TRAVELING SALESMAN PROBLEM has numerous applications in sequencing and scheduling. Branch exchange heuristic is a quick algorithm which can result in a near optimal solution for symmetrical TRAVELING SALESMAN PROBLEM. In this paper an new method for selecting the branch is presented which saves the quality of solvings but results in a decrease of one- third in computational time compare with other branch exchange algorithms.

In this paper, we introduce a new approach for solving the TRAVELING SALESMAN PROBLEMs (TSP) and provide a solution algorithm for a variant of this PROBLEM. The concept of the proposed method is based on the Hungarian algorithm, which has been used to solve an assignment PROBLEM for reaching an optimal solution. We introduced a new fittest criterion for crossing over such PROBLEMs, and illustrated it with analytical examples and by computer programming. The proposed method builds on the initial solution of the TRAVELING SALESMAN PROBLEM (TSP) which is very simple, easy to understand and apply.

Click stream analysis is known as an effective method for customer’s viewing route prediction in a particular web site. Predicting Customer viewing behavior provides considerable advantages in different areas such as e-commerce, e-business and customer relationship management. This paper aims to provide a 0-1 mathematical model based on Markov models for evaluating the most probable viewing route of a customer in a website. This PROBLEM can be formulated as an especial case of well-known PRIZE COLLECTING TRAVELING SALESMAN PROBLEM (PCTSP) which is a NP-hard PROBLEM and its sub tour elimination constraints are increased drastically by increasing the model parameters.  Also an effective algorithm is introduced in this paper to solve this NP-hard model. For model validation, the proposed model was implemented by using the log files of a university web site server for 20 different users. Comparison of the results with commonly used Giudici algorithm shows that the proposed model yields better and exacter solutions.