The Implications of Cognitive-style Findings for Operational Research

Cognitive style refers to differences that can be perceived in individuals due to differing cognitive structures. There has been a large amount of research on how cognitive style affects managerial decision-making, particularly in the context of management information systems, but little or no attempt to relate these findings to operational research. This paper reviews the findings of cognitive-style research from the perspective of operational research, and draws a number of useful inferences concerning the matching of OR aids to managers, the presentation of information and recommendations, the use and acceptance of OR, and the structure of OR groups. Further, it is argued that cognitive-style findings help to give a justification to the move towards ‘soft’ approaches to analysis.     

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Book reviews

     

Truncated Newton method for sparse unconstrained optimization using automatic differentiation

When solving large complex optimization problems, the user is faced with three major problems. These are (i) the cost in human time in obtaining accurate expressions for the derivatives involved; (ii) the need to store second derivative information; and (iii), of lessening importance, the time taken to solve the problem on the computer. For many problems, a significant part of the latter can be attributed to solving Newton-like equations. In the algorithm described, the equations are solved using a conjugate direction method that only needs the Hessian at the current point when it is multiplied by a trial vector. In this paper, we present a method that finds this product using automatic differentiation while only requiring vector storage. The method takes advantage of any sparsity in the Hessian matrix and computes exact derivatives. It avoids the complexity of symbolic differentiation, the inaccuracy of numerical differentiation, the labor of finding analytic derivatives, and the need for matrix store. When far from a minimum, an accurate solution to the Newton equations is not justified, so an approximate solution is obtained by using a version of Dembo and Steihaug's truncated Newton algorithm (Ref. 1).This paper was presented at the SIAM National Meeting, Boston, Massachusetts, 1986.     

Evaluating the Efficiency of US Air Force Real-Property Maintenance Activities

The objective of this research is to evaluate a new methodology, data envelopment analysis (DEA), as a tool for measuring and evaluating the operational efficiency of US Air Force organizations. This study involves the application of DEA to locate possible inefficiencies in the performance of US Air Force real-property maintenance activities. Dynamic behaviour, as exhibited via window analyses, is reviewed along with static counterparts. The testing was done in close coordination with Air Force officials, who reviewed the results for accuracy, validity and relevance. We conclude that this type of efficiency analysis does have value for the Air Force, where it can serve as a guide to auditors, budget programmers, managers and others in measuring, evaluating and enhancing operational efficiency.     

3D-TOFSIMS characterization of black spots in polymer light emitting diodes

The occurrence and formation of black spots areas in PolyLED devices has been studied by time-of-flight SIMS (TOFSIMS). The composition, shape and position of the black spots is visualised by three-dimensional (3D)-TOFSIMS depth-profiling. It has been established that the formation of non-emissive spots is due to the growth of aluminium oxide clusters at the AlBa/polymer interface. Electron injection in the black spots is lost by the resulting local increase of the resistivity of the cathode.     

Applying Stochastic Algorithms to a Locomotive Scheduling Problem

This paper addresses a problem common to all railway networks. Given a fixed train timetable and locomotives (or other forms of traction) of various types, each train must be allocated a locomotive. This paper examines the use of stochastic algorithms for such a problem. Two types of algorithm are used—a simple ‘local improvement’ method, performed successively from randomly chosen starting points, and a ‘simulated annealing’ approach. Both are found to give considerably better results than a deterministic method in current use, and the annealing approach is probably the better stochastic method.     

Rehabilitation of the Gauss-Jordan algorithm

Summary In this paper a Gauss-Jordan algorithm with column interchanges is presented and analysed. We show that, in contrast with Gaussian elimination, the Gauss-Jordan algorithm has essentially differing properties when using column interchanges instead of row interchanges for improving the numerical stability. For solutions obtained by Gauss-Jordan with column interchanges, a more satisfactory bound for the residual norm can be given. The analysis gives theoretical evidence that the algorithm yields numerical solutions as good as those obtained by Gaussian elimination and that, in most practical situations, the residuals are equally small. This is confirmed by numerical experiments. Moreover, timing experiments on a Cyber 205 vector computer show that the algorithm presented has good vectorisation properties.