Robert L. Armacost

AN AHP FRAMEWORK FOR PRIORITIZING CUSTOMER REQUIREMENTS IN QFD: AN INDUSTRIALIZED HOUSING APPLICATION

Abstract Construction of housing in the United States is highly decentralized. There is an increasing use of manufactured components and modules constructed off-site at a manufacturing facility and assembled at die building site. However, there has been little use of modern manufacturing processes and controls. In an effort to develop energy efficient, affordable industrialized housing, a total engineering design approach is needed. This study uses a concurrent engineering approach to examine the production of an essential component in industrialized housing, a manufactured exterior structural wall panel. In particular, we apply Quality Function Deployment to fully integrate the customers requirements. This paper focuses on the identification and prioritization of those customer requirements. We integrate the Analytic Hierarchy process with QFD to establish a framework for prioritizing customer requirements.

Minimizing makespan on parallel machines with release time and machine eligibility restrictions

Scheduling in the presence of machine eligibility restrictions when not all machines can process all the jobs is a practical problem into which there has been little research. Pinedo demonstrated that the least flexible job (LFJ) rule was optimal for minimizing makespan in a parallel machine environment (with equal processing times) when there are machine eligibility restrictions, the machine eligibility sets are nested, and no release time constraint exists. The results presented in this paper demonstrate that for the more realistic case when the machine eligibility sets are not nested (with unequal processing times known when a job is released), the longest processing time (LPT) rule performs better than the LFJ rule in the presence or absence of release time stipulations. The experimental results show that the order (job selection first or machine selection first) does not matter, which is consistent with Pinedo’s observation. The new heuristics that are evaluated in this paper provide important results for the parallel machine scheduling problem and their applications in the semiconductor industry, which motivated this research.

Parallel machine scheduling with release time and machine eligibility restrictions

Abstract We present an algorithm for the problem of minimizing maximum lateness in a parallel machine environment with release dates and machine eligibility restrictions (P m | r j , M j | L max ) for the special case where due dates are equal to release dates plus a constant. No preemption of jobs is allowed. The algorithm has been evaluated using real data from an operational environment of a semiconductor manufacturing firm. Comparison is also made with the actual scheduling system being used by the organization.

Identification of Determinant Attributes Using the Analytic Hierarchy Process

This article develops a new approach, using information available in the intermediate and final phases of the analytic hierarchy process, to explicitly identify which attributes or criteria are determinant in making a choice among several given alternatives. The approach parallels that used in the popular direct dual questioning determinant attribute (DQDA) analysis, which has been widely used in marketing applications. Using the hierarchical structure and pairwise comparisons, the combined relative priorities of the criteria are compared with the relative priorities of the choice alternatives to compute determinance scores. These values are the basis for identifying which of the criteria are both important and different across alternatives (i.e., determinant). This new approach overcomes the potential ambiguities of traditional direct dual questioning methods. Moreover, the approach is easily extended to include decision hierarchies with multiple levels of attributes and subattributes.

Understanding Simulation Solutions to Resource Constrained Project Scheduling Problems with Stochastic Task Durations

AbstractThe project scheduling problem domain is an important research and applications area of engineering management. Recently introduced project scheduling software such as Risk+, @Risk for Project, SCRAM and Risk Master have facilitated the use of simulation to solve project scheduling problems with stochastic task durations. Practitioners, however, should be made aware that the solution algorithm used in these software systems is based on the implicit assumption of perfect information, an assumption that jeopardizes the feasibility of solution results. This paper discusses the impact of assuming perfect information, introduces a multi-period stochastic programming based model of the project scheduling problem with stochastic task durations, and presents an alternative simulation algorithm that does not assume the availability of perfect information. A simple case study is used to illustrate the practical implications of applying simulation to address project scheduling problems with stochastic task d...

The role of the nonanticipativity constraint in commercial software for stochastic project scheduling

Recently announced commercial project scheduling products address the stochastic project scheduling problem. Their solution methods do not consider the nonanticipativity constraint and thus provide potentially unattainable solutions to stochastic resource constrained project scheduling problems. The nonanticipativity constraint of multiperiod stochastic problems imposes the requirement that solutions be based only on information known at the time of decisions. Users should use the results from these programs with extreme care.

Using the Analytic Hierarchy Process as a Two-phase Integrated Decision Approach for Large Nominal Groups

When decision makers who comprise a large nominal group face an unstructured decision problem and no simultaneous interactive communications are available, problem identification and consensus building are difficult, if not impossible. Few tools are available to assist decision makers in this situation. The Analytic Hierarchy Process (AHP) has typically been used to evaluate a set of alternatives after a decision problem has been structured as a hierarchy with various levels of criteria above the alternatives. With a group of decision makers, AHP has been used to evaluate those alternatives either by consensus building or by combining judgments or priorities using the geometric mean to aggregate their preferences. In this paper, we extend the use of AHP to a situation involving a large nominal group of dispersed decision makers where the entire hierarchy is not defined at the outset. In particular, we use the AHP as an integrative approach to identify the priorities of the various criteria and then use those priorities to screen and consolidate a large set of potential alternatives. This results in considering a reduced set of alternatives that will be affected by the more important criteria. The consolidated set of alternatives is evaluated by each individual in the group using AHP, combined using the geometric mean, and the results are synthesized to obtain the overall priorities of the alternatives. The approach is demonstrated and evaluated in a case study to select an alunmi anniversary gift to the U.S. Coast Guard Academy with a large nominal group of decision-makers dispersed throughout the United States.

Review of techniques for optimizing multiple quality characteristics in product development

Abstract For a manufacturing organization to compete effectively in the global marketplace a strong product development process is essential. An important ingredient in the product development process is the identification and subsequent optimization of those product characteristics which denote quality. Relying upon inspection and testing to ensure product quality is inefficient and expensive. Rather, quality should be built into the design of the product. Consequently, product quality characteristics must be optimized during the product development process. Typically, there are several product quality characteristics which are important indicators of quality, and one or more of the quality characteristics may require optimization of both mean and variance. This paper describes the application of the methodologies available for optimization of quality characteristics in product development: Taguchis Methods, mathematical programming, and response surface methodology. The utilization of each technique for optimization of a single quality characteristic and for optimization of both the mean and variance of multiple quality characteristics is described.

The two-group discriminant problem with equal group mean vectors: An experimental evaluation of six linear/nonlinear programming formulations

Abstract Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discriminate among or classify entities (e.g., loan applicants, customers, employees, businesses) based on several of their distinguishing characteristics called discriminant variables. Statistical parametric procedures require that the mean vectors of discriminant variables for the populations of entities be different across groups. This requirement may not always be met in practical settings. This paper reports on a preliminary Monte Carlo simulation experiment which compares the performance of six 1 p -norm distance models including two linear and four nonlinear formulations as well as two statistical procedures to address the discriminant problem under equal mean vectors. The experimental data were generated from multivariate normal or nonnormal populations with equal or unequal dispersion matrices and with or without outliers. The results indicate that, when population mean vectors are equal, the most significant characteristics which affects the performance of all of the methods is the similarity (or dissimilarity) of dispersion matrices. The departure from normality and the presence of outliers and some of the interactions between these three factors are also statistically significant

State of the art in multiple response surface methodology

The research begins with a brief history of multiple response surface methodology, including a description of the available multiple response surface methodology (MRSM) approaches. To facilitate the appropriate application of the available multiple response surface methodology approaches, the research includes the development of a structure for categorizing and evaluating multiple response surface methodologies (MRSM). Three schemes for categorizing multiple response optimization methodologies are developed: (1) type of approach (MRSM1 and MRSM2); (2) number and type of response variables (two means, more than two means, mean and variance of a single quality characteristic, and means and variances of multiple quality characteristics); and (3) preference elicitation (implicit and explicit). Each multiple response surface methodology approach is categorized according to these schemes. The application of multiple response surface methodology in product development is described, including the use of simulation with multiple response surface methodology approaches. Once underlying probability distributions are determined, simulation can be used to generate data ordinarily obtained using the experiments conducted in a laboratory, thereby producing a considerable cost savings in product development.