Logit models are one of the most important discrete choice models and they play an important role in

describing decision makers’ choices among alternatives. In this paper the Multi-Nominal Logit models has been used

in mode choice modeling of Isfahan. Despite the availability of different mathematical computer programs there are

not so many programs available for estimating discrete choice models. Most of these programs use optimization

methods that may fail to optimize these models properly. Even when they do converge, there is no assurance that they

have found the global optimum, and it just might be a good approximation of the global minimum. In this research a

heuristic optimization algorithm, simulated annealing (S.A), has been tested for estimating the parameters of a Logit

model for a mode choice problem that had 17 parameters for the city of Isfahan and has been compared with the same

model calculated using GAUSS that uses common and conventional algorithms. Simulated annealing is and algorithm

capable of finding the global optimum and also it’s less likely to fail on difficult functions because it is a very robust

algorithm and by writing the computer program in MATLAB the estimation time has been decreased significantly. In

this paper, this problem has been briefly discussed and a new approach based on the simulated annealing algorithm

to solve that is discussed and also a new path for using this technique for estimating Nested Logit models is opened

for future research by the authors. For showing the advantages of this method over other methods explained above a

case study on the mode choice of Isfahan has been done.

This paper presents a novel approach to solve the double-track railway rescheduling problem, when an incident occurs into one of the block sections of the railway. The approach restricts the effects of an incident to a specific time, based on which the trains are divided into rescheduled and unchanged ones, so that the latter retain their original time-table after the incident. The main contribution of this approach is the simultaneous consideration of three rescheduling policies: cancelling, delaying and re-ordering. A mixed-integer optimization model is developed to find optimal conflict-free time-table compatible with the proposed approach. The objective function minimizes two cost parts: the cost of deviation from the primary time-table and the cost of train cancellation. The model is solved by CPLEX 11 software which automatically generates the optimal solution of a problem. Also, a meta-heuristic solution method based on simulated annealing algorithm is proposed for tackling the large-scale problems. The results of an experimental analysis on two double-track railways of the Iranian network show an appropriate capability of the model and solution method for handling the simultaneous train rescheduling. The results indicate that the proposed solution method can provide good solutions in much shorter time, compared with the time taken to solve the mathematical model by CPLEX software.