Timetabling problem is known as an NP-hard problem that centres around finding an optimized allocation of subjects onto a finite available number of slots and spaces. It is perhaps the most challenging issues looked by colleges around the globe. Every academic institution faces a problem when preparing courses and exam plans. There are many restrictions raised while preparing a timetable. This paper proposed a method based on the evolutionary algorithms to solve the constrained timetable problem, which helps to create theory as well as lab schedule for universities. A smart adaptive mutation scheme is used to speed up convergence and chromosome format is also problem specific. Here in this paper two algorithms are compared in respect of Timetabling problems. Using GA (Genetic Algorithm) and MA (Memetic algorithm), we optimised the output by selecting the best solution from the available options to present a comprehensive curriculum system.

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The Vehicle Routing Problem is an optimization problem with number of applications in the transportation field. In this paper, Vehicle Routing Problem (VRP) with uncertainty in customer demand is considered. Often in real world situation, the information on demand on each customer is not accurate enough. Here, in this article we are assuming that the customer demands in each time period are uncertain and are considered as fuzzy variables. This kind of problem is known as an NP-hard problem. The main purpose of this article is to give conceptual and practical idea on how fuzzy logic with meta heuristic algorithm can be implemented to improve the optimization of vehicle routing problem with uncertainty in customer demand. To solve this problem, a hybrid Meta heuristic algorithm (HMHA) including clustering is proposed, with the objective of determining an optimal route to minimize the total cost.

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In corporate and industries sectors, transportation plays an important role. Vogel Approximation Method (VAM) is a effectual method to find the initial basic feasible solution of transportation problems, but when there is a tie between highest penalty costs for rows and columns, we have to choose arbitrary which results we may get solution far from optimal solution also. In this paper, we have modified the steps of VAM to find the feasible solution. The proposed revised algorithm of Vogel Approximation Method (RA-VAM) gives feasible solution which is always nearest to the optimal solution. Three examples are also solved to illustrate RA-VAM.