On the conditional acceptance of iterates in SAO algorithms based on convex separable approximations

We reflect on the convergence and termination of optimization algorithms based on convex and separable approximations using two recently proposed strategies, namely a trust region with filtered acceptance of the iterates, and conservatism. We then propose a new strategy for convergence and termination, denoted filtered conservatism, in which the acceptance or rejection of an iterate is determined using the nonlinear acceptance filter. However, if an iterate is rejected, we increase the conservatism of every unconservative approximation, rather than reducing the trust region. Filtered conservatism aims to combine the salient features of trust region strategies with nonlinear acceptance filters on the one hand, and conservatism on the other. In filtered conservatism, the nonlinear acceptance filter is used to decide if an iterate is accepted or rejected. This allows for the acceptance of infeasible iterates, which would not be accepted in a method based on conservatism. If however an iterate is rejected, the trust region need not be decreased; it may be kept constant. Convergence is than effected by increasing the conservatism of only the unconservative approximations in the (large, constant) trust region, until the iterate becomes acceptable to the filter. Numerical results corroborate the accuracy and robustness of the method

An augmented Lagrangian coordination method for distributed optimal design in MDO: Part I formulation and algorithms

Quite a number of coordination methods have been proposed for the distributed optimal design of large-scale 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 shared variables, not through constraints and/or objectives). In this paper we present a new coordination method for MDO problems with coupling variables as well as coupling objectives and constraints. Our approach employs an augmented Lagrangian penalty relaxation in combination with a block coordinate descent method. The coordination method can be shown to converge to KKT points of the original problem by using existing convergence results. 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. In a sequel paper we demonstrate the flexibility of the formulations, and investigate the numerical behavior of the proposed method

On sustainable operation of warehouse order picking systems

Sustainable development calls for an efficient utilization of natural and human resources. This issue also arises for warehouse systems, where typically extensive capital investment and labor intensive work are involved. It is therefore important to assess and continuously monitor the performance of such a system to identify possible improvements in the system configuration. We believe that a modular system architecture and an accompanying performance monitoring method serve this purpose. In this paper we advocate a system architecture with a decentralized hierarchical control structure. The architecture allows easy adjustment of system configurations and control heuristics to deal with ever-changing warehouse requirements. We also propose a performance monitoring method that only requires little shop-floor data. This method is based on the concept of effective process time and it can be used to generate key performance indicators of the warehouse. By applying the system architecture and the performance monitoring method, we believe that more efficient ways of utilizing resources and capitals can be identified to improve the sustainability of warehouse order picking systems

Effective process times for multi-server flowlines with finite buffers

An effective process time (EPT) approach is proposed for aggregate model building of multi-server tandem queues with finite buffers. Effective process time distributions of the workstations in the flow line are measured without identifying the contributing factors. A sample path equation is used to compute the EPT realizations from arrival and departure events of lots at the respective workstations. If the amount of blocking in the line is high, the goodness of the EPT distribution fits determines the accuracy of the EPT-based aggregate model. Otherwise, an aggregate model based on just the first two moments of the EPT distributions is sufficient to obtain accurate predictions. The approach is illustrated in an industrial case study using both simulation and analytical queueing approximations as aggregate models

Quantifying the impact of product changes on manufacturing performance

Every adjustment to a physical product disrupts the manufacturing organization, requiring adaptation in tools and processes. The resulting disruption to manufacturing performance is poorly understood. We use design structure matrices and a complexity metric to quantify the complexity and change of product architecture in an explorative, small scale experiment. Based on the results we develop two propositions to guide further research into the factors that affect the shape of consecutive learning curves upon product changes. The first proposition is that after product change, the complexity of the novel part of product architecture is responsible for the initial decrease in manufacturing performance. Second, we propose that the asymptote of a learning curve and the complexity of a product’s architecture are inversely related.Every adjustment to a physical product disrupts the manufacturing organization, requiring adaptation in tools and processes. The resulting disruption to manufacturing performance is poorly understood. We use design structure matrices and a complexity metric to quantify the complexity and change of product architecture in an explorative, small-scale experiment. Based on the results we develop two propositions to guide further research into the factors that affect the shape of consecutive learning curves upon product changes. The first proposition is that after product change, the complexity of the novel part of product architecture is responsible for the initial decrease in manufacturing performance. Second, we propose that the asymptote of a learning curve and the complexity of a product’s architecture are inversely related.</p

An augmented Lagrangian coordination method for distributed optimal design in MDO: Part II examples

The formulation flexibility and the numerical performance of the augmented Lagrangian coordination method proposed in the part I paper is demonstrated on several example problems. Results for a number of test problems indicate that the coordination method is effective and robust in finding solutions of the original non-decomposed problem, and does not introduce new local minima for non-convex problems. The required coordination costs are found to be determined by how the problem is partitioned and coordinated. These costs do not only depend on the number of quantities that have to be coordinated, but also on their coupling strengths. The formulation flexibility of the new method provides means to minimize these costs by adapting the problem at hand

Use of design sensitivity information in response surface and kriging metamodels

Metamodels based on responses from designed (numerical) experiments may form efficient approximations to functions in structural analysis. They can improve the efficiency of Engineering Optimization substantially by uncoupling computationally expensive analysis models and (iterative) optimization procedures. In this paper we focus on two strategies for building metamodels, namely Response Surface Methods (RSM) and kriging. We discuss key-concepts for both approaches, present strategies for model training and indicate ways to enhance these metamodeling approaches by including design sensitivity data. The latter may be advantageous in situations where information on design sensitivities is readily available, as is the case with e.g. Finite Element Models. Furthermore, we illustrate the use of RSM and kriging in a numerical model study and conclude with some remarks on their practical value