Download e-book for kindle: Production Planning and Control for Semiconductor Wafer by Lars Mönch, John W. Fowler, Scott J. Mason

By Lars Mönch, John W. Fowler, Scott J. Mason

Over the past fifty-plus years, the elevated complexity and pace of built-in circuits have considerably replaced our global. this present day, semiconductor production is likely to be an important phase of the worldwide production zone. because the semiconductor has turn into extra aggressive, enhancing making plans and keep watch over has turn into a key issue for company good fortune. This booklet is dedicated to creation making plans and keep watch over difficulties in semiconductor wafer fabrication amenities. it's the first publication that takes a accomplished examine the position of modeling, research, and comparable details structures for such production structures. The booklet offers an operations study- and machine science-based advent into this crucial box of semiconductor manufacturing-related study.

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Additional info for Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems

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A is an IRm×n matrix. 9) where the second-stage objective function, also called the recourse function, is defined as follows: ˜ ξ (ω )) := min {q(ω )y|y ∈ Y } . 10) ˜ ξ (ω )) is the objective function value of a second linear program with the Q(x, set of feasible solutions p Y := y ∈ IR+ |Wy = h(ω ) − T (ω )x . 11) The quantity ω is a realization of a random variable on a probability space Ω . 10), h(ω ) ∈ IRs is a column vector that is the part of the right-hand side of the s constraints that do not depend on the first-stage decision variable x, and finally T (ω ) ∈ IRs×n is the so-called technology matrix.

Therefore, we mainly focus on efficient heuristics. However, it is generally useful to know optimal decision methods, because we can exploit them to assess the performance of heuristics for small-size problem instances. On the other hand, often very efficient heuristics can be derived from combining optimal decision methods with heuristics. In the remainder of this section, we discuss several optimal decision methods and heuristic algorithms that will later be used to tackle production planning and control problems in semiconductor manufacturing.

7 and 4, respectively. Except for the description of stochastic programming methods in Sect. 4, so far only decision methods for deterministic problems are studied. , queueing theory and discreteevent simulation. 7 Queueing Theory The first method related to decision-making for stochastic problems is queueing theory. A simple queueing system can be characterized as an input–output system consisting of a waiting line, also called a queue, and a single server. However, there are also queueing systems that are formed by service centers and interconnecting queues.

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