Analytical target cascading is a method for design optimization of hierarchical, multilevel systems. A quadratic penalty relaxation of the system consistency constraints is used to ensure subproblem feasibility. A typical nested solution strategy consists of inner and outer loops. In the inner loop, the coupled subproblems are solved iteratively with fixed penalty weights. After convergence of the inner loop, the outer loop updates the penalty weights. The article presents an augmented Lagrangian relaxation that reduces the computational cost associated with ill-conditioning of subproblems in the inner loop. The alternating direction method of multipliers is used to update penalty parameters after a single inner loop iteration, so that subproblems need to be solved only once. Experiments with four examples show that computational costs are decreased by orders of magnitude ranging between 10 and 1000.

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SUMMARYQuite a number of coordination methods have been proposed for the distributed optimal design of largescale systems consisting of a number of interacting subsystems. Several coordination methods are known to have numerical convergence difficulties that can be explained theoretically. The methods for which convergence proofs are available have mostly been developed for so-called quasi-separable problems (i.e. problems with individual subsystems coupled only through a set of linking variables, not through constraints and/or objectives). In this paper, we present a new coordination approach for multidisciplinary design optimization problems with linking variables as well as coupling objectives and constraints. Two formulation variants are presented, offering a large degree of freedom in tailoring the coordination algorithm to the design problem at hand. The first, centralized variant introduces a master problem to coordinate coupling of the subsystems. The second, distributed variant coordinates coupling directly between subsystems. Our coordination approach employs an augmented Lagrangian penalty relaxation in combination with a block coordinate descent method. The proposed coordination algorithms can be shown to converge to Karush-Kuhn-Tucker points of the original problem by using the existing convergence results. We illustrate the flexibility of the proposed approach by showing that the analytical target cascading method of Kim et al. (J. Mech. Design-ASME 2003; 125(3):475-480) and the augmented Lagrangian method for quasi-separable problems of Tosserams et al. (Struct. Multidisciplinary Opt. 2007, to appear) are subclasses of the proposed formulations.

Several decomposition methods have been proposed for the distributed optimal design of quasi-separable problems encountered in Multidisciplinary Design Optimization (MDO). Some of these methods are known to have numerical convergence difficulties that can be explained theoretically. We propose a new decomposition algorithm for quasi-separable MDO problems. In particular, we propose a decomposed problem formulation based on the augmented Lagrangian penalty function and the block coordinate descent algorithm. The proposed solution algorithm consists of inner and outer loops. In the outer loop, the augmented Lagrangian penalty parameters are updated. In the inner loop, our method alternates between solving an optimization master problem, and solving disciplinary optimization subproblems. The coordinating master problem can be solved analytically; the disciplinary subproblems can be solved using commonly available gradient-based optimization algorithms. The augmented Lagrangian decomposition method is derived such that existing proofs can be used to show convergence of the decomposition algorithm to KKT points of the original problem under mild assumptions. We investigate the numerical performance of the proposed method on two example problems.

Operational time variability is one of the key parameters determining the average cycle time of lots. Many different sources of variability can be identified such as machine breakdowns, setup, and operator availability. However, an appropriate measure to quantify variability is missing. Measures such as overall equipment effectiveness (OEE) used in the semiconductor industry are entirely based on mean value analysis and do not include variances. The main contribution of this paper is the development of a new algorithm that enables estimation of the mean effective process time and the coefficient of variation 2 of a multiple machine workstation from real fab data. The algorithm formalizes the effective process time definitions as known in the literature. The algorithm quantifies the claims of machine capacity by lots, which include time losses due to down time, setup time, and other irregularities. The estimated and 2 values can be interpreted in accordance with the well-known queueing relations. Some test examples as well as an elaborate case from the semiconductor industry show the potential of the new effective process time algorithm for cycle time reduction programs. Index Terms-Capacity and cycle time losses, data extraction, equipment modeling, factory dynamics, manufacturing line performance. I. INTRODUCTION E QUIPMENT in semiconductor manufacturing is subject to many sources of variability. An important source is machine down time, which occurs due to highly complex and technologically advanced semiconductor manufacturing processes [1]. Many other corrupting operational influences are also present, such as batching, hot lots, rework, setup, and operator availability. All together, they introduce a substantial amount of variability in the interarrival and operational times of the lots during their flow through the fab. Queue times are mainly influenced by variability and utilization. High utilization is necessary in the semiconductor industry in order to maximize productivity and minimize costs. In combination with large variability, high utilization leads to large cycle