Simulation optimization tools have the potential to provide an unprecedented level of support for the design and execution of operational control in Discrete Event Logistics Systems (DELS). While much of the simulation optimization literature has focused on developing and exploiting integration and syntactical interoperability between simulation and optimization tools, maximizing the effectiveness of these tools to support the design and execution of control behavior requires an even greater degree of interoperability than the current state of the art. In this paper, we propose a modeling methodology for operational control decision-making that can improve the interoperability between these two analysis methods and their associated tools in the context of DELS control. This methodology establishes a standard definition of operational control for both simulation and optimization methods and defines a mapping between decision variables (optimization) and execution mechanisms (simulation / base system). The goal is a standard for creating conforming simulation and optimization tools that are capable of meeting the functional needs of operational control decision making in DELS.
Abstract Contemporary simulation technology can produce accurate assessments of integrated circuit factory (fab) production performance, including the contribution by the automated material handling systems (AMHS). However, the corresponding simulation models are both expensive and time-consuming to construct, and require long execution times to produce statistically valid estimates. These attributes render simulation ineffective as a decision support tool in the early phase of system design, where requirements and configurations are likely to change often. In this paper, we describe an analytical approach to AMHS performance modelling for a simple closed loop AMHS, such as is typical in supporting a 300 mm wafer fab bay. In this system, due to the significant impact of vehicle blocking, a straightforward queueing network model which treats the material handling system as a central server can be very inaccurate. We propose an alternative model that estimates the MHS performance considering the possibility of vehicle-blocking. While the resulting large-scale model presents some computational challenges, it promises reasonably accurate estimates with computation times that are acceptable in a design environment. Keywords: Semiconductor manufacturingAMHSMarkov chainVehicle blocking Acknowledgments We gratefully recognize the generous support of the Keck Virtual Factory Lab by the W. M. Keck Foundation. The suggestions of the anonymous reviewers and discussions with Professor Robert Foley have significantly improved the paper. Notes † The International Technology Roadmap for Semiconductors (ITRS) periodically lays out a technology plan to guide the semiconductor industry in the coming decades. The latest study is summarized in the ITRS 2003 annual report. It provides the current estimates for research and development that is required over the next decade to meet the historical numbers in performance growth, size reductions, cost, etc.
For most who do it, completing the PhD is the hardest thing they've ever done. There is a tendency to think that life will only get easier afterwards. The truth is that while life may get better, it doesn't necessarily get easier. It is possible, however, to ease the transition, if you pay attention to some basic truths.
This article reports on our ongoing experiences in developing visual analytics tools for real-world CESs. Our work focuses on the early design phase during which a large design space is explored, poor alternatives are pruned, and valuable alternatives are considered further. Visual analytics tools can provide interactive discovery, exploration, and understanding of real-world complex engineered systems (CES). The proposed tool, which focuses on the early design phase, can help users perform routine CES design analysis tasks and offer stakeholder-specific visual representations of complex design models.
Consider the production of an evolving family of similar products, each having a well-defined life cycle. The fundamental production resources are inherently flexible, i.e., reconfigurable and reprogrammable. Two distinct strategies can be followed in configuring production facilities: (1) focused facilities, where a facility is dedicated to one product at a time, but may be reassigned; and (2) nonfocused facilities, where setup operations permit a variety of products to be produced during a given planning period. When focused facilities are used, which is a common strategy in some electronics companies, products must be assigned to specific facilities. If facilities are not identical, and capacity is limited, then changing production requirements may force reassignment of products from one facility to another. Thus, the product assignment/reassignment decision may have a significant impact on the production capacity required. This paper concentrates on the product assignment/reassignment decision when a pure focused facility strategy is used. This problem is analyzed and a number of insights are developed. Based on this analysis, the problem is reformulated and an optimal solution procedure based on a multi-commodity network flow model is presented and tested for the product assignment/reassignment decision.
Aviation spare parts provisioning is a highly complex problem. Traditionally, provisioning has been carried out using a conventional Poisson-based approach where inventory quantities are calculated separately for each part number and demands from different operations bases are consolidated into one single location. In an environment with multiple operations bases, however, such simplifications can lead to situations in which spares -- although available at another airport -- first have to be shipped to the location where the demand actually arose, leading to flight delays and cancellations. In this paper we demonstrate how simulation-based optimisation can help with the multi-location inventory problem by quantifying synergy potential between locations and how total service lifecycle cost can be further reduced without increasing risk right away from the Initial Provisioning (IP) stage onwards by taking into account advanced logistics policies such as pro-active re-balancing of spares between stocking locations.
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