A technique is presented which uses Response Surface Methodology to select a cutting tool in order to maximize profit. Different tools are analyzed to determine what combinations of cutting speed, feed, and depth of cut will give maximum profit for each kind of tool. Only then may different tools be compared. There is a possibility that the tool which is capable of yielding maximum profit may be most economical at operating conditions which are somewhat anomolous. With conventional tests a suboptimal tool may be selected, or the best tool may be used at suboptimal operating conditions. The contribution of this article is the expression of profit as a function of the direct physical decision variables and the subsequent optimization. The use of these techniques with economic decision theory is unique.
The aim of this article is to demonstrate how decision analysis can be helpful in formulating and solving capacity expansion problems faced by electric utility companies. This general problem involves the specification of sizes and types of generation capacity (i.e., nuclear units, coal units, combustion turbines) to be brought on line at various points in time. Decision analysis is suggested here as a means of identifying alternative strategies in the early stages of expansion plan ning in small power systems (i.e., 5000 MW or less). Such strategies serve as loose guidelines for detailed and often time-consuming studies that follow. An example problem (fashioned after an actual situation but with the data changed) is utilized to demonstrate decision analysis of nuclear-and coal-fueled alternatives for meeting projected increases in need for base-loaded generation facilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.