Innovative Production Machines and Systems Conference - Optimisation Techniques

Welcome to the Intelligent Optimisation Techniques Session

Ebubekir — Sat, 01 Jul 2006 20:01:20 +0100

Welcome to IPROMS 2006 virtual conference. And the Session on Intelligent Optimisation Techniques for Production Machines and Systems.
I am Ebubekir and am here to keep discussions going and to help resolve any problems or questions

An efficient meta-heuristic for the single machine common due date scheduling problem

Nearchou — Sun, 02 Jul 2006 13:32:04 +0100

Andreas C. Nearchou

A new meta-heuristic for scheduling multiple jobs on a single machine so that they are completed by a common specified date (neither earlier, nor later) is addressed in this paper. Costs are set depending on whether a job finished before (earliness), or after (tardiness) the specified due date. The objective is to minimize the total weighted earliness and tardiness penalized costs from the specified common due date. Minimizing these costs pushes the completion time of each job as close as possible to the due date. Extensive computational experiments over public benchmark problems show the effectiveness of the developed approach. In particular, the proposed meta-heuristic put new improved upper bounds on the majority of the benchmarks test problems.

The Bees Algorithm – A Novel Tool for Complex Optimisation Problems

Ebubekir — Mon, 26 Jun 2006 17:40:07 +0100

Authors : D.T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri, S. Rahim, and M. Zaidi

Keywords : Bees Algorithm, Function Optimisation, Swarm Intelligence

Optimization of fixture layout by means of the genetic algorithm

Ebubekir — Mon, 26 Jun 2006 17:37:52 +0100

Authors : T. Aoyama, Y. Kakinuma, I. Inasaki

Keywords : Fixture, FEM, Genetic algorithm

The fixturing process for holding and locating workpieces on machine tools is essential in manufacturing systems. In this study, a new fixturing support system is proposed. The elastic deformation of the workpiece caused by the fixturing forces is analyzed by the finite element method, and the optimum fixturing position which results in the minimum form error of the surface to be machined is determined. The genetic algorithm is applied to the optimization process of the fixturing condition.

Evolutionary Approach to Measure Production Performance

Ebubekir — Mon, 26 Jun 2006 17:35:43 +0100

In order to compete on global markets today’s production needs to keep track and continuously optimize its performance. It is crucial for a performance measurement system to enable to a general survey fast, so that trends can be identified timely in advance. Generally a broad set of performance indicators forms the basis of such a system. By combining these measures with benchmarking and target-setting a successful system for assessing and improving performance can be established. The BETTI® Benchmark (Benchmark Tool to Improve the Production Performance) is such an exemplary performance measurement system. In order to increase its effectiveness it has recently been extended by continuously including the experience and knowledge attained in previous benchmarks into the analysis. Additionally it allows appraising the consequences of actions targeted at improving production performance.

Feature selection for SPC chart pattern recognition using fractional factorial experimental design

Ebubekir — Mon, 26 Jun 2006 17:33:19 +0100

Feature selection is one of the important steps in designing a pattern recognizer. This paper presents a study to select a minimal set of statistical features for SPC chart pattern recognition using the fractional factorial experimental design. A resolution IV design was used to identify the significant features from a list of ten possible candidates to represent the input data streams. Further judgment was adopted to arrive at the final selection in the light of some ambiguities among confounded two-factor interactions. The final six selected features set comprising, autocorrelation, cusum, mean, standard deviation, mean-square value, and skewness as the input vector resulted in an average correct classification rate of 97.1% and standard deviation of 0.878. The methodology adopted in this study could be applied to other feature selection problems beside for SPC chart pattern recognition.