Some Thoughts on MCDM: Myths and Ideas
Decision making is a most important function and process of management. It is one of the most pervasive problems of management and a pervasive problem in general. Multiple criteria decision making is an approach to decision making. It is the making of decisions taking into consideration multiple conflicting objectives. How do we make decisions? What is involved? There are many simple paradigms, and until recently about all that could be said was that you should list the alternatives, score them on certain dimensions, and then somehow aggregate the scores, and choose an alternative. Unfortunately, many excellent decision makers simply choose an alternative, based on instinct, intuition, or feel. Then they go on. Though that is fine in many cases; in some cases it is not. In this paper I consider some myths of decision making. I then propose some steps that we, as decision analysts and scientists, should consider taking to try to sensitize decision makers to thinking more about their decisions and making better decisions.
Optimum Multivariate Stratified Sampling Designs with Travel Cost: A Multiobjective Integer Nonlinear Programming Approach
The problem of optimum allocation in stratified sampling and its solution is well known in sampling literature for univariate populations (see Cochran, 1977; Sukhatme et al., 1984). In multivariate populations where more than one characteristics are to be studied on every selected unit of the population the problem of finding an optimum allocation becomes more complex due to conflicting behaviour of characteristics. Various authors such as Dalenius (1953, 1957), Ghosh (1958), Yates (1960), Aoyama (1963), Gren (1964, 1966), Folks and Antle (1965), Hartley (1965), Kokan and Khan (1967), Chatterjee (1972), Ahsan and Khan (1977, 1982), Chromy (1987), Wywial (1988), Bethel (1989), Kreienbrock (1993), Jahan et al. (1994), Khan et al. (1997), Khan et al. (2003), Ahsan et al. (2005), Díaz-García and Ulloa (2006, 2008), Ahsan et al. (2009) etc. used different compromise criteria to work out a compromise allocation that is optimum for all characteristics in some sense. Almost all the previous authors used some function of the sampling variances of the estimators of various characteristics to be measured as an objective that is to be minimized for a fixed cost given as a linear function of sample allocations. Because the variances are not unit free it is more logical to consider the minimization of some function of squared coefficient of variations as an objective. Previously this concept was used by Kozok (2006). Furthermore, investigators have to approach the sampled units in order to get the observations. This involves some travel cost. Usually this cost is neglected while constructing a cost function. This travel cost may be significant in some surveys. For example if the strata consist of some geographically difficult-to-approach areas. The authors problem of optimum allocation in multivariate stratified sampling is discussed with an objective to minimize simultaneously the coefficients of variation of the estimators of various characteristics under a cost constraint that includes the measurement as well as travel cost. The formulated problem of obtaining an optimum compromise allocation turns out to be a multiobjective all-integer nonlinear programming problem. Three different approaches are considered: the value function approach, ∈ –constraint method, and Distance–based method, to obtain compromise allocations. The cost function considered also includes the travel cost within stratum to reach the selected units. Additional restrictions are placed on the sample sizes to avoid oversampling and ensure the availability of the estimates of the strata variances. Numerical examples are also presented to illustrate the computational details of the proposed methods.
A device for uranium series leaching from glass fiber in HEPA filter
For the disposal of a high efficiency particulate air (HEPA) glass filter into the environment, the glass fiber should be leached to lower its radioactive concentration to the clearance level. To derive an optimum method for the removal of uranium series from a HEPA glass fiber, five methods were applied in this study. That is, chemical leaching by a 4.0 M HNO3–0.1 M Ce(IV) solution, chemical leaching by a 5 wt% NaOH solution, chemical leaching by a 0.5 M H2O2–1.0 M Na2CO3 solution, chemical consecutive chemical leaching by a 4.0 M HNO3 solution, and repeated chemical leaching by a 4.0 M HNO3 solution were used to remove the uranium series. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after leaching for 5 h by the 4.0 M HNO3–0.1 M Ce(IV) solution were 2.1, 0.3, 1.1, and 1.2 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after leaching for 36 h by 4.0 M HNO3–0.1 M Ce(IV) solution were 76.9, 3.4, 63.7, and 71.9 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after leaching for 8 h by a 0.5 M H2O2–1.0 M Na2CO3 solution were 8.9, 0.0, 1.91, and 6.4 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after consecutive leaching for 8 h by the 4.0 M HNO3 solution were 2.08, 0.12, 1.55, and 2.0 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after three repetitions of leaching for 3 h by the 4.0 M HNO3 solution were 0.02, 0.02, 0.29, and 0.26 Bq/g. Meanwhile, the removal efficiencies of 238U, 235U, 226Ra, and 234Th from the waste solution after its precipitation–filtration treatment with NaOH and alum for reuse of the 4.0 M HNO3 waste solution were 100, 100, 93.3, and 100%.