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ABSTRACT: In recent years the reported successes of Japanese production systems, particularly the just-in-time approach to inventory control, has caused managers to focus more of their attention on efficient decision-making procedures for determining production schedules that minimize inventory costs. One such potential area of attention is the economic lot-scheduling problem (ELSP), which occurs in a variety of manufacturing environments where machining operations are prevalent. The economic lot-scheduling problem addresses the determination of lot sizes for N products with constant demand (and cycled through one machine with a given production rate) to minimize setup and inventory costs. The most successful solution approaches to the ELSP have been based on the concept of a basic period that is of sufficient length for the production of all items, even though each item might not be produced during each repetition of the basic period. This paper proposes a heuristic approach to the solution of the ELSP (referred to as the method of prime subperiods), which is an extension of the basic period approaches. The procedure is described and demonstrated via an example and then tested using a set of six example problems previously employed in the literature related to the ELSP. The results indicate as good or superior performance by the proposed method of prime subperiods.
Decision Sciences 06/2007; 20(4):794 - 809. · 1.36 Impact Factor
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ABSTRACT: The production control system for a shop can be viewed as consisting of three sequential stages, the order-promising stage, the order-release stage, and the dispatching (or shop floor) stage. The first stage, wherein a customer's job arrives and is assigned a due date, provides the focus for this research. In particular, the performance of six regression-based due-date assignment rules found in the literature is compared with due dates determined by a neural network. The purpose is to see whether neural networks hold any promise for application in this area.For the particular shop and the conditions studied, it is found that the neural network outperforms all six conventional rules according to mean-absolute-deviation (MAD) and standard-deviation-of-lateness (SDL) criteria, although for one rule on the latter criterion, the difference is not statistically significant. Further analysis indicates that this conclusion generally holds both when the amount of data available is varied and a second, more structured shop is studied. On a third shop with random routings, the neural network outperforms the best conventional method according to the MAD measure, but results are mixed for the SDL criterion. The superior performance of the neural network leads us also to evaluate a regression model nonlinear in its independent variables, a case not considered in the due-date literature. The nonlinear model generally outperforms the conventional rules on MAD and SDL. The neural network outperforms the nonlinear model on MAD, while the results for SDL are not as clear.
Decision Sciences 06/2007; 25(5‐6):825 - 851. · 1.36 Impact Factor
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ABSTRACT: Traditional methods of due-date assignment presented in the literature and used in practice generally assume cost-of-earliness and cost-of-tardiness 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 and tardy behavior. In this article the consequences of such assumptions are pointed out, and a cost-based assignment scheme is suggested whereby the cost of early completion may differ in form and/or degree from 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 considered that: (a) implicitly ignoring cost-based assignments can be very costly; (b) simpler regression-based rules cited in the literature are very poor cost performers; (c) if the earliness and tardiness cost functions are both linear, linear programming and neural networks are the methodologies of choice; and (d) if the form of the earliness cost function differs from that of the tardiness cost function, neural networks are statistically superior performers. Finally, it is noted that neural networks can be used for a wide range of cost functions, whereas the other methodologies are significantly more restricted. © 1997 John Wiley & Sons, Inc.
Naval Research Logistics 12/1998; 44(1):21 - 46. · 1.04 Impact Factor
