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This paper seeks to develop an effective methodology to solve the partner selection, and production-distribution planning problem that arises in the formation of an optimal supply chain. The methodology includes a mathematical model which describes the characteristics of the integrated supply chain and an efficient genetic search algorithm. The objective is to minimize the total operating cost. Unlike canonical genetic algorithms, individuals of the proposed genetic algorithm have the ability to learn from their ancestors in order to enhance the convergence speed and the quality of the final solution. The performance of the proposed algorithm is evaluated by solving a set of randomly generated problems. Comparison of the results obtained with that of a conventional genetic algorithm clearly shows that the proposed algorithm is superior to the conventional genetic algorithm. The proposed methodology is described in some details in the hope of thus stimulating the use of similar methodology to the solution of other important problems in industrial engineering practice. Suggestions for future work are also included. [ABSTRACT FROM AUTHOR]

The strength of Micro Systems Technology (MST) is the ability to fabricate a large number of small devices economically. However such devices tend to have errors caused by the variations of fabrication and inherent noise signals such as Brownian motion or Johnson noise. This paper advances the understanding of Micro Switch Group Sensors (MSGS). In this paper, a MSGS comprising of 20 switches has been built using electronic circuits and tested to verify the performance. The output signal is compared to the number of switches turned on and analysed. The fluctuations of the output signals and the measured performance of the electrical circuit was shown to be in good agreement with the theoretical prediction. The output signal is compared to the input signal and shows that MSGS has been successfully applied as a measurement tool. [ABSTRACT FROM AUTHOR]