levels of stock and results in a wide fluctuation in panel prices. Inventory stocks of panels could be decreased by optimising the product mix. This will help manufacturers to reduce the risk of holding stocks, increase profit, and improve competitive advantage. This study uses mixed integer linear programming (MILP) to construct a product mix for the TFT-LCD industry given the conditions of profit, productivity, raw materials supply, and market demand. A case study shows that this model is proven to be effective in generating product mix for the TFT-LCD industry while improving profit. The product mix generated by this model can provide a reference for the sales department for orders and shipping, for the production department for the order quantity, and for master production scheduling for each product.     

Group technology cell design: a case study

Rising production costs, combined with competitive pressures, have resulted in an increased focus on the need for effective manufacturing planning and control. To remain competitive, organizations are exploring new ways for increasing productivity while reducing work-in-progress inventories and product lead time. This article describes a case study of the successful introduction of production cells in a company engaged in small batch production. The company is described and its manufacturing problems presented. Following a detailed analysis of the utilization of machines in the company's machine shop, a new manufacturing technique, giving improved work flow, is proposed.     

Manufacturing feature-based cost management system: a case study in Brazil

Feature costing is a topic of recent discussion related to cost management systems (CMSs) aimed at understanding how product features influence production process costs. It raises as a key issue in mass customisation environments where a single product model can present hundreds of menu options. In this study, we explore the concept of feature costing beyond the use of activity-based costing (ABC). We propose a CMS based on features and common elements as costing objects instead of products. In our model, we use the unit of production effort method to organise the direct manufacturing costs, ABC method to address the indirect cost and the standard methods to compute raw materials costs. We illustrate the method in the production of bus seats in a Brazilian manufacturing company. This study gives different perspectives to the international academic community on the use of a different CMS used by many Brazilian companies.