A face search heuristic algorithm for optimizing over the efficient set

The problem of optimizing a linear function over the efficient set of a multiple objective linear program is an important but difficult problem in multiple criteria decision making. In this article we present a flexible face search heuristic algorithm for the problem. Preliminary computational experiments indicate that the algorithm gives very good estimates of the global optimum with relatively little computational effort. © 1993 John Wiley & Sons, Inc.     

Toward a determination of optimal multilevel sampling plans

Traditionally continuous sampling plans have been evaluated according to relatively few criteria. These typically include the OC curve (on which AQL systems are based), the ASN, and the AOQ curve. These characteristics are all calculated under the assumption that the process is “in control” so that mathematically they are derived as long-term averages. Thus, any two plans which (long term) spend the same proportion of time on each type of sampling inspection will be identical relative to these criteria. This is true whether sampling from lots or doing unit-by-unit inspection. The goal is to first establish desirable additional criteria and then to develop methods to determine which procedure (of those which satisfy the standard criteria) is optimal relative to the new criteria. To be considered will be measures of a plan's ability to detect a sudden drop in quality (such as ARL).     

Approximate Bayesian intervals for the reliability of series systems from mixed subsystem test data

The determination of exact Bayesian intervals for the reliability of a series system from subsystem test data gives rise to computational difficulties involving severe loss of computing precision as the number of subsystems in the system increases. The end points of Bayesian intervals are percentage points of the posterior distribution and these are shown to be well approximated by Cornish and Fisher expansions when the number of subsystems is small. As the number of subsystems in the system increases even greater accuracy is guaranteed by the asymptotic nature of the expansions. The system posterior distribution function is also shown to be well approximated by an Edgeworth expansion.     

A note on a modified primal-dual algorithm to speed convergence in solving linear programs

The primal-dual algorithm is modified in a two part procedure. In the first part, the pivot row is selected so that an artificial variable is always dropped. The end of the first part usually produces some basic variables with negative values. The second part consists of selecting the most negative basic variable. The equation, represented by the selected basic variable, is multiplied through by minus one and then added to all equations with negative basic variables; it is then augmented by an artificial variable. This procedure produces feasibility for all basic variables and maintains canonical form. The standard primal-dual method is then used to complete the solution. Computational results are presented.