Question

急！！！求高手翻译一段英文！！！速度20分！

The complexity of the pavement management problem increases
exponentially with the size of the problem. For example,
on the project level (one section), if A is the number
of rehabilitation actions to be considered over m years (or time
periods) within a study period, the number of possible combinatorial
solutions is Am. The number of solutions would become
(Am)N, where N is the number of pavement sections making
up a network. Clearly, the optimization of the pavement
management problem suffers from the curse of combinatorial
explosion and is labeled as ‘‘np-hard’’ by the operations research
community (Pilson et al. 1999). Therefore, it is quite
impossible to solve the microscopic pavement management
problem to optimality even for moderate size networks using
the fastest supercomputers and the most efficient optimization
techniques.
This research simplified the combinatorial problem by substituting
pavement classes for pavement projects. There are six
pavement classes and only four classes are considered in the
optimization process, namely those requiring major rehabilitation.
Also, the number of rehabilitation actions A is limited
to four with the number of time periods m limited to five.
Even with these limitations, the resulting combinatorial solutions
would still be very large.
The optimization process to this stated problem has been
attempted in a modified approach. The pavement network consists
of a number of pavement projects, each constructed of a
unique structural section, with each having its own performance
curve. The pavement projects are then grouped into six
classes based on their current conditions as defined by the
corresponding PSI values. The optimization process takes
place with respect to four variables representing the amount
of rehabilitation work that should be done on each pavement
class as a percentage of its total length during each time period
of the budget cycle.
The optimization technique used is the simultaneous uniform
search (exhaustive search) that divides each variable
range [0, 1.0] into search values that increase by 0.05 increments.
The optimization process is handled separately for each
year in the study period with the outcome of any preceding
year considered valid in the optimization of the following year.
The objective of the optimization is maximizing the annual
average network PSI for a given pavement network. There are
budget constraints to be verified prior to the selection of any
feasible solution. The details of the optimization procedure are
provided in the Methodology section.

Answer 1

复杂的路面管理问题增加
指数与问题的大小。举例来说，
该项目的水平（一组） ，如果是人数
康复行动，被视为米以上的年（或时间
期）的研究期间，一些可能的组合
解决办法是上午。数目的解决方案将成为
（上午） n ，其中n是多少路面的路段决策
了一个网络。显然，优化路面
管理问题受到诅咒组合
爆炸和标示为'' NP难''由运筹学
社区（ pilson等人。 1999年） 。因此，这是相当
不可能解决微观路面管理
问题的最优性，甚至为中度规模的网络使用
速度最快的超级计算机和最有效的优化
技术。
本研究简化了组合的问题，而代以
路面班路面工程。有6
路面班，只有4个班，被认为是在该
优化过程，即那些需要重大的康复服务。
此外，数目康复行动，一是有限的
四，与数目的时间米时限定为五。
即使这些限制，由此产生的组合解决方案
仍然会很大。
优化的过程，这说明问题已
试图在一种改进的方法。路面网络
了一批路面工程，每建造1
独特的结构部分，各有其自己的表现
曲线。路面工程，然后分成6
班的基础上，他们目前的条件界定，由
相应的防扩散安全倡议的价值观。优化过程需时
地方与尊重，以4个变量代表的数额
康复工作应做的每一个路面
工人阶级作为一个百分比，其总长度在每次的时间
该预算周期。
优化使用的技术是在同一时间统一
搜索（详尽无遗的搜索）划分为每个变数
范围[ 0 ， 1.0 ]到搜索的价值增加0.05递增。
优化过程是分开处理，为每个
今年在研究期间与结果的任何前
今年视为有效，在优化下一年。
目标的优化是最大限度地每年
平均网络防扩散安全倡议，为某一特定路面网络。有
预算限制，以验证之前，选择任何
可行的解决办法。详细的优化程序
所提供的方法一节。