Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class

Wireless Personal Communications - Studying computer programming requires not only an understanding of theories and concepts, but also coding pragmatism. Success in studying or conducting such a...     

A genetic algorithm for the multi-class contingent bidding model

This paper proposes a Genetic Algorithm (GA) in searching for a near-optimal sequence of jobs in a make-to-order (MTO) production system in order to maximize the average marginal revenue earned per bid in the bidding model that allows contingent orders. Even though the complexity of the sequencing problem is NP-hard by nature, it is found to be a key determinant in improving the capacity allocation and the expected tardiness cost for an arriving order. The model incorporates operational constraints and marketing policies to effectively reflect the interests of customers. A simulation study was conducted to analyze the relative performance of the proposed system in a finite horizon. The results show the significant impact of the ordering sequence on the average marginal revenue and that the GA is an effective and efficient method to search for a good sequence and can improve the profit margin of the MTO firm and satisfaction of its customers.