We implement a gradient-based simulation optimization approach, the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, to estimate clearing functions (CFs) that describe the expected output of a production resource as a function of its expected workload from empirical data. Instead of trying to optimize the fit of the CF to the data, we seek values of the CF parameters that optimize the expected performance for the system when the fitted CFs are used to develop release schedules. A simulation model of a scaled-down wafer fabrication facility is used to generate the data and evaluate the performance of the CFs obtained from the SPSA. We show that SPSA significantly improves the production plan by either searching for better CF parameters or by directly optimizing releases.

Estimating Clearing Functions for Production Resources Using Simulation Optimization

An important goal of Production Planning and Control systems is to achieve short and predictable flow times, especially where high flexibility in meeting customer demand is required, while maintain...

Comparison between rule- and optimization-based workload control concepts: a simulation optimization approach

We consider the problem of representing the capabilities of production systems to convert inputs into outputs over time in the face of deterministic demand. The fact that planning models generally operate in discrete-time while the factory being modeled operates in continuous time suggests the use of multimodel methods combining an optimization model that plans the releases into the plant over time with a simulation model that assesses the impacts of these releases on performance. We discuss several such schemes that have been implemented using discrete-event simulation, present recent computational results, and discuss their strengths and weaknesses. We then consider two alternative approaches that have emerged in the recent literature: the use of discrete-time linear programming models based on nonlinear clearing functions, and methods using systems of coupled partial differential equations based on transport phenomena. We identify the relationships between queueing models, clearing functions, and transport model-based methods, and we discuss future research directions.

Continuous Dynamic Models, Clearing Functions, and Discrete-Event Simulation in Aggregate Production Planning

Empirical validation of meta-models of work centres in order release planning

We examine the problem of estimating clearing functions (CFs) that describe the expected output of a production resource as a function of its expected workload from empirical data. We use a simulation model of a scaled-down wafer fabrication facility to generate the data and evaluate the performance of the resulting CFs. We compare the performance of production plans generated using load based and product based CFs and find that using load based CFs yields better performance.

A comparison of multiple linear regression approaches for fitting clearing functions to empirical data

/pdf/estimating-clearing-functions-from-simulation-data-5fddqm6nkx.pdf

Estimating clearing functions from simulation data

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Estimating clearing functions from simulation data