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The method of robust design has long been used for the design of systems that are insensitive to noises. In this paper it is demonstrated how this approach can be used to obtain a robust eco-design (ecological design). In a case study, robust design principles are applied to the design of a factory smokestack, using the Gaussian Plume Model (GPM). The GPM is a well-known model for describing pollutant dispersal from a point source, subject to various atmospheric conditions. In this research, the mean-square-error (MSE) of the accumulated and the maximum pollution values around a given target are defined as the performance measures and used to adjust the design parameters. Both analytical and numerical approaches are used to evaluate the MSE measures over the design space. It is demonstrated how to use the non-linearity in the GPM to reach a low MSE value that produces a cheaper design configuration. The differences between the manufacturer viewpoint and the environmentalist viewpoint with respect to the considered eco-design problem are discussed and analyzed. Keywords: Ecological design, green manufacturing, air pollution, clean production, 1. Introduction: Eco-design and quality engineering The concept of eco-design, often referred to as cleaner production or design for the environment, describes a design process which takes into consideration the [...]

We suggest an experimentation strategy for the robust design of empirically fitted models. The suggested approach is used to design experiments that minimize the variance of the optimal robust solution. The new design-of-experiment optimality criterion, termed [V.sub.s]-optimal, prioritizes the estimation of a model's coefficients, such that the variance of the optimal solution is minimized by the performed experiments. It is discussed how the proposed criterion is related to known optimality criteria. We present an analytical formulation of the suggested approach for linear models and a numerical procedure for higher-order or nonpolynomial models. In comparison with conventional robust-design methods, our approach provides more information on the robust solution by numerically generating its multidimensional distribution. Moreover, in a case study, the proposed approach results in a better robust solution in comparison with these standard methods., 1. Introduction In the last three decades, the Taguchi method of robust design has been widely applied to the design of various systems. In many cases, the exact underlying relationship [...]

In certain types of processes, verification of the quality of the output units is possible only after the entire batch has been processed. We develop a model that prescribes which units should be inspected and how the units that were not inspected should be disposed of, in order to minimize the expected sum of inspection costs and disposition error costs, for processes that are subject to random failure and recovery. The model is based on a dynamic programming algorithm that has a low computational complexity. The study also includes a sensitivity analysis under a variety of cost and probability scenarios, supplemented by an analysis of the smallest batch that requires inspection, the expected number of inspections, and the performance of an easy to implement heuristic., 1. Introduction Most researchers and practitioners in the field of production management agree that process improvement contributes more to product quality than the inspection of process output. However, quality inspection, [...]

In this paper we follow previous 'pseudo-stochastic' approaches that solve stochastic control problems by using deterministic optimal control methods. In a similar manner to the certainty equivalence principle, the suggested model maximizes a given profit function of the expected system outcome. However, unlike the certainty equivalence principle, we model the expected influences of all future events (including those that are expected beyond the planning horizon), as encapsulated by their density functions and not only by their mean values. The model is applied to the optimal scheduling of multiple part-types on a single machine that is subject to random failures and repairs. The objective of the scheduler is to maximize the profit function of the produced multiple-part mix. A numerical study is performed to evaluate the suggested pseudo-stochastic solutions under various conditions. These solutions are compared to a profit upper bound of the stochastic optimal control solutions., 1. Introduction A major difficulty that has to be resolved when modeling a stochastic dynamic system is how to treat information regarding future events. Several modeling approaches have been suggested [...]

Conventional Statistical Process Control (SPC) schemes fail to monitor nonlinear and finite-state processes that often result from feedback-controlled processes. SPC methods that are designed to monitor autocorrelated processes usually assume a known model (often an ARIMA) that might poorly describe the real process. In this paper, we present a novel SPC methodology based on context modeling of finite-state processes. The method utilizes a series of context-tree models to estimate the conditional distribution of the process output given the context of previous observations. The Kullback-Leibler divergence statistic is derived to indicate significant changes in the trees along the process. The method is implemented in a simulated flexible manufacturing system in order to detect significant changes in its production mix ratio output., 1. Introduction and literature review In many industrial environments process outputs are often adjusted by applying feedback mechanisms and policies. As an example, the temperature in a chemical reactor is [...]

This paper presents the performance of the Weight-Balanced Testing (WBT) algorithm with multiple testers. The WBT algorithm aims to minimize the expected number of (round of) tests and has been proposed for coding, memory storage, search and testing applications. It often provides reasonable results if used with a single tester. Yet, the performance of the WBT algorithm with multiple testers and particularly its upper bound have not been previously analyzed, despite the large body of literature that exists on the WBT algorithm, and the recent papers that suggest its use in various testing applications. Here we demonstrate that WBT algorithm with multiple testers is far from being the optimal search procedure. The main result of this paper is the generalization of the upper bound on the expected number of tests previously obtained for a single-tester WBT algorithm. For this purpose, we first draw an analogy between the WBT algorithm and alphabetic codes; both being represented by the same Q-ary search tree. The upper bound is then obtained on the expected path length of a Q-ary tree, which is constructed by the WBT algorithm. Applications to the field of testing and some numerical examples are presented for illustrative purposes., 1. Introduction In this paper we analyze the Weight-Balanced Testing (WBT) algorithm with multiple testers. We start by exemplifying the sequential search problem as presented in Ahlswede and Wegner (1987) [...]

Most statistical process control (SPC) methods are not suitable for monitoring nonlinear and state-dependent processes. This article introduces the context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory [...]

The paper considers a sequential Design Of Experiments (DOE) scheme. Our objective is to maximize both information and economic measures over a feasible set of experiments. Optimal DOE strategies are developed by introducing information criteria based on measures adopted from information theory. The evolution of acquired information along various stages of experimentation is analyzed for linear models with a Gaussian noise term. We show that for particular cases, although the amount of information is unbounded, the desired rate of acquiring information decreases with the number of experiments. This observation implies that at a certain point in time it is no longer efficient to continue experimenting. Accordingly, we investigate methods of stochastic dynamic programming under imperfect state information as appropriate means to obtain optimal experimentation policies. We propose cost-to-go functions that model the trade-off between the cost of additional experiments and the benefit of incremental information. We formulate a general stochastic dynamic programming framework for design of experiments and illustrate it by analytic and numerical implementation examples.

In this paper we present a novel treatment of the inspection-system design problem when inspection is unreliable and subject to classification errors. Our approach, based on the theory of Error-Correcting Codes (ECC), leads to the development of a Self-Correcting Inspection (SCI) decision rule that does not require complete knowledge of inspection error probabilities. We show that the proposed rule assures correct classification, if the number of inspection errors is less than a certain number. We analyze the performance of the SCI decision rule under different inspection situations, including some situations that are uncommon in the field of error-correcting codes. Then, we show how the underlying mathematical structure can be applied to determine the number of inspections and the level of inspection reliability in order to minimize the sum of inspection-related costs. The practical contribution of this work lies in that it expands the ability of the designer of inspection systems to deal with cases where th ere is very little or no information regarding the reliability of the inspection operations., IRAD BEN-GAL (1)(*) YALE T. HERER (2) TZVI RAZ (3) 1. Introduction and literature review Inspections are performed in virtually every production system. Their purpose is to verify that the [...]

The increasing use of computerized tools for virtual manufacturing in workstation design has two main advantages over traditional methods; first, it enables the designer to examine a large number of design solutions; and second, simulation of the work task may be performed in order to obtain the values of various performance measures. In this paper a new structural methodology for the workstation design is presented. Factorial experiments and the response surface methodology are integrated in order to reduce the number of examined design solutions and obtain an estimate for the best design configuration with respect to multi-objective requirements. Biographies Irad Ben-Gal is an Assistant Professor at the Department of Industrial Engineering in Tel Aviv University. He holds a B.Sc. (1992) degree from Tel Aviv University, M,Sc. (1996) and Ph.D. (1998) degrees from Boston University. Irad is a member of thc Institute for Operations Research and Management Sciences (INFORMS) and is the head of the Computer Integrated Manufacturing (CIM) lab at Tel Aviv University. He has worked for several years for various industrial organizations, both as a consultant and as a project manager. His research interests include, quality control, design of experiments and testing procedures, and application of information theory to industrial problems. Joseph Bukchin is a member of the Department of Industrial Engineering at Tel Aviv University. He received his B.Sc., M.Sc. and D.Sc. degrees in Industrial Engineering from the Technion-Israel Institute of Technology. He is a member of the IIE and INFORMS. Bukchin has been a Visiting Professor at the Grado Department of Industrial and Systems Engineering at Virginia Tech. His main research interests are in the areas of assembly systems design, assembly line balancing, facility design, design of cellular manufacturing systems, operational scheduling as well as work station design with respect to cognitive and physical aspects of the human operator., IRAD BEN-GAL (*) 1. Introduction 1.1. Background The design and planning of manual workstations and the determination of proper work methods to be employed are challenging tasks. In order to [...]

In this paper, an information theoretic approach is applied to analyze the performance of a decentralized control system. The control system plays the role of a correcting device which decreases [...]