WSC 2006 Abstracts

Simulation-Based Scheduling Track

Monday 10:30:00 AM 12:00:00 PM
Simulation-Based Scheduling I

Chair: Jennifer Bekki (Arizona State University)

Sequence Step Algorithm for Continuous Resource Utilization in Probabilistic Repetitive Projects
Photios G. Ioannou and Chachrist Srisuwanrat (University of Michigan)

The sequence step algorithm addresses for the first time the problem of scheduling repetitive projects with probabilistic activity durations while maintaining continuous resource utilization. This algorithm is based on generalized concepts that can be implemented in most general-purpose simulation systems. The algorithm is presented in detail and is applied to an example project with 7 activities and 4 repetitive projects using a simulation model developed in Stroboscope, an activity-based simulation system. Numerical and graphical results help explain the algorithm and provide insight into the underlying tradeoff problem between reducing the expected crew idle time and increasing the expected project duration.

Simulation-Based Scheduling System in a Semiconductor Backend Facility
Sven Horn and Gerald Weigert (Dresden University of Technology), Sebastian Werner (Infineon Technologies) and Thomas Jähnig (Qimonda)

The semiconductor manufacturing process is usually divided in two parts: frontend and backend. In contrast to the frontend, where the manufacturing process is dominated by cluster-tools and cyclic routes, the backend has a predominant linear structure. In contrast to the frontend flow which is mostly controlled by dispatch rules, the backend process is suitable for real scheduling. A scheduling system for the backend of Infineon Technologies Dresden based on a Discrete Event Simulation (DES) system was developed and tested in the real industrial environment. The simulation model is automatically generated from the databases of the manufacturer. The system is used for short term scheduling - from one shift up to one week. The paper will focus on the aspect of optimizing the process flow and calculating exact release dates for lots. The basic principles are applicable not only in the semiconductor industry but also in other industrial sectors.

Pareto Control in Multi-Objective Dynamic Scheduling of a Stepper Machine in Semiconductor Wafer Fabrication
Amit Kumar Gupta (SIMTech) and Sivakumar Appa Iyer (Singapore-MIT Alliance, NTU)

This paper focuses on Pareto control in multi-objective dynamic scheduling of a stepper machine that is considered as a bottleneck machine in the semiconductor wafer fabrication process. We propose the use of compromise programming method for achieving Pareto control in the needs of conflicting objectives such as mean cycle time, cycle time variance and maximum tardiness. Using conjunctive simulated scheduling, at each decision instance in simulated time, a Pareto job is selected and loaded on the machine for processing. Using the real factory data, we demonstrate the concept of Pareto control in dynamic scheduling and show how a stepper machine can be controlled at specified needs of scheduling objectives. The results obtained from Pareto control approach are superior to the simulated results of actual operating heuristic in the factory.

Monday 1:30:00 PM 3:00:00 PM
Simulation-Based Scheduling II

Chair: Gerald Weigert (Technische University Dresden)

Simulation-Based Multi-Objective Optimization of a Real-World Scheduling Problem
Anna Persson, Henrik Grimm, Amos Ng, and Thomas Lezama (Centre for Intelligent Automation) and Jonas Ekberg, Stephan Falk, and Peter Stablum (Posten AB)

This paper presents a successful application of simulation-based multi-objective optimization of a complex real-world scheduling problem. Concepts of the implemented simulation-based optimization architecture are described, as well as how different components of the architecture are implemented. Multiple objectives are handled in the optimization process by considering the decision makers’ preferences using both prior and posterior articulations. The efficiency of the optimization process is enhanced by performing culling of solutions before using the simulation model, avoiding unpromising solutions to be unnecessarily processed by the computationally expensive simulation.

A Reinforcement Learning Algorithm to Minimize the Mean Tardiness of a Single Machine with Controlled Capacity
Hadeel D. Idrees, Mahdy O. Sinnokrot, and Sameh Tawfiq Al-Shihabi (University of Jordan)

In this work, we consider the problem of scheduling arriving jobs to a single machine where the objective is to minimize the mean tardiness. The scheduler has the option of reducing the processing time by half through the employment of an extra worker for an extra cost per job (setup cost). The scheduler can also choose from a number of dispatching rules. To find a good policy to be followed by the scheduler, we implemented a lambda-SMART algorithm to do an on-line optimization for the studied system. The found policy is only optimal with respect to the state representation and set of actions available, however, we believe that the developed policies are easy to implement and would result in considerable savings as shown by the numerical experiments conducted.

Stochastic Shipyard Simulation with SimYard
Oliver Dain, Matthew Ginsberg, Erin Keenan, John Pyle, Tristan Smith, and Andrew Stoneman (On Time Systems) and Iain Pardoe (University of Oregon)

SimYard is a stochastic shipyard simulation tool designed to evaluate the labor costs of executing different schedules in a shipyard production environment. SimYard simulates common production problems such as task delays and labor shortages. A simulated floor manager reacts to problems as they arise. Repeatedly simulating multiple schedules allows the user to compare the schedules on many different metrics, such as expected labor costs and the probability of missing the deadline. A SimYard simulation is driven by many inputs that describe the shipyard being simulated. Determining the correct values for these inputs can be framed as a multivariate calibration problem, which can be solved using inverse regression methods. Predictive sampling from the resulting model provides an appropriate adjustment for statistical uncertainty.

Monday 3:30:00 PM 5:00:00 PM
Simulation-Based Scheduling III

Chair: Gerald Mackulak (Arizona State University)

SIMUL8-Planner for Composites Manufacturing Center
Kim Hindle and Matt Duffin (Visual8 Corporation)

SIMUL8-PLANNER is a simulation-based planning and scheduling tool that intelligently sequences product flow across the plant. Combining order planning with production modeling, SIMUL8-PLANNER can generate production schedules that satisfy delivery objectives and capacity limits. This paper examines a case study where the SIMUL8-PLANNER tool was used to answer the complex scheduling problem of sequencing part requirements through a Composites Manufacturing Center. First a production model was used to capture the current workings of plant processes and product flow. Next, a sequencing system was added to the production model in order to provide a powerful, flexible, and adaptive scheduling system for all of the work cells and machines within the facility, complete with links to the company's ERP / shop-floor data systems. SIMUL8-Planner provides a practical approach and powerful platform for developing, testing, and refining production schedules before they are released to the shop-floor in a virtual and risk-free setting. It offers a flexible and open scheduling system that can be used for a wide range of production scheduling applications.

Simulation Assisted Optimization and Real-Time Control Aspects of Flexible Production Systems Subject to Disturbances
Kiran R Mahajan, Wilhelm Dangelmaier, Thomas Seeger, Benjamin Klöpper, and Mark Aufenanger (Heinz Nixdorf Institute, University of Paderborn)

Several types of production systems have been studied and researched in the past using either simulation and\or optimization methods. In this paper we describe the design and development of a simulation assisted predictive-reactive system for scheduling and rescheduling a typical flexible production system configuration. Aspects like the combined use of simulation and optimization to solve complex scheduling and rescheduling tasks are described in view of system stability and some of the broader production system elements like buffer sizing and material handling equipment. Results show that combining simulation and optimization for predictive scheduling resulted in better and valid performance measures for a typical example. Results show that some newly addressed aspects of stability and real-time control can be handled efficiently using a combination of simulation and optimization. Our discussions only bolster the claim that simulation is an indispensable tool in managing complex production systems.

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