Question

急急急···哪位英语哥能帮我翻译一下下面的英文报纸内容 O(∩_∩)O谢谢！

Introduction
Planning under uncertainty is a common class of problems
found in process systems engineering. Some examples widely
found in the literature are capacity expansion, scheduling,
supply chain management, resource allocation, transportation,
unit commitment, and product design problems. The first studies
on planning under uncertainty could be accredited to
Dantzig (1955) and Beale (1955), who proposed the two-stage
stochastic models with recourse, which provide the mathematical
framework for this article.
The industrial importance of planning process capacity expansions
under uncertainty has been widely recognized and
discussed by several researchers (Ahmed and Sahinidis, 2000b;
Berman and Ganz, 1994; Eppen et al., 1989; Liu and Sahinidis,
1996; Murphy et al., 1987; Sahinidis et al., 1989). In the
majority of industrial applications, capacity expansion plans
require considerable amount of capital investment over a longrange
time horizon. Moreover, the inherent level of uncertainty
in forecast demands, availabilities, prices, technology, capital,
markets, and competition make these decisions very challenging
and complex. Therefore, several approaches were proposed
to formulate and solve this problem. They mainly differ in the
way uncertainty is handled, the robustness of the plans, and
their flexibility. This article follows the two-stage stochastic
programming approach with discretization of the uncertainty
space by random sampling of the parameter probability distributions.
In turn, the feasibility constraints for the problem are
enforced for every scenario in a deterministic fashion (taking
recourse actions with an associated cost) such that the resulting
plan or design is feasible under every possible uncertainty
realization.
A formal two-stage stochastic model for capacity planning in
the process industry was presented by Liu and Sahinidis (1996)
as an extension of the deterministic models developed by
Sahinidis et al. (1989). In the two-stage stochastic approach, it
is assumed that the capacity expansion plan is decided before
the actual realization of uncertain parameters (scenarios), allowing
only some operational recourse actions to take place to
improve the objective and correct any infeasibility. In this
formulation, the objective is usually to maximize the expected
profit or to minimize the expected cost over the two stages of
the capacity expansion project. Typically, the resulting objective
function is accounted using the expected net present value
or ENPV.

Answer 1

Introduction
介绍

Planning under uncertainty is a common class of problems
在不确定性下的规划是一种常见的类别的问题

found in process systems engineering.
发现在过程系统工程。

Some examples widely
一些例子广泛

found in the literature are capacity expansion, scheduling,
文学作品中发现的容量扩充、调度、

supply chain management, resource allocation, transportation,
供应链管理、资源分配、运输、

unit commitment, and product design problems.
郑金龙,和产品设计问题。

The first studies
第一个研究

on planning under uncertainty could be accredited to
在不确定性下的规划可以被委任为

Dantzig (1955) and Beale (1955), who proposed the two-stage
Dantzig(1955)和Beale(1955),提出了两阶段

stochastic models with recourse, which provide the mathematical
随机模型,提供了与追索权的数学

framework for this article.
这篇论文的框架。

The industrial importance of planning process capacity expansions
规划过程工业的重要性的产能扩张

under uncertainty has been widely recognized and
在不确定性下的已得到广泛认同

discussed by several researchers (Ahmed and Sahinidis, 2000b;
讨论和Sahinidis几位研究人员(艾哈迈德- 44。

Berman and Ganz, 1994; Eppen et al., 1989; Liu and Sahinidis,
甘兹,文及1994年成立;1989年成立;总裁苏达权等,Eppen刘和Sahinidis,

1996; Murphy et al., 1987; Sahinidis et al., 1989).
1996年,墨菲苏达权等,1987;Sahinidis苏达权等,1989年9月初版。

In the
在

majority of industrial applications, capacity expansion plans
大部分的工业应用,生产能力扩张计划

require considerable amount of capital investment over a longrange
需要相当数量的资金投资在一个longrange

time horizon.
时间的地平线。

Moreover, the inherent level of uncertainty
此外,固有水平的不确定性因素

in forecast demands, availabilities, prices, technology, capital,
据天气预报的要求,可供、价格、技术、资金、

markets, and competition make these decisions very challenging
市场竞争使这些决定,很有挑战性

and complex.
而复杂的。

Therefore, several approaches were proposed
因此,几种方法提出了建议

to formulate and solve this problem.
制定和解决这个问题。

They mainly differ in the
他们主要是不同的

way uncertainty is handled, the robustness of the plans, and
方法不确定性处理,具有较强的鲁棒性的计划,以及

their flexibility.
他们的灵活性。

This article follows the two-stage stochastic
下面的文章记录了两阶段随机

programming approach with discretization of the uncertainty
离散化的程序设计方法的不确定性

space by random sampling of the parameter probability distributions.
采用随机抽样的空间参数的概率分布。

In turn, the feasibility constraints for the problem are
反过来,可行性约束的问题

enforced for every scenario in a deterministic fashion (taking
执行任何情况在一个确定性的时装(取

recourse actions with an associated cost) such that the resulting
行动是美联社成本追索权)等所产生的

plan or design is feasible under every possible uncertainty
规划或设计在每一个可能的不确定性是可行的

realization.
实现。

A formal two-stage stochastic model for capacity planning in
一份正式的两阶段随机模型为容积规划

the process industry was presented by Liu and Sahinidis (1996)
提出了流程工业是刘先生,Sahinidis(1996年)

as an extension of the deterministic models developed by
在中国的一种扩展规划模型研制而成

Sahinidis et al.
Sahinidis孙俐。

(1989).
(1989)。

In the two-stage stochasti
在两阶段stochasti