5. 文字

可以使用css語法來修飾文字，例如改變顏色，這個之前提過了，再來看看有哪些可以變化。

字體

可以使用font相關屬性來改變文字，計有:
舉例說明。


css code:



其中family列好幾個是以防前面的不支援，則使用後續的替補。style可決定是否為斜體， size是字體大小，weight是粗細，variant的small-caps是小型大寫字。結果顯示如下:

This paper presents a study on routing problems associated with bridge inspection tasks. In the evaluatedproblems, a bridge inspection team must depart from the depot, visit bridges, and eventually return tothe depot. Since a single inspection team may require several days to perform this task, the inspectorsmust find lodging accommodations during the inspection period. This problem becomes a special type ofvehicle routing problem (VRP). Two types of scenarios are established for the bridge inspection problem. In the first scenario, only one inspection team is evaluated, and in the second scenario, more than oneinspection team and a specific inspection duration are assessed. The goal of this study is to determineoptimal routes and to find accommodations that minimize the total inspection cost, including the traveland lodging costs. The problem is solved using an ant colony optimization (ACO) algorithm. In addition, alocal search method is proposed for improving the quality of the solutions. Three benchmark datasets aregenerated to estimate the performance of the proposed method. First, a combination of the ACO parametervalues that yielded overall good results is determined, and subsequently the proposed method is appliedto the benchmarks. The results indicate that the proposed process yield promising solutions within areasonable time frame.

text

text的屬性如下:
css code如下:


顯示結果:

This paper presents a study on routing problems associated with bridge inspection tasks. In the evaluatedproblems, a bridge inspection team must depart from the depot, visit bridges, and eventually return tothe depot. Since a single inspection team may require several days to perform this task, the inspectorsmust find lodging accommodations during the inspection period. This problem becomes a special type ofvehicle routing problem (VRP). Two types of scenarios are established for the bridge inspection problem. In the first scenario, only one inspection team is evaluated, and in the second scenario, more than oneinspection team and a specific inspection duration are assessed. The goal of this study is to determineoptimal routes and to find accommodations that minimize the total inspection cost, including the traveland lodging costs. The problem is solved using an ant colony optimization (ACO) algorithm. In addition, alocal search method is proposed for improving the quality of the solutions. Three benchmark datasets aregenerated to estimate the performance of the proposed method. First, a combination of the ACO parametervalues that yielded overall good results is determined, and subsequently the proposed method is appliedto the benchmarks. The results indicate that the proposed process yield promising solutions within areasonable time frame.


多行文字

我們可以將文字排列成多個區塊，類似報紙的效果，需使用column關鍵字。其包含以下屬性:
以下例說明:

顯示結果如下:

Application of the ant colony optimization in the resolution of the bridge inspection routing problem

This paper presents a study on routing problems associated with bridge inspection tasks. In the evaluatedproblems, a bridge inspection team must depart from the depot, visit bridges, and eventually return tothe depot. Since a single inspection team may require several days to perform this task, the inspectorsmust find lodging accommodations during the inspection period. This problem becomes a special type ofvehicle routing problem (VRP). Two types of scenarios are established for the bridge inspection problem. In the first scenario, only one inspection team is evaluated, and in the second scenario, more than oneinspection team and a specific inspection duration are assessed. The goal of this study is to determineoptimal routes and to find accommodations that minimize the total inspection cost, including the traveland lodging costs. The problem is solved using an ant colony optimization (ACO) algorithm. In addition, alocal search method is proposed for improving the quality of the solutions. Three benchmark datasets aregenerated to estimate the performance of the proposed method. First, a combination of the ACO parametervalues that yielded overall good results is determined, and subsequently the proposed method is appliedto the benchmarks. The results indicate that the proposed process yield promising solutions within areasonable time frame.