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Question

In literature different approaches are proposed for the solution of the transport design problem. The approaches may be classified according to the decision variables (integer, continuous and mixed integer) and according to solution algorithms used (analytical or heuristic).
The public transport network design problem is non-convex (Newell) and the best and most useful solution methods are based on heuristic procedures. This approach,followed throughout the study, concerns the minimization of an objective function (OF),including all different possible impacts of transport, but usually is limited to a combination
of operator and user’s costs.In the last years, researchers have tested new approaches based on Artificial Neural Networks, Genetic Algorithms (GAs), Simulated Annealing or Tabu Search to solve optimization problems.1 Dip. Scienze dell’Ingegneria Civile, Università di Roma Tre, eciprian@uniroma3.it 2 DITS, Università di Roma “La Sapienza”, gaetano.fusco@uniroma1.it 3 Dip. Scienze dell’Ingegneria Civile, Università di Roma Tre, sgori@uniroma3.it 4 Dip. Scienze dell’Ingegneria Civile, Università di Roma Tre, labtras@uniroma3.it 682 E. Cipriani et al.
The utilization of powerful and probabilistic optimization techniques as GAs results particularly opportune for several specific aspects: the nonlinearity and the nonconvexity of the objective functions and the discrete nature of several variables.Xiong and Schneider show that GA may efficiently solve transport network design problem. Other authors developed GAs applications to solve specific public transport network design problems (routing, scheduling, etc.). Pattnaik et al implemented a two phases procedure. First, a set of feasible routes is generated and then a GA selects the optimum routes set. Also Dhingra et al proposed an application of GAs. The solution approach provides a double application of GAs to solve separately, the routing and the scheduling problem. Ngamchai and Lovell proposed a solution model based on the use of several genetic operators designed for this specific problem. Among the most remarkable works on this matter not using GAs, it is possible to mention Mandl, Filippi and Gori, Ceder and Israeli and Baaj and Mahmassani.

Answer 1

在文学不同的做法，提出了为解决交通设计问题。该办法可被归类根据这项决定的变数（整数，持续和混合整数） ，并据以解决算法（分析或启发式） 。
公共交通网络设计问题就是非凸（纽维尔）和最好和最有用的解决方法是基于启发式程序。这种做法，随后在整个研究中，涉及到最小化的目标函数（ ） ，其中包括所有不同的可能影响交通，但通常只限于组合
对经营者和使用者的costs.in过去的几年中，研究人员测试新的方法基于人工神经网络，遗传算法（气） ，模拟退火或禁忌搜索，以解决优化problems.1展拳脚。 scienze dell'ingegneria附带民事诉讼， università罗马tre ， eciprian@uniroma3.it二日署长， università迪罗姆人"萨皮恩扎" ， gaetano.fusco @ uniroma1.it三日展拳脚。 scienze dell'ingegneria附带民事诉讼， università罗马tre ， sgori@uniroma3.it四日展拳脚。 scienze dell'ingegneria附带民事诉讼， università罗马tre ， labtras@uniroma3.it 682体育cipriani等人。
利用强大和概率优化技术作为天然气的结果，特别是时机的几个具体方面：非线性和nonconvexity的目标函数和离散性的几个variables.xiong和Schneider表明，遗传算法，可有效地解决交通网络设计问题。其他作者开发的气体应用，以解决一些具体的公共交通网络设计问题（路由，调度等） 。 pattnaik等人实施了两个阶段的程序。首先，建立了一套行之有效的路线生成，然后加文选择最佳路线。也dhingra等人提出申请的天然气。解决办法提供了一个经济的双重应用天然气，以解决另外，路由和调度问题。 ngamchai和Lovell提出了一个解决方案模型的基础上，利用几种遗传操作设计，为这个具体的问题。其中最引人注目的作品就这个问题，不是用气，是有可能提mandl ， filippi和哥里， ceder和以色列及baaj和邀请。