Optimization on Emergency Resources Transportation Network Based on Bayes Risk Function: A Case Study

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="9"><hr/></td></tr><tr><td class="align_left">The affected area</td><td class="align_center">1</td><td class="align_center">2</td><td class="align_center">3</td><td class="align_center">4</td><td class="align_center">5</td><td class="align_center">6</td><td class="align_center">7</td><td class="align_center">8</td></tr><tr><td class="align_left">Time threshold <svg height="12.5794pt" id="M173" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 33.8512 12.5794" width="33.8512pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M620 675H597C578 656 570 650 541 650H144C112 650 104 653 94 675H72C59 618 42 552 23 493L53 491C71 534 88 564 105 585C124 608 144 615 238 615H290L197 121C182 40 174 34 88 28L82 0H361L367 28C275 34 266 38 281 121L374 615H441C522 615 543 608 553 583C562 560 566 531 565 493L597 494C603 551 612 629 620 675Z" id="g113-85"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="637" vert-adv-y="637"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,7.176,3.132)"><path d="M504 89L488 119C453 85 428 69 416 69C408 69 405 75 412 102L462 304C492 438 460 451 436 451S388 441 356 423C311 397 229 336 170 256H168L189 340C208 418 200 451 177 451C144 451 82 413 24 352L40 322C65 345 98 369 108 369C114 369 119 363 112 335L28 -3L36 -12C54 -3 83 4 112 10C125 73 138 122 153 174C205 258 328 382 376 382C395 382 399 369 384 305L330 93C312 25 323 -12 351 -12C378 -12 439 21 504 89Z" id="g50-111"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="512" vert-adv-y="512"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,12.351,0)"><path d="M368 703H309L44 -163H104L368 703Z" id="g113-48"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="412" vert-adv-y="412"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,17.712,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z" id="g113-41"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="346" vert-adv-y="346"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,22.21,0)"><path d="M513 0V26C455 31 447 37 447 103V278C447 398 390 449 309 449C255 449 202 412 166 376V712C127 700 67 684 19 677V653C85 647 87 643 87 580V103C87 37 79 31 19 26L18 0H231V26C171 32 166 39 166 103V341C194 373 232 392 270 392C337 392 368 351 368 269V103C368 38 360 32 302 26V0H513Z" id="g121-102"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="534" vert-adv-y="534"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,29.152,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z" id="g113-42"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="346" vert-adv-y="346"></glyph.data></g></svg></td><td class="align_center">1.92</td><td class="align_center">2.57</td><td class="align_center">1.02</td><td class="align_center">5.24</td><td class="align_center">5.34</td><td class="align_center">3.22</td><td class="align_center">4.62</td><td class="align_center">5.21</td></tr><tr class="table-tr"><td colspan="9"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Time threshold of affected area <i>n</i>.

Mathematical Problems in Engineering

tab4

Table 4

Table 4: Optimization on Emergency Resources Transportation Network Based on Bayes Risk Function: A Case Study 