Knowledge that Transforms

A new type of PERT/CPM methodology is introduced whereby the individual activities within a project management network are endowed with resistive and capacitive elements. This methodology will enable us to mathematically define a completion time for the various activities and to relate the completion time to relevant cost in the completion of a particular task. It will also allow us to define the work done, and the rate at which work is being done, in an activity as a function of the applied effort and resource outlay. As a consequence, both the resource expenditure and the work done can be tracked within a network as a function of time.

Since the development of early versions of material requirements planning (mrp) systems, it has been known that a weak link in this technique is the failure to consider the available capacity at the time the lot sizes for individual items are calculated. Ignoring the available capacity may result in infeasible production plans, i.e., those that can only be accomplished with the use of overtime. We present a technique to search for feasible production plans by means of minimizing the total overtime. The technique is based on modifying periodic‐order‐quantity (poq) lot sizes within a tabu search framework. Computational experiments with the largest problem structure reported in the literature show that the procedure is effective in determining lot sizes for individual items that either minimize or eliminate overtime. Additional experiments reveal that, with appropriate calibration of search parameters, the procedure is also able to deal with more general cost functions (e.g., those that include holding and setup costs).

The costs of starting‐up and shutting down production lines (and plants) in a process industry are often quite high. Therefore, when a plant's capacity significantly exceeds its forecast demand over an annual planning horizon, a manufacturer must either plan temporary production line shutdowns during the year, or plan to temporarily idle production lines without formally shutting line(s) down. The trade‐offs between these two strategies can be complex. In this paper, we propose a methodology to evaluate the impact of both strategies on a plant's production costs by developing an analytical model based on the authors' experience with several process industries.

A product has been formally denned as being subject to sudden obsolescence if its lifetime is negative exponentially distributed. Using an approximate model, Masters suggested that the traditional method of incorporating obsolescence cost as a component of inventory holding costs in the economic order quantity (EOQ) model was appropriate‐for products subject to sudden obsolescence, provided that the obsolescence cost component was computed properly. Unfortunately, current practice of the EOQ model seriously underestimates the costs of sudden obsolescence. An exact model demonstrating that Masters' model also underestimated true lifetime costs and overestimated the optimal order quantity has been presented. Neither of these models addressed quantity discounts. Furthermore, with their cost‐minimization focus, these models fail to identify situations when minimized costs exceed expected revenues. We extend Joglekar and Lee's model to focus on maximizing profits, rather than minimizing costs. This model answers such questions as whether to stock the product at all, whether to accept a quantity discount offer, and what order quantity to use. Numerical examples and sensitivity analyses suggest that Masters' model provides a significant improvement over the traditional model in moving toward true optimality. However, they also illustrate situations where both the traditional and the Masters' model accept a quantity discount that deserves to be rejected and stock a product that should not be stocked. In such situations, it seems important that a retailer uses the profit‐maximization model presented here.

This paper demonstrates an approach to successfully managing change of manufacturing strategy. It first introduces the issues and management guidelines, and then describes how one company used this approach to achieve dramatic benefits from changing its manufacturing strategy. A third part of the paper elaborates on how the company made the approach work.

The classic problem of determining an optimum schedule for preventive maintenance when the time‐to‐failure (TTF) is random has been previously addressed in the literature under various distributional assumptions. However, no general solution that requires only partial distributional information (in the form of the first few moments) has been suggested. In this paper we develop solution procedures characterized by four features: (1) The solutions are general in the sense that only the first few moments of the TTF distribution need to be specified. (2) The optimal solution is given explicitly in terms of the decision variables, thus allowing simple sensitivity analysis. (3) When the TTF moments are unknown, a new two‐moment (partial and complete) distributional fitting procedure, used in the solution routine, ensures a better representation for the underlying TTF distribution relative to three‐moment or four‐moment distributional fitting. (4) When the TTF observations are truncated, simple routines to calculate maximum likelihood estimates are developed. We demonstrate that the partiál distributional specification, required for the new solution procedures, does not detract meaningfully from the optimality of the solution.

This is a study of single and parallel machine scheduling problems with controllable processing time for each job. The processing time for job j depends on the position of the job in the schedule and is a function of the number of resource units allocated to its processing. Processing time functions and processing cost functions are allowed to be nonlinear. The scheduling problems considered here have important applications in industry and include many of the existing scheduling models as special cases. For the single machine problem, the objective is minimization of total compression costs plus a scheduling measure. The scheduling measures include makespan, total flow time, total differences in completion times, total differences in waiting times, and total earliness and tardiness with a common due date for all jobs. Except when the total earliness and tardiness measure is involved, each case the problem is solved efficiently. Under an assumption typically satisfied in just‐in‐time systems, the problem with total earliness and tardiness measure is also solved efficiently. Finally, for a large class of processing time functions; parallel machine problems with total flow time and total earliness and tardiness measures are solved efficiently. In each case we reduce the problem to a transportation problem.