多产品串并联生产系统动态看板的设计和计算

针对机电产品企业普遍采用的多品种、串并联生产系统,提出了批量轮番生产方式下采用动态看板作为控制手段时看板内容的设置和看板数量的计算方法,使得看板的内容和数量能随“需求”而动态变化,实现了“多品种均衡”生产和现场在制品数量的有效控制。     

Gated MaxWIP: A strategy for controlling multistage production systems

This study considers serial production systems with exponential processing times. The systems are balanced, i.e. all resources have the same production rate capacity. Preceding the system there is a 'gate' set to regulate the flow of material into the system. A control strategy directs each resource when to work or stay idle. The trade-off is between throughput (TP) and work-in-process (WIP). G-MaxWIP is a production control strategy allowing resources, except for the gate, to work unconstrained. The gate 'shuts' once the system's WIP reaches a maximum allowable level. We study the properties of G-MaxWIP and compare it to 'simple pull' and CONWIP. Our main findings are: G-MaxWIP is superior to simple pull. For any simple pull system operating a fixed set of resources it is possible to find G-MaxWIP systems that operate the same resources with less WIP while maintaining the same (or higher) TP. G-MaxWIP is superior to CONWIP. For any CONWIP system operating a fixed set of resources it is possible to find G-MaxWIP systems that operate the same resources with less WIP while maintaining the same (or higher) TP.     

AMHS capacity determination model for wafer fabrication based on production performance optimization

Automatic material handling system (AMHS) is becoming more important in 300?mm wafer fabrication factories (fab). Effective and efficient design and control of AMHS has become more critical particularly in capacity planning. The major concept of the AMHS capacity determination model is to maintain the originally designed optimal production throughput or cycle time of products. In order to maintain fab’s throughput or cycle time of products, WIP (work in process) portfolio of the constraint or the fastest workstation should be kept. Based on this concept, a GI/G/m queuing model based on FCFS (First-come-first-serve) dispatching rule of AMHS is applied to determine the required number of vehicles. Basically, products should be transported to the specific workstation (constraint or fastest workstation) before the workstation finishes the existing process; therefore, sufficient WIP in front of this specific workstation should be kept. Under this condition, the probability that transportation time exceeds product processing time under a certain transportation capacity level can be calculated by the proposed model. Hence, we can get the required capacity of AMHS to achieve the probability target set in advance. Due to the capacity of AMHS can be set according to the acceptable probability of non-exceeding the processing time of the constraint or fastest workstation, the level of WIP in front of this workstation can be kept. It also can be ensured that AMHS will not affect the production performance as well as keep the reasonable investment level.     

Setting WIP levels with statistical throughput control (STC) in CONWIP production lines

We develop a simple adaptive production control method for setting WIP levels to meet target production rates in a pull production operating under the CONWIP (Constant work in process) protocol. This method, termed Statistical Throughput Control (STC), uses real-time data to automatically adjust WIP levels (via kanban cards) in the face of noisy estimates of throughput. Because STC does not rely on a steady-state model, it is well-suited to systems subject to environmental changes such as those induced by continuous improvement efforts. Using simulation, we demonstrate the effectivness of STC under a variety of conditions, including single and multiple products, simple flow lines, routeings with shared resources and assembly systems.     

Scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

Multi-product production systems with sequence-dependent setup times are typical in the manufacturing of semiconductor chips and other electronic products. In such systems, the scheduling policies coordinating the production of multiple product types play an important role. In this paper, we study a multi-product manufacturing system with finite buffers, sequence-dependent setup times and various scheduling policies. Using continuous-time Markov chain models, we evaluate the performance of such systems under seven scheduling policies, i.e. cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing time), longest queue, longest processing time, and longest overall time. The impacts of these policies on system throughput are compared, and the conditions characterising the superiority of each policy are investigated. The results of this work can provide production engineers and supervisors practical guidance to operate multi-product manufacturing systems with sequence-dependent setups.     

Two-level hedging point control of a manufacturing system with multiple product-types and uncertain demands

This research is motivated by the co-operative production process of networked manufacturing systems (NMS). Manufacturing resource sharing and flexible production scheduling are two features of NMS. For an individual manufacturing system in an NMS, ‘flexible production scheduling’ means that it can produce multiple product-types and the switching of products is quick enough to respond to the demand fluctuation. ‘Manufacturing resource sharing’ means the utilisation of extra production capacity from other manufacturing systems in the NMS. Of course, that will bring extra cost. This paper focuses on the optimal production control problem of such a situation: one manufacturing system, multiple product-types, and uncertain demands. Here, it is assumed that there are two demand-levels for each product-type: the lower one and the higher one. The total normal production capacity is larger than the total lower demands and smaller than the total higher demands. If the total demands cannot be satisfied and the work-in-process (WIP) of all product-types decrease to a certain level, e.g. zero WIP, the extra production capacity may be utilised. For such a system, a new two-level hedging point policy is proposed, in which two hedging points (a higher one and a lower one) are given for each product-type. Different from the prioritised hedging point (PHP) policy which is usually applied to one-machine and multiple part-type systems, our control policy considers all part-types at the same prioritised level and keeps the work-in-process states of all product-types on a straight line in the state space. Thus, the total costs for WIP inventory and the occupation of extra capacity can be obtained in a closed form, which is a function with respect to the hedging points. Then the method for optimising the hedging points is proposed and the special structure of the optimal hedging point is obtained. Numerical experiments verify the optimality and the special structure of the hedging point obtained by our method.     

Dynamic card number adjusting strategy in card-based production system

In a production line, throughput rate changes according to WIP (work in process) level and WIP is controlled through the number of production cards in a card-based production system. This paper presents a fast tuning card adjusting procedure using the CONWIP system, which can adjust the number of production cards to ensure desirable throughput rate. The method, termed DTC (difference throughput control), is based on the concept of classic automatic control theory. Whenever a TTH (target throughput value) is set, the difference between TTH and RTH (real time throughput value) is used to adjusting the number of cards. We test this procedure with an existing one in a variety of scenarios; the results show that the proposed procedure is competitive. We also analyse how processing time impacts on the performance of CONWIP system through simulation data.     

Push and pull strategies for controlling multistage production systems

This study considers push and pull strategies to control multistage production systems with random processing times. Such systems are important as they mirror the level of complexity often encountered in practice. We start with definitions of push and pull systems, and develop a framework to compare multistage production systems based upon work-in-process (WIP) and throughput (TP) tradeoff. Surprisingly, we find that often push out performs pull, i.e. push systems accumulate less WIP than pull systems, while maintaining higher PT Concerning pull systems we find that WIP linearly increases in the number of stages and that WIP is not affected by variation in processing time. Concerning push systems we find that the release of material into the system in deterministic time intervals greatly improves performance.     

Job order releasing and throughput planning for multi-priority orders in wafer fabs

To meet the production target of multi-level (multiple priority rank) orders in wafer fabs, this paper uses a hierarchical framework based on a mathematical model, and without the assistance of any simulation tool, to build a production scheduling system to plan wafer lot releasing sequence and time. This system first applies capacity loading analysis to set up the batch policy for each level (rank) of orders. Next, the production cycle time of each product level is estimated with considerations of batching and loading factor. The cycle time is then used to derive system control parameters such as the most appropriate level of work in process (WIP) and the number of daily operations on the bottleneck workstation. Lastly, a Constant WIP mechanism is applied to establish a wafer release sequence table and a throughput timetable. The due date designation for each specific order can hence be confirmed. With the comparison with the result of simulation, it shows that under the designed system the performance and planning measures in the master production schedule can be drawn up quickly and accurately, and the system throughput target and due date satisfaction can be achieved. Overall, the proposed production scheduling system is both effective and practicable, and the planning results are supportive for good target planning and production activity control.     

Critical WIP loops: a mechanism for material flow control in flow lines

Critical WIP loops (CWIPL) is a proposed material flow control mechanism for a balanced flow line environment aiming at improving throughput and lead time. The mechanism establishes critical loops which their WIP identifies the time of releasing raw material to the line. So, through control of WIP level of critical loops the material flow is managed. The proposed mechanism releases the raw material to the line if the ‘total WIP of the line’ or ‘the WIP of the last machine’ is less than the limit. Besides the aforementioned condition, the necessary condition for releasing the raw material to the line is ‘idleness of the first machine’. Simulation is used to compare the performance of the CWIPL, CONWIP and G-MaxWIP. Different line characteristics such as number of machines, processing time distributions and the maximum WIP level of the line are considered in numerical examples. The results show that CWIPL improves both throughput and lead time compared with CONWIP, while CWIPL has better results than G-MaxWIP with respect to both throughput and lead time in the flow line that has less than nine machines.     

Solving coupled task assignment and capacity planning problems for a job shop by using a concurrent genetic algorithm

The problems of task assignment and capacity planning of manufacturing systems have been researched for many years. However, in the existing literature, these two types of problems are researched independently. Namely, when solving the task assignment problem, it is usually assumed that the production capacity of the manufacturing systems has been determined. On the other hand, when solving the capacity planning problem, the production tasks assigned to the workstations in the manufacturing system have also been determined. Actually, the task assignment problem and the capacity planning problem are coupled with each other. When we assign production tasks to workstations, production capacities of these workstations should be regulated so that they are enough for completing the tasks. At the same time, when planning the production capacity, we must know what production tasks are assigned to what workstations. This research focuses on the coupling relations between the two problems for a closed job shop, in which the total work-in-process (WIP) is assumed to be constant. The objective of the task assignment problem is to balance the workloads of the workstations and the objectives of the capacity planning problem are maximising the throughput and minimising total costs of machine purchasing and WIP inventory. We construct the fundamental system architecture for controlling the two coupled optimisation processes, and propose a concurrent genetic algorithm (CGA) to solve the two coupled optimisation problems. The influences of the decision variables of one problem on the objective function of the other problem are taken into consideration when the fitness functions of the CGA are constructed. Numerical experiments are done to verify the effectiveness of the algorithm.     

Total WIP and WIP mix for a CONWIP controlled job shop

A planning procedure to set the constant level of Work-In-Process (WIP) for each product type in a job shop operated under CONWIP control is developed. We model the job shop as a single chain multiple class closed queuing network. Given a specified product mix and a total WIP, a nonlinear program bounds the throughput of the network and optimizes the WIP mix. We identify the minimum total WIP that is guaranteed to yield throughput near the maximum possible for the specified product mix and set individual WIP levels by multiplying the optimal WIP mix proportions by the minimum total WIP. Numerical examples illustrate how these individual product WIP levels achieve the goal of high throughput consistent with the specified product mix.     

Performance evaluation and capacity planning in a metallurgical job-shop system using open queueing network models

In this paper, we apply performance evaluation and capacity allocation models to support decisions in the design (or redesign) and planning of a job-shop queueing network of a metallurgical plant. Approximate parametric decomposition methods are used to evaluate system performance measures, such as the expected work-in-process (WIP) and production leadtimes. Based on these methods, optimisation models are then applied for the allocation (or reallocation) of capacity to the stations of the job-shop network. These models are also used to generate approximate trade-off curves between capacity investment and WIP or leadtime, which are valuable for a production manager to estimate how much capacity should be allocated to the stations to reach some targeted performance measures. These curves are also useful for the sensitivity analysis of the solutions to changes in the input parameters, such as the variability of the product demands, the mix of the production and the throughput rate of the network.     

Combining Innovation and Capacity Utilization in High Throughput Systems: Moving Beyond the Product Life Cycle Model by Introducing Second-Order Innovations

The literature on the product life cycle and on high throughput systems has been preoccupied with studying an apparent lack of flexibility in capital-intensive production systems. Companies in capital-intensive industries need to maintain a high level of capacity utilization in order to stay economically viable, however, their efforts to uphold the throughput of their systems often have the unforeseen and unintended consequence of limiting their ability to introduce new products and services. Nevertheless, some companies have managed to resolve these tensions by introducing what we describe as “second-order innovations”, a type of innovation which acts on the innovation process itself and enables new products and services to be introduced without a steep decline in capacity utilization. By focusing on these cases and discussing their theoretical implications, we want to contribute to the existing literature on high throughput systems by identifying key mechanisms for introducing and maintaining such second-order innovations and describing the patterns of industrial evolution that they create.     

有限能力按订单生产厂商的生产订购策略分析

考虑一个具有多种零件、多种订单的按订单生产系统,厂商具有有限生产能力.为了充分利用生产能力,最大化利润,厂商可以在接到顾客具体订货数量之前和之后两个阶段分别订购零件和装配产品.首先通过分析订单生产的优先级别,得到厂商在第二阶段的生产、订购策略.然后以此为基础,建立了一个有约束的利润最大化模型,利用遗传算法求解该模型,可以得到厂商在第一阶段的最优生产、订购策略.最后利用数值模拟表明了模型和算法的有效性.     

Throughput performance analysis and machine layout for discrete-space closed-loop conveyors

In this paper we analyze the performance of discrete-space, fixed-window, closed-loop conveyors. Given the job flow and routing data as well as the configuration of the loop, we first derive the stability condition for the conveyor system. We then address the layout of the stations around the loop. We examine basic tradeoffs with respect to throughput capacity versus the expected Work-In-Process (WIP) on the conveyor for alternative layouts. We show that a layout that minimizes the expected WIP on the conveyor is not, in general, the optimum layout for maximizing the throughput capacity of the system. Last, we discuss the relation between certain conveyor parameters (such as speed and loop length) and system performance.     

Multi-dimensional clearing functions for aggregate capacity modelling in multi-stage production systems

Nonlinear clearing functions have been proposed in the literature as metamodels to represent the behaviour of production resources that can be embedded in optimisation models for production planning. However, most clearing functions tested to date use a single-state variable to represent aggregate system workload over all products, which performs poorly when product mix affects system throughput. Clearing functions using multiple-state variables have shown promise, but require significant computational effort to fit the functions and to solve the resulting optimisation models. This paper examines the impact of aggregation in state variables on solution time and quality in multi-item multi-stage production systems with differing degrees of manufacturing flexibility. We propose multi-dimensional clearing functions using alternative aggregations of state variables, and evaluate their performance in computational experiments. We find that at low utilisation, aggregation of state variables has little effect on system performance; multi-dimensional clearing functions outperform single-dimensional ones in general; and increasing manufacturing flexibility allows the use of aggregate clearing functions with little loss of solution quality.     

An exploratory study of protective inventory in a re-entrant line with protective capacity

While the effect of protective inventory on the performance of simple lines has received considerable attention, the same cannot be said for re-entrant lines. This paper attempts to meet that deficiency. This paper examines two different but related issues. First, the theory of constraints (TOC) evaporating cloud method is employed to show the traditional dilemma of increasing work-in-process (WIP) to fully utilise resources versus decreasing WIP inventory to reduce cycle time. The assumptions and implications of three different management philosophies (traditional, JIT/lean, and TOC) are explored in addressing this dilemma with respect to the use of both protective inventory and protective capacity. Second, given an unbalanced re-entrant line with fixed capacity, simulation is used to explore the effectiveness of using protective inventory by changing the level of WIP on two dependent variables: cycle time and throughput. Two sources of variation are simulated: processing time and breakdowns (machine failures). At a given WIP level, it was found that the amount of protective capacity at non-bottlenecks changed with increases in variability. Therefore the level of WIP inventory (with its protective inventory) and the level of protective capacity needed to protect against variability play a critical role in determining cycle time and throughput of the re-entrant line. While this is an exploratory study, comments on protective inventory and protective capacity are provided based on the three different management philosophies.     

Determining inventory levels in a CONWIP controlled job shop

We extend the concept of CONWIP control to a job shop setting, in which multiple products with distinct routings compete for the same set of resources. The problem is to determine the fixed overall WIP level and its allocation to product types (WIP mix) to meet a uniformly high customer service requirement for each product type. We formulate an optimization problem for an open queuing network model in which customer orders pull completed products from the system. Then, assuming heavy demand, we derive a throughput target for each product type in a closed queuing network and provide a simple heuristic to find a minimum total WJP and WIP mix that will achieve an operating throughput close to this target. In numerical examples, the WIP mix suggested by this approach achieves the customer service requirement with a relatively low total WIP     

Production planning system for a combination of make-to-stock and make-to-order products

This paper deals with the problem of designing a production planning system for a combination of make-to-stock (MTS) and make-to-order (MTO) products where the production facility produces both types of products. Important management points in such production systems are to design an efficient production planning system which can shorten the manufacturing time of MTO products as well controlling the unfilled rate of MTS products to the market demand at a low level. A hierarchical production planning model is introduced in order to design the production planning system. The buffer capacity is set as a design variable for determining production capacity at a higher planning level, and the rule for allocating the production capacity to types of products is adopted as a design variable at a lower level. First, we analyse how these two design variables affect the unfilled rate of MTS products and the average manufacturing time of MTO products. Second, we clarify the relationship of the buffer capacity with the manufacturing time of MTO products and with the required buffer inventory level of MTS products which can maintain the unfilled rate of MTS products to the market demand within an acceptable limit. Third, we show an example of the range of design variables which can control the unfilled rate of MTS products and the manufacturing time of MTO products within their acceptable limits, if the individual limits of the performance measure is given to respond to customer orders quickly.