Adedeji Badiru

Computational survey of univariate and multivariate learning curve models

A computational survey of the various univariate and multivariate learning curve models that have evolved over the past several years is presented. Discussions are presented to show how the models might be used for cost analysis or productivity assessment in engineering management. A computational experiment comparing a univariate model to a bivariate model is presented. While the bivariate model provides only a slightly better fit than the univariate model, it does provide more detailed information about the factor interactions, and better utilization of available data. The results of the computational experiment can be generalized for the appropriateness of multivariate models. >

Neural network as a simulation metamodel in economic analysis of risky projects

An artificial neural network (ANN) model for economic analysis of risky projects is presented in this paper. Outputs of conventional simulation models are used as neural network training inputs. The neural network model is then used to predict the potential returns from an investment project having stochastic parameters. The nondeterministic aspects of the project include the initial investment, the magnitude of the rate of return, and the investment period. Backpropagation method is used in the neural network modeling. Sigmoid and hyperbolic tangent functions are used in the learning aspect of the system. Analysis of the outputs of the neural network model indicates that more predictive capability can be achieved by coupling conventional simulation with neural network approaches. The trained network was able to predict simulation output based on the input values with very good accuracy for conditions not in its training set. This allowed an analysis of the future performance of the investment project without having to run additional expensive and time-consuming simulation experiments.

Fuzzy modeling and analytic hierarchy processing to quantify risk levels associated with occupational injuries. I. The development of fuzzy-linguistic risk levels

This paper presents the Part I in a two-phase research project to develop a fuzzy-linguistic expert system for quantifying and predicting the risk of occupational injury, specifically, cumulative trauma disorders of the forearm and hand. This aspect of the research focuses on the development and representation of linguistic variables to qualify risk levels. These variables are then quantified using fuzzy-set theory, thus allowing the model to evaluate qualitative and quantitative data. These linguistic risk variables may be applied to other potentially hazardous environments. The three phases of the knowledge acquisition and variable development are covered, as well as the feasibility of the linguistic variables.

Handbook of Industrial and Systems Engineering

General Introductions Origins of Industrial and Systems Engineering Fundamentals of Industrial Engineering Human Factors Marc Resnick Human Factors and Ergonomics: How to Fit into the New Era Dongjoon Kong Process Control for Quality Improvement Wei Jiang and John V. Farr Project Scheduling Jonathan F. Bard Cost Concepts and Estimation Adedeji Badiru Engineering Economic Evaluation and Cost Estimation Olufemi A. Omitaomu Work Sampling Paul S. Ray Fundamentals of Systems Engineering An Overview of Industrial and Systems Engineering S. A. Oke System Safety Engineering Paul S. Ray Metaheuristics: A Solution Methodology for Optimization Problems Reinaldo J. Moraga, Gail W. DePuy and Gary E. Whitehouse Multidisciplinary Systems Teams Craig M. Harvey, Taren Daigle, Ashok Darisipudi, Ling Rothrock and Larry Nabatilan Strategic Performance Measurement Garry D. Coleman Fundamentals of Project Management Abedeji B. Badiru Modeling, Identification/Estimation in Stochastic Systems O. Ibidapo-Obe Manufacturing and Production Systems Manufacturing Technology Shivakumar Raman and Aashish Wadke Cross-Training in Production Systems with Human Learning and Forgetting David A. Nembhard and Bryan A. Norman Design Issues and Analysis of Experiments in Nanomanufacturing Harriet Black Nembhard, Navin Acharya, Mehmet Aktan and Seong Kim Lean Manufacturing Cells M. A. Badar Cluster Analysis: A Tool for Industrial Engineers Paul S. Ray and H. Aiyappan Information Engineering Teresa Wu and Jennifer Blackhurst Dependability of Computer and Network Systems Nong Ye New Technologies Optimization Problems in Applied Sciences: From Classical through Stochastic to Intelligent Metaheuristic Approaches Oye Ibidapo-Obe and Sunday Asaolu An Architecture for the Design of Industrial Information Systems Richard E. Billo, J. David Porter and Richard J. Puerzer Fuzzy Group Decision-Making David Ben-Arieh and Zhifeng Chen Introduction to Applications of Fuzzy Set Theory in Industrial Engineering Pamela R. McCauley-Bell and Lesia L. Crumpton-Young Maintenance Management in the 21st Century S.A. Oke Ranking Irregularities when Evaluating Alternatives by Using Some Multi-Criteria Decision Analysis Methods Xiaoting Wang and Evangelos Triantaphyllou e-Design Systems Bartholomew O. Nnaji, Yan Wang and Kyoung-Yun Kim General Applications Generating User Requirements in Project Management David Ben-Arieh and Zhifeng Chen Learning and Forgetting Models and their Applications Mohamad Y. Jaber Industrial Engineering Applications in the Construction Industry Lincoln H. Forbes Scheduling of Production and Service Systems Bobbie Leon Foote The Application of Industrial Engineering to Marketing Management Tzong-Ru Lee and Shing-Chi Chang A Management Model for Planning Change Based on the Integration of Lean and Six Sigma R. Sawhney and Ike C. Ehie Critical Resource Diagramming: A Tool for Resource Utilization Analysis Adedeji B. Badiru Integrating Six Sigma and Lean Manufacturing for Process Improvement: A Case Study Ike C. Ehie and Rupy Sawhney Appendix

Multivariate analysis of the effect of learning and forgetting on product quality

The impact of alternate periods of learning and forgetting on product quality in a manufacturing system is discussed in this paper. Factors that may precipitate forgetting are often recognized as interruptions in the production process. Interruptions can occur at scheduled times or randomly. The approach presented here addresses the mixed effect of learning and forgetting. The resultant output of the worker is compared with the expected output without interruption. A case model is used as an illustrative model for the proposed methodology. The approach extends the conventional univariate learning curve to a multivariate model and incorporates a forget model. Multivariate analysis facilitates the inclusion of other important factors in manufacturing productivity analysis. The inclusion of a forget model creates a realistic representation of the effects of learning and forgetting on worker output and product quality.

FLEXPERT: facility layout expert system using fuzzy linguistic relationship codes

Linguistic variables, fuzzy statements and fuzzy algorithms, from the theory of fuzzy sets, provide suitable tools to solve ill-defined problems. Unlike conventional techniques that deal only with discrete conditions, like ‘on’ and ‘off’, and precise numerical values, fuzzy logic offers an alternate technique that deals with non-discrete conditions, such as ‘absolutely important’, ‘quite important’ and ‘less important’. This paper presents a new approach, based on the theory of fuzzy logic, to solve the facility layout problem. The proposed approach considers the multicriteria nature of the layout problem and the fuzziness of the input data through the integration of an expert system and a fuzzy algorithm with a facility layout program. The system generates the best layout that satisfies die qualitative as well as the quantitative constraints on the layout problem. This facilitates the incorporation of the knowledge of facility layout experts. A knowledge-based system, named FLEXPERT, has been developed t...

A single-loop deterministic method for reliability-based design optimization

Reliability-based design optimization (RBDO) is a technique used for engineering design when uncertainty is being considered. A typical RBDO problem can be formulated as a stochastic optimization model where the performance of a system is optimized and the reliability requirements are treated as constraints. One major challenge of RBDO research has been the prohibitive computational expenses. In this research, a new approximation approach, termed the single-loop deterministic method for RBDO (SLDM_RBDO), is proposed to reduce the computational effort of RBDO without sacrificing much accuracy. Based on the first order reliability method, the SLDM_RBDO method converts the probabilistic constraints to approximate deterministic constraints so that the RBDO problems can be transformed to deterministic optimization problems in one step. Three comparison experiments are conducted to show the performance of the SLDM_RBDO. In addition, a reliable forearm crutch design is studied to demonstrate the applicability of SLDM_RBDO to a real industry case.

Fuzzy modeling and analytic hierarchy processing-means to quantify risk levels associated with occupational injuries. II. The development of a fuzzy rule-based model for the prediction of injury

This paper presents the second phase in a two-part research project to develop a fuzzy rule-based expert system for predicting occupational injuries of the forearm and hand. Analytic hierarchy processing (AHP) is used to assign relative weights to the identified risk factors. A fuzzy rule base is constructed with all of the potential combinations for the given factors. The input parameters are linguistic variables obtained in the first part of the research. These inputs are fuzzified and defuzzified to provide two system outputs: a linguistic value and a numeric value as a prediction of injury. The system provides linguistic risk levels as well as quantified risks in assessing the overall risk of injury. The system evaluation was conducted resulting in calculations for Type I and Type II errors. The contributions and limitations of the system are discussed.

DDM: decision support system for hierarchical dynamic decision making

Abstract A simulation-based decision support system for AHP (Analytic Hierarchy Process) is presented in this paper. The software, named DDM (Dynamic Decision Making), is applicable to dynamic decision scenarios where probabilistic interactions exist between the factors in the AHP hierarchy. Decision scenarios are generated using probability information specified by the user. The output of the simulation is the relative frequency of the selection of each alternative rather than a single final selection. The decision maker will evaluate the distribution histogram and make final selection based on his or her own inherent disposition to risk. DDM can be used for forecasting or for evaluating strategic planning options.

A metric for agility measurement in product development

The achievement of lower product cycle times requires the use of a multidimensional agility measure. In addition to quantifying a manufacturers ability to respond quickly and cost effectively to sudden unexpected changes in customer demands, this measure should be able to guide the concurrent engineering effort. Such a measure is described in this paper. This model utilizes state variable based performance metrics, which account for the hierarchy among design activities. Also, to address the subjectivity that is inherent in this process and provide confidence interval estimates for the resulting agility measures, simulation modeling is used.