CWIPL II,a mechanism for improving throughput and lead time in unbalanced flow line

Critical WIP loops II (CWIPL II) is a proposed material flow control mechanism for an unbalanced flow line environment. CWIPL II is based on CWIPL and it determines critical loops in unbalanced lines. The WIP of critical loops identifies the time of releasing raw material to the line. CWIPL II proposed a new classification for unbalanced flow line which is ‘near unbalanced flow line’ and ‘perfect unbalanced flow line’. In near unbalanced line, there is one bottleneck and a raw material release to the line if ‘WIP of the bottleneck’ or ‘WIP upstream the bottleneck’ is less than defined level. In perfect unbalanced line there are multiple bottleneck and a raw material release to the line if ‘WIP upstream the slowest machine’ or ‘WIP between two primary bottleneck’ is less than defined level. Like CWIPL, the necessary condition for releasing the raw material is ‘idleness of the first machine’. CWIPL II is compared with CONWIP and TOC by simulation. Different scenarios are employed in the comparison analysis. The scenarios address variables such as number of machines, processing time distribution, WIP target level. Location of slowest machine and location of two primary bottlenecks are considered in examples. Simulation results and statistical tests of 141 numerical examples show that CWIPL II improves lead time in near unbalanced line and throughput in perfect unbalanced line compared with TOC. Because of the trade off between line throughput and lead time, the mechanism that improves one of them while maintaining the other at previous level is valuable. It is shown that CWIPL II has improved TOC in the cases that TOC hasn’t improved CONWIP.     

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.     

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.     

Petri Net model of repetitive push manufacturing with Polca to minimise value-added WIP

A common simplifying assumption used in the literature is to minimise average Work In Process (WIP), while achieving maximum production rate, which does not consider the fact that the value of WIP increases down the stream of a production process as labour, time, energy and resources are added to it. This paper aims at minimising queues of parts waiting downstream of production process and in turn minimising value-added WIP by constraining more and more inventory of unfinished products at the earlier stages of production. For this reason, generic black token closed loop Petri Net (PN) model of Flexible Flow Shop (FFS) with Paired-cell Overlapping Loops with Card Authorisation (POLCA) is developed. Tokens in the control loops represent Polca cards to control the flow of material from order release till finished products. Marking of PN is done through Mix Integer Linear Programming after the computation of semi-positive P invariants. Simulation is being done to obtain queues of parts, waiting at each station during demand variation. Results were compared with PN model of FFS without Polca. Results showed the constriction of unnecessary WIP towards the initial stage of production instead of at bottlenecks, with proposed model, and in turn minimisation of value-added WIP.     

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.     

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     

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.     

考虑双目标的JIT生产系统看板数量设定

针对现有JIT系统看板数量决策问题研究多以单目标为主的不足,提出了一种基于实验设计的双目标JIT生产系统看板数量设定方法。该方法同时考虑了高订单满足率和低系统平均在制品水平的双目标优化,以B公司CR油嘴JIT生产系统为例,建立了该JIT生产线的Witness仿真模型以实现数据的收集,以各看板循环回路的看板数量和看板容量进行水平设定,并进行正交实验设计及数据的直观分析处理,然后采用全因子实验的方法,基于帕累托最优的思想获得生产系统看板数量帕累托最优解,形成近似最优看板数量组合的帕累托最优前沿。生产管理人员可根据不同的生产计划和绩效目标从组合中选择合适的看板数量。最后的研究结果验证了该方法的可行性和有效性。     

A neural network model for on-line control of flexible manufacturing systems

This research investigates the potential of using a neural network approach in real-time control of flexible manufacturing systems. A hierarchical manufacturing controller, consisting of two neural network structures, is proposed. The first neural system participates in the feasibility analysis, and the other, at the lower level, in the process of dispatching and control. At the first level, a Sigma-Pi type of connection is used to translate work-in-process (WIP) move requests into directed arcs. Through a filter scheme, infeasible arcs are identified and eliminated from further consideration. At the second level, a modified Hopfield-Tank model is developed to determine the correct moves. Its goal is to deliver the right WIP to the right workstation, and process it at the right time. An example is used throughout the paper to illustrate the architecture developed. This two-phase control procedure provides adaptability, speed, and good solution quality which are important for real-time control of flexible manufacturing systems.     

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.     

Base stock versus WIP cap in single-stage make-to-stock production–inventory systems

A single-stage production-inventory system produces parts in a make-to-stock mode, and unsatisfied demand is backordered. The system operates under a so-called base stock with WIP cap replenishment policy, which works as follows. Whenever the Work-In-Process (WIP) plus finished goods inventory falls below a specified level, called base stock, a replenishment order for the production of a new part is issued. If the WIP inventory is below a different specified level, called WIP cap, the order goes through and a new part is released for production; otherwise, the order is put on hold until the WIP inventory drops below the WIP cap. First, it is shown that the optimal base stock that minimizes the long-run, average, inventory holding cost for a given minimum customer service level, is a non-increasing function of the WIP cap that reaches a minimum value, called minimum optimal base stock, at a finite WIP cap value, called critical WIP cap. Then, it is shown that the optimal WIP cap is less than or equal to the critical WIP cap and therefore the optima! base stock is greater than or equal to the minimum optimal base stock. More interestingly, however, it is conjectured that the optimal WIP cap is in fact exactly equal to the critical WIP cap and therefore the optimal base stock is exactly equal to the minimum optimal base stock. The minimum optimal base stock is none other than the optimal base slock of the same system operating under a classical base stock policy (with no WIP cap). Finally, the optimal parameters of a system operating under a base stock with WIP cap policy are related to the optimal parameter of the same system operating under a make-to-stock CONWIP policy.     

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.     

Design and storage cycle time analysis for the automated storage system with a conveyor and a rotary rack

In this paper, we analyse the performance of an automated Work-In-Process (WIP) storage system consisting of a conveyor and a rotary rack. The stability condition and the expected storage cycle time are derived to analyse the performance of the WIP storage system. As part of the storage cycle time analysis, the derived expected waiting times at the conveyor and the rotary rack are important performance measures that can be used for buffer-sizing purpose in such systems. Given the fixed storage space of the rotary rack, we also develop a heuristic approach to determine the near optimum ‘shape’ of the rotary rack so that the expected storage cycle time is minimized. Numerical results are presented to examine the storage cycle time model and the proposed shape design. The analytical model introduces a simple approach over simulation with acceptable accuracy; it is useful when designing such WIP storage systems. Moreover, it can be expanded to model more complex systems. The derived model also provides insightful information on the design parameters that a typical simulation tool can hardly provide.     

光通讯产品在制品数量目标研究

以实际的光通讯生产线在制品数量为研究对象,研究生产线在制品数量的设定方法。分别运用分段计算方法和排队论的［M/M/s］∶［∞/∞/FCFS］ 模型以及［M/M/s］∶［N/∞/FCFS］模型研究其目标值,分析结果表明： ［M/M/s］∶［N/∞/FCFS］模型建立的目标值切合实际,故以此为目标,提出生产管理改善措施,实现在制品数量目标控制,使在制品数量降低50%,生产周期减少50%,流动资金占用减少1875万元,很好地验证了该方法的合理性和科学性。     

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.     

酒品包装生产中动态缓冲管理机制

考虑存在资源约束的流线式酒品包装生产车间,为了减少企业生产成本,提出了一种以控制在制品(WIP)库存为主要参考指标的动态缓冲管理方法。本方法通过实时监控缓冲区的WIP库存,根据在监控窗口中缓冲区出现WIP库存由高于安全下限变化到低于安全下限的频率以及随后是否出现WIP库存耗尽的情况,对库存安全上下限进行动态调节进而实现对缓冲的动态管理。为了验证方法的有效性,安排了3组不同的仿真实验。实验结果表明：与传统的缓冲管理策略相比,基于动态缓冲管理策略的控制方法在WIP库存控制方面存在22%以上的优势,采用该方法可以有效的控制生产系统中的在制品库存。     

Shift scheduling for steppers in the semiconductor wafer fabrication process

In this paper, an approach is proposed for scheduling stepper machines that are acting as bottleneck machines in the semiconductor wafer fabrication process. We consider the problem of scheduling the steppers for an 8 hour shift, determining which types of wafer lots to work on each machine. The scheduling objective is to find the optimal stepper allocations such that the schedule meets target production quantities that have been derived from the given target Work-In-Process (WIP) levels. A Mixed Integer Programming (MIP) model is formulated, and three heuristic approaches are proposed and tested to approximately solve the M1P model. Numerical tests show that one of the proposed heuristics using linear programming relaxation of MIP generates, on average, schedules within 5° of the optimum values.     

Scheduling manufacturing systems with work-in-process inventory control: single-part-type systems

A real-time algorithm is developed for scheduling single-part-type production lines with work-in-process inventory buffers. We consider three classes of activities: operations, failures and repairs, and starvation and blockage. The scheduling objectives are to keep the actual production close to the demand, the work-in-process (WIP) inventory level low, and the cycle time short. A three-level hierardhical controller is constructed to regulate the production. At the top level, we determine the desirable buffer sizes and the target production level for each operation. At the middle level is a production flow rate controller that recalculates the production rates whenever a machine fails or is starved or blocked. The loading times for individual parts are determined at the bottom level of the hierarchy. The production scheduling algorithm is evaluated by using computer simulations for a variety of cases. Compared with a transfer line policy, a significant improvement in system performance is observed.     

Determining the number of kanbans: a step toward non-stock-production

Just-in-time (JIT) production is a philosophy that calls for reducing work-in-process (WIP) inventory to aid process improvement and reduce process variability. In some cases, JIT production has been misinterpreted as a method that would lead to zero or minimal WIP with a lot size of one. There are no models or theories to achieve the JIT goal, i.e. non-stock-production (NSP), and, in particular, to help determine when and where to maintain this minimal inventory. A kanban system acts as the nerve of a JIT production system whose functions are to direct materials just-in-time to workstations in stages of manufacturing, and to pass information as to what and how much to produce. Indeed, the number of kanbans between two adjacent workstations decides the inventory level of that pair of workstations. With the objective of minimizing WIP inventory level, one model dealing with three cases of production configuration is developed for deciding the optimum number of kanbans. The model is then solved using a Markov process approach which considers the demand of finished products as the departure rate and the production rates of stations as arrival rate. In this paper, the model and solution procedure are illustrated with a numerical example.     

Base-stock control for single-product tandem make-to-stock systems

In this paper, we consider a multiple-stage tandem production/inventory system producing a single product. Processing time at each stage is assumed to have a general stationary processing time distribution. The cost of holding work-in-process (WIP) inventory is different at each stage. Therefore, decisions on when to release work to the system as well as when to transfer WIP from one stage to another need to be made. We formulate this problem of release/production control as a Markov decision process. However, the optimal policy is rather complex, making its implementation impracticable in practice. We therefore investigate the performance of simple base stock policies. Our approach aggregates several stages into one and uses a simple approximation to compute 'approximately optimal' base stock levels. We present the results of a simulation study that tests the performance of our approximation in estimating the best base stock levels, and the performance of base stock policies as compared with the optimal policy.