COST-BASED DUE-DATE ASSIGNMENT WITH THE USE OF CLASSICAL AND NEURAL-NETWORK APPROACHES

Traditional methods of due-date assignment presented in the literature and used in practice generally assume cost-of-earliness and cost-of-t ardiness functions that may bear little resemblance to true costs. For example, practitioners using ordinary least-squares (OLS) regression implicitly minimize a quadratic cost function symmetric about the due date, thereby assigning equal second-order costs to early completion a nd tardy behavior. In this article the consequences of such assumption s are pointed out, and a cost-based assignment scheme is suggested whe reby the cost of early completion may differ in form and/or degree fro m the cost of tardiness. Two classical approaches (OLS regression and mathematical programming) as well as a neural-network methodology for solving this problem are developed and compared on three hypothetical shops using simulation techniques. It is found for the cases considere d that: (a) implicitly ignoring cost-based assignments can be very cos tly; (b) simpler regression-based rules cited in the literature are ve ry poor cost performers; (c) if the earliness and tardiness cost funct ions are both linear, linear programming and neural networks are the m ethodologies of choice; and (d) if the form of the earliness cost func tion differs from that of the tardiness cost function, neural networks are statistically superior performers. Finally, it is noted that neur al networks can be used for a wide range of cost functions, whereas th e other methodologies are significantly more restricted. (C) 1997 John Wiley & Sons, Inc.