A Metamodel-Based Monte Carlo Simulation Approach for Responsive Production Planning of Manufacturing Systems

Production planning is concerned with finding a release plan of jobs into the manufacturing system so that its actual outputs over time match the customer demand with the least cost. The biggest challenge of production planning lies in the difficulty to quantify the performance of a release plan, which is the necessary basis for plan optimization. Triggered by an input plan over a time horizon, the system outputs, work in process (WIP) and job departures, are non-stationary bivariate time series that interact with customer demand (another time series), resulting in the fulfillment/non-fulfillment of demand and in the holding cost of both WIP and finished-goods inventory. The relationship between a release plan and its resulting performance metrics (typically, mean/variance of the total cost and the demand fulfill rate is far from being adequately quantified in the existing literature of production planning. In this dissertation, a metamodel-based Monte Carlo simulation (MCS) method is developed to accurately capture the dynamic and stochastic behavior of a manufacturing system, and to allow for real-time evaluation of a release plan in terms of its performance metrics. This evaluation capability is embedded in a multi-objective optimization framework to enable the quick search of good (or optimum) release plans. The developed method has been applied to a scaled-down semiconductor fabrication system to demonstrate the quality of the metamodel-based MCS evaluation and the plan optimization results

Serial production line performance under random variation:Dealing with the ‘Law of Variability’

Many Queueing Theory and Production Management studies have investigated specific effects of variability on the performance of serial lines since variability has a significant impact on performance. To date, there has been no single summary source of the most relevant research results concerned with variability, particularly as they relate to the need to better understand the ‘Law of Variability’. This paper fills this gap and provides readers the foundational knowledge needed to develop intuition and insights on the complexities of stochastic simple serial lines, and serves as a guide to better understand and manage the effects of variability and design factors related to improving serial production line performance, i.e. throughput, inter-departure time and flow time, under random variation

Analysis and Comparison of Fixed-Size Lot and Fixed-Time Lot Batch Production Systems

In high volume automated discrete item batch production systems, the batches or lots are typically fixed quantity (i.e., size), with setups incurred between the different production lots. Processing times for the fixed-size lots are relatively constant when the workstation is operating however, random workstation disruptions cause variability in the lot completion time making the operations of interrelated activities such as material handling and setup crews less efficient. In this dissertation, the long-run average cost performance of fixed-size lot batch production systems is compared to batch production systems where the lot size is defined as a fixed time. In the “fixed-time” lot batch production system, there is ideally no variability in the time length to produce a lot, but the production output in this fixed time length may vary. In this comparison, the batch production systems considered are workstations operating under a continuous review (Q, r) inventory system. The comparison was conducted assuming unmet demands are lost (lost-sales policy), and also when unmet demand can be backordered (backordering policy). One objective of this research was to identify the factors having the largest effect on the long-run average cost differences between fixed-size lot and fixed-time lot systems. Because of the system complexity due to the inclusion of multiple real-world factors, a designed experiment is employed to compare the fixed-sized and fixed-time lot systems using discrete event simulation. For every treatment combination tested the batch sizes and reorder point levels (quantities or time) were optimized, so that differences between systems cannot be attributed to poor batch size and re-order point selection. The experimental results show that for the lost sales policy the factors: interarrival time between demands, and the coefficient of variation of the demand probability distribution have the largest impact on the long-run average cost difference between a fixed-size lot and fixed-time lot batch production systems. For the backordering policy the factors: workstation stand-alone availability, failure and repair frequency, and capacity utilization have the largest impacts Another research objective was to identify functional relationships between the input factors and the output. A feedforward backpropagation neural network with the connection weight approach was applied to the experimental results database to search for relationships between various input factors and the categorical outcomes 1) a fixed-size lot production system has significantly lower cost performance than a fixed-time lot system, 2) a fixed-time lot production system has significantly lower cost performance than a fixed-size lot system, and 3) the cost performance of two systems is not significantly different. The results show that for the lost sales policy the factors: demand coefficient of variation, and stand-alone availability, have the largest relative importance in predicting the outcomes. For the backordering policy the factors: demand coefficient of variation, stand-alone availability, and inventory holding cost have the largest relative importance in predicting the outcomes. In general, at higher stand-alone availability levels and lower demand coefficient of variation the production time to produce a fixed-size lot is low enough that the system can operate in a “just-in-time” manner and a fixed-size lot production system will result in lower costs than a fixed-time lot system. However, as the stand-alone availability reduces and demand coefficient of variation increases, the fixed-time lot system results in significantly lower costs than the fixed-size lot system. The insights developed from this research can be utilized by the decision makers to select which batch production system should be utilized such that the long-run average cost can be minimized

Simulation and Optimization of Production Control for Lean Manufacturing Transition

Lean manufacturing is an operations management philosophy that advocates eliminating waste, including work-in-process (WIP) inventory. A common mechanism for controlling WIP is "pull" production control, which limits the amount of WIP at each stage. The process of transforming a system from push production control to pull is not well understood or studied. This dissertation explores the events of a production control transition, quantifies its costs and develops techniques to minimize them. Simulation models of systems undergoing transition from push to pull are used to study this transient behavior. The transition of a single stage system is modeled. An objective function is introduced that defines transition cost in terms of the holding cost of orders in backlog and material in inventory. It incorporates two techniques for mitigating cost: temporarily deferring orders and adding extra capacity. It is shown that, except when backlog costs are high, it is better to transform the system quickly. It is also demonstrated that simulation based optimization is a viable tool to find the optimal transition strategy. Transition of a two-stage system is also modeled. The performance of two simple multi-stage transition strategies is measured. In the first, all of the stages are transformed at the same time. In the second, they are transformed one at a time. It is shown that the latter strategy is superior. Other strategies are also discussed. A new modeling formalism, the Production Control Framework (PCF), is introduced to facilitate automated searches for transition strategies in more complex systems. It is a hierarchical description of a manufacturing system built on a novel extension of the classic queue server model, which can express production control policy parametrically. The PCF is implemented in the form of a software template and its utility is shown as it is used to model and then find the optimal production control policy for a five stage system. This work provides the first practical guidance and insight into the behavior and cost of Lean production control transition, and it lays the groundwork for the development of optimal transition strategies for even the most complex manufacturing systems

[Activity of Institute for Computer Applications in Science and Engineering]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science

Complex materials handling and assembly systems.

Report covers June 1, 1976-July 31, 1978.Each v. has also a distinctive title.National Science Foundation. Grant NSF/RANN APR76-12036 National Science Foundation. Grant DAR78-1782

Analysis of buffer allocations in time-dependent and stochastic flow lines

This thesis reviews and classifies the literature on the Buffer Allocation Problem under steady-state conditions and on performance evaluation approaches for queueing systems with time-dependent parameters. Subsequently, new performance evaluation approaches are developed. Finally, a local search algorithm for the derivation of time-dependent buffer allocations is proposed. The algorithm is based on numerically observed monotonicity properties of the system performance in the time-dependent buffer allocations. Numerical examples illustrate that time-dependent buffer allocations represent an adequate way of minimizing the average WIP in the flow line while achieving a desired service level

Color postprocessing for 3-dimensional finite element mesh quality evaluation and evolving graphical workstation

Three general tasks on general-purpose, interactive color graphics postprocessing for three-dimensional computational mechanics were accomplished. First, the existing program (POSTPRO3D) is ported to a high-resolution device. In the course of this transfer, numerous enhancements are implemented in the program. The performance of the hardware was evaluated from the point of view of engineering postprocessing, and the characteristics of future hardware were discussed. Second, interactive graphical tools implemented to facilitate qualitative mesh evaluation from a single analysis. The literature was surveyed and a bibliography compiled. Qualitative mesh sensors were examined, and the use of two-dimensional plots of unaveraged responses on the surface of three-dimensional continua was emphasized in an interactive color raster graphics environment. Finally, a postprocessing environment was designed for state-of-the-art workstation technology. Modularity, personalization of the environment, integration of the engineering design processes, and the development and use of high-level graphics tools are some of the features of the intended environment