We introduce a general framework that guides the management scientist's formulation of deterministic models of production processes. Using the framework, we reformulate the constraints of familiar linear programming-based planning models to specifically treat components of production lead time, thereby realizing a more accurate representation of the production process. In addition, the reformulation accommodates noninteger values for lead times as well as unequal-length planning periods. Manufacturing Resources Planning (MRP) and the Critical Path Method (CPM) are recast in terms of the framework, revealing opportunities for model generalization and extension, and their relationship to linear programming models.

A simple, fast and effective heuristic for the Simple Assembly Line Balancing Type I problem (minimizing the number of workstations) is proposed. A fast and effective branch-and-bound algorithm, which incorporates this heuristic for use in bounding, is developed. The algorithm introduces heuristic fathoming as a technique for reducing the size of the branch-and-bound tree. Methods to solve the Simple Assembly Line Balancing Type II problem (maximizing the production rate) are also described. Upper bounds on all heuristics for both problems are provided.

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Of the products tested, all produced similar change in shade tab value, none were sensitivity free, but NiteWhite Excel 2Z and Rembrandt Xtra-Comfort did not produce thermal sensitivity.

SUMMARYAn econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, based on the principle of maximum likelihood, uses entropic distance and convex programming techniques to estimate production functions. Empirical applications are presented to demonstrate the feasibility of the methodology in small and large datasets.