A heuristic scheduling policy for multi-item, single-machine production systems with time-varying, stochastic demands

Article Abstract:

A heuristic scheduling policy is suggested for single-machine, multi-item production schemes facing time-varying demands that are stochastic in nature. This dynamic cycle lengths heuristic meshes feedback control based on keeping track of inventory levels with the keeping of economic production cycles. Application is time period by time period to come to decisions about which items to produce in certain quantities during the next period of time. These amounts show production cycles changed each time period in response to variations between actual and projected inventory levels and in response to demand rate changes. The basic scheduling policy is extended to mesh its use with tactical planning. Also discussed are the results of simulations exams of the heuristic against other scheduling policies.

Scheduling (Management), Heuristic programming

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The stochastic modeling of purchase intentions and behavior

Article Abstract:

Real purchasing behavior and planned purchasing have differences and these taken into account through an estimation model for finding the demographic/psychographic correlates of behavior using purchasing plans. An hierarchical Bayes method can be applied to achieve maximal pooling of data across brands when data on multiple brands from a common product class are available. The said estimation procedure can be very helpful in enhancing the accuracy of estimates of the relationship of the covariates and actual purchase behavior as illustrated using simulated data and data from a marketing study. This ability to improve the application of intentions data may result to more accurate market segmentation and therefore, enhanced marketing management decisions.

Purchasing Management, Purchasing, Bayesian statistical decision theory, Bayesian analysis, Markov processes, Monte Carlo method, Monte Carlo methods

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