Learning by objectives for adaptive shop-floor scheduling

Article Abstract:

An adaptive approach in managing manufacturing processes was designed in an effort to provide an efficient means of conducting production scheduling. The approach dwells on the use of temporally local scheduling strategies that are capable of meeting system-wide objectives. Such an approach does not only enable firms to satisfy objectives, but enhance overall performance level, as well. The approach further employs the GA-based learning concept in designing operating policies in the shop floor level.

Scheduling (Management), Manufacturing processes, Manufacturing, Shop floor control

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Rationing capacity between two product classes

Article Abstract:

A decision theory-based solution to the short-term capacity allocation problem encountered by make-to-order manufacturers is developed. The problem arises when firms anticipate total demand in excess of their production capacity. To solve it, a capacity rationing policy that allows firms to discriminate between different product categories is introduced. In addition, its effectiveness is evaluated with respect to total profit, capacity utilization, and fill rate of each product category.

Resource allocation, Industrial capacity

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What to learn from near misses: an inductive learning approach to credit risk assessment

Article Abstract:

The Dynamic Updating Process (DUP) establishes a decision support tool in credit risk analysis, using an inductive learning approach. The process uses positive and negative near misses to evaluate the accuracy of the inductive learning process and relocates the boundaries of these misses for a constructive credit granting decision model. The learning process is found to have greater accuracy and stability after DUP is performed.

Credit Management, Behavior Theory, Risk assessment, Technology application, Human behavior, Expert systems, Decision tree, Decision trees

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