University course TIMETABLING problem is a challenging and time-consuming task on the overall structure of timetable in every academic environment. The problem deals with many factors such as the number of lessons, classes, teachers, students and working time, and these are influenced by some hard and soft constraints. The aim of solving this problem is to assign courses and classes to teachers and students, so that the restrictions are held. In this paper, a constraint programming method is proposed to satisfy maximum constraints and expectation, in order to address university TIMETABLING problem. For minimizing the penalty of soft constraints, a cost function is introduced and AHP method is used for calculating its coefficients. The proposed model is tested on department of management, University of Isfahan dataset using OPL on the IBM ILOG CPLEX Optimization Studio platform. A statistical analysis has been conducted and shows the performance of the proposed approach in satisfying all hard constraints and also the satisfying degree of the soft constraints is on maximum desirable level. The running time of the model is less than 20 minutes that is significantly better than the non-automated ones.

The paper discusses the current state of research concerning railway network TIMETABLING and traffic management. Timetable effectiveness is governed by frequency, regularity, accurate running, recovery and layover times, as well as minimal headway, buffer times and waiting times. Analytic (queuing) models and stochastic microsimulation are predominantly used for estimation of waiting times and capacity consumption anlong corridors and in stations, while combinatorial models and stability analysis are suitable for network timetable optimisation. Efficient traffic management can be achieved by real-time monitoring, fusion, analysis and rescheduling of railway traffic in case of disturbances. Real-time simulation, optimisation and impact evaluation of dispatching measures can improve the effectiveness of rescheduling and traffic management. The display of dynamic signal and track occupancy data in driver cabins, as Route Lint developed by ProRail, can support anticipative actions of the driver in order to reduce knock-on delays and increase throughput.

In the university TIMETABLING problem, necessity of considering variables corresponding to lessons, teachers, classes, days of the week and hours bring about a large scale mix integer linear programming problem. Usually the problem is so big that the exact mathematical programming solvers can not solve them in a small period of time. So variety of heuristic algorithms is proposed to solve such problems. In this paper we describe the problem and formulate it as a mix integer linear problem (MILP) then we decompose the problem into two smaller (MILP) that can be solved by exact solvers at the small period of time. Based on proposed model, we have made software for TIMETABLING the lessons at the university. This software is used for scheduling the lessons in mathematics faculty of Iranian university of science and technology at second semester of 1382-1383. The result is presented and compared with the traditional TIMETABLING.

University course TIMETABLING is a complex optimization problem. There are many components like departments, faculties, rooms, and students making the problem huge and difficult to solve. Each component enforces a set of normally conflicting constraints on the solution space. The problem will be solved if courses are scheduled in different rooms and within some specific time slots such that a set of constraints are satisfied. In this paper, a graph-based hyper-heuristic is proposed to find a solution to the problem. This is a two tiers modeling approach combining hyper heuristic with graph coloring technique. The upper tier heuristic is used to select a suitable heuristic to find a feasible solution on the lower tier. To find the suitability of the proposed approach, it has been applied to a real world case. The proposed approach was able to satisfy all the hard and soft constraints. Based on the research findings, it can be concluded that a graph-based hyper heuristic approach is a suitable and computationally efficient method to find a solution to university course TIMETABLING problem.