Cost-based shop control using artificial neural networks

The production control system of a shop consists of three stages: due-date prediction, order release, and job dispatching. The literature has dealt thoroughly with the third stage, but there is a paucity of study on either of the first two stages or on interaction between the stages. This dissertation focuses on the first stage of production control, due-date prediction, by examining methodologies for improved prediction that go beyond either practitioner or published approaches. In particular, artificial neural networks and regression nonlinear in its variables are considered. In addition, interactive effects with the third stage, shop-floor dispatching, are taken into consideration.

The dissertation conducts three basic studies. The first examines neural networks and regression nonlinear in its variables as alternatives to conventional due-date prediction. The second proposes a new cost-based criterion and prediction methodology that explicitly includes costs of earliness and tardiness directly in the forecast; these costs may differ in form and/or degree from each other. And third, the benefit of tying together the first and third stages of production control is explored. The studies are conducted by statistically analyzing data generated from simulated shops.

Results of the first study conclude that both neural networks and regression nonlinear in its variables are preferred significantly to approaches advanced to date in the literature and in practice. Moreover, in the second study, it is found that the consequences of not using the cost-based criterion can be profound, particularly if a firm's cost function is asymmetric about the due date. Finally, it is discovered that the integrative, interactive methodology developed in the third study is significantly superior to the current non-integrative and non-interactive approaches. In particular, interactive neural network prediction is found to excel in the presence of asymmetric cost functions, whereas regression nonlinear in its variables is preferable under symmetric costs.