A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty

<table>Zoom-in of the conservative scheme, <svg height="16.6625" id="M181" style="vertical-align:-3.56265pt" version="1.1" viewbox="0 0 18.65 16.6625" width="18.65" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,12.162)"><path d="M619 670q0 -13 -9 -26t-18 -19q-13 -10 -25 2q-36 38 -66 38q-31 0 -54.5 -50t-45.5 -185h120l-20 -31l-107 -12q-23 -138 -57 -293q-27 -122 -55 -184.5t-75 -109.5q-60 -61 -114 -61q-25 0 -47.5 15t-22.5 31q0 17 31 44q11 8 20 -1q10 -11 31 -19t35 -8q26 0 47 19
q34 34 71 253l54 315h-90l-3 12l31 30h70q28 138 90 204q35 37 75 57.5t70 20.5q26 0 45 -14t19 -28z" id="x1D453"></path></g>
<g transform="matrix(.012,-0,0,-.012,10.975,4)"><path d="M535 230h-483v50h483v-50z" id="x2212"></path></g>
</svg>.</table>

Mathematical Problems in Engineering

fig4

Figure 4

Figure 4: A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty 