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.

No abstract

No abstract

STATEMENT OF PURPOSEThe purpose of this panel is to discuss the effective implementation of cost modeling methodologies on the factory floor. Our target audience is industrial and manufacturing engineers that are experienced in simulation modeling and are familiar with manufacturing cost issues. MANUFACTURING COST ACCOUNTING -THE ARENAIn todays competitive manufacturing environment, engineers are often required to provide performance analysis memcs in terms of actual dollarsthe bottom line. No longer can the engineer provide simulation results in terms of reduced work-in-process (WIP) or increased capacity; managers are demanding that quantitative (dollar) calculations on factory performance be provided before projects are approved. This requirement is further complicated, as recent studies have demonstrated that the financial information maintained by accounting personnel is inadequate or even incomect in properly evaluating these systems. Outdated accounting principles that were developed during the industrial revolution are being improperly applied in the evaluation of today's complex manufacturing systems. Thus, engineers are required to develop their own techniques and tools to accurately evaluate the financial impact of such technologies as Total Quality Control (TQC) and Just in Time (JIT) manufacturing. COST MEASUREMENT VERSUS COST MODELINGyield, etc. On-line accounting systems can measure the costs associated with a certain product or processalthough they require the maintenance of copius quantities of data. Modeled systems, by comparison, are effective for performing what-if analysis on a simulated system. A verifidvalidated model may be used to accurately assess the benefits of an additional machine, or quantify the impact of process variability on throughput. These systems also involve large amounts of performance databut are easily maintained and manipulated as they are typically not directly associated with on-line information systems. A number of manufacturing and service f m s have successfully developed simulation models that include economic analysis. In addition, a number of simulation software packages provide constructs for economic modeling. COST MANAGEMENT -SOME CURRENT ISSUESFinancial information has traditionally been associated solely with accounting personnel; hence, manufacturing cost management is a relatively new concept. The development of new costing philosophies based on logical management rules as opposed to general accounting principles is still a novel idea to many people.Just as any novel methodology (JIT, TQC) needs to be accepted by all members of the manufacturing environment before benefits are fully realized, manufacturing cost accounting must be accepted by engineers, operators, managers and accountants before it can truly impact performance. A p a t deal of research and development work has been performed by such noted academicians as Robert S . Kaplan (Harvard Business School) and Peter B. B. Tumey (Portland State). Although their work has successfully explored cost t...