One obvious solution is to set the objective variables x1 = x2 = 0, which corresponds to the extreme point at the origin.
In the primal problem the objective is replaced by the product (px) of a vector x = (x1, x2, x3, …, xn T, whose components are the objective variables and where the superscript "transpose" symbol indicates that the vector should be written vertically, and another vector p = (p1, p2, p3, …, pn), whose components are the coefficients of each of the objective variables.
If one of the objective variables is increased from zero while the other one is fixed at zero, the objective value x0 will increase as desired (subject to the slack variables satisfying the equality constraints).
In addition, the system of inequality constraints is replaced by Ax ≤ b, where the m by n matrix A replaces the m constraints on the n objective variables, and b = (b1, b2, b3, …, bm)T is a vector whose components are the inequality bounds.
In Table 1 the scoring of the objective variables is shown.
The two objective variables used in this study are Crew Idle Time and Missed Opportunities.
Optimization of both subsystems is achieved with recommendations for the objective variables.
As a result, objective variables that indicate modeled attribute values measured by specific rating scales and factors are organized as sets of model formulae.
The former groups that considered objective variables mainly suggested that density, mixed land use, street connectivity, outdoor seating and pedestrian-oriented designs encourage walking trips.
The CTP system [9] assigns points to 3 continuous objective variables (serum bilirubin, albumin, and prothrombin time) and 2 subjective variables (ascites and hepatic encephalopathy).
The global sensitivity analysis shows that the effects of design variables on objective variables are substantially different and no variable should be eliminated from the analysis.
