There is still considerable doubt and even anxiety among simulation modelers as to what the methodologically correct guidelines or procedures for validating simulation models should be. Epistemically, the approaches one finds in the simulation literature run the gamut from objectivist to relativist with shades in between. At present in the philosophy of science, there appears to be a convergence toward a nonalgorithmic but discursive and nonrelativistic view of the argumentation involved in warranting scientific theorizing. The present paper attempts to give a description of the various philosophical positions as well as to summarize their problems and the kinds of evidentiary arguments they would each allow in arriving at defensible simulation models. From the debate, we attempt to set out a perspective that frees the practioner to pursue a varied set of approaches to validation with a diminished burden of methodological anxiety. Reciprocally this perspective does not let the modeler off of the hook but rather converts the validation problem into an ethical problem in which the practitioner must responsibly and professionally argue for the warrant of the model.Simulation, Validation, Philosophy of Science, Hermeneutics

No abstract

Where the durations of the activities in an acyclic scheduling network are random variables, this paper obtains upper and lower bounding distributions for the activity starting- and finishing-time probability distributions, as well as upper and lower bounds for the expected starting and finishing time of each network activity, and for expected network resource flows. The tightness of the bounds for various networks is examined, and a computational experience with the methods is reported.

No abstract

This discussion demonstrates the application of discrete optimal control theory to production planning problems. In particular we show that most of the previous models which have appeared in the literature on production planning (scheduling, smoothing and work force balancing) can be characterized as special cases of the control theory problem formulation. This more general model relaxes a number of the assumptions required by other formulations in capturing a wide spectrum of production planning policies. Computational aspects of the control theory approach are discussed and solution results included for numerical examples.