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Creating of courses timetable is an extremely difficult, time-consuming task and usually takes a long time. In many educational institutions, the courses schedule is developed manually. Schedule theory includes problems that are actually less complex than problems in practice, but theoretical analysis provides a fundamental understanding of the complexity of the schedule. The logical result is that the schedule is very difficult to build in practice due to many constraints [1]. Scheduling courses is a planning problem. In 1996, the problem of scheduling was described as the allocation of some resources with restrictions on a limited number of time intervals and at the same time to satisfy the set of stated objectives [2]. This is a general statement and is a common description of the courses timetabling creation problem. Schedule of courses is an important administrative activity in most educational institutions. The timetable problem is the distribution of classes by available audiences and time intervals, taking into account the constraints. We usually distinguish between two types of constraints: hard and soft. Hard constraints are compulsorily fulfilled by the educational institution. Decisions that do not violate hard constraints are called possible solutions. With the development of the general theory of the schedule, the approaches to the formalization and solution of the courses timetabling creation problem in educational institutions also changed. Currently, the problem of automation of the courses timetabling creation remains relevant. The urgency of the problem is determined by the growing requirements for the quality of education, student work planning, rational use of the audiences, as well as taking into account additional optimization parameters. The task of finding the optimal schedule of courses in most cases belongs to the class of complex problems. If we take into account the real conditions, the problem is even more complicated, because the desired solutions must meet numerous constraints of production, organizational and psychophysiological nature, which contradict each other. The genetic algorithm helps to efficiently search for optimal solutions in spaces with a very large dimension.

Keuliling Reservoir which is located in Kuta Cot Glie, Aceh Besar is one of the reservoirs that have a function as a provider of water for irrigation needs, supports food self-sufficiency programs and others. The reservoir area has limited volume of water so, the most important water-saving irrigation water should be done to determine the scheduling of the distribution of irrigation water to the area of rice fields. The purpose of this research is to develop a method that can be used to the calculation of scheduling and can be applied to the irrigation of the Keuliling Reservoir watershed in Aceh Besar. In this study, the Genetic Algorithm method was used to determine the optimization of the Keuliling Reservoir in the Aceh Besar. From the analysis that has been done, by test of the Genetic Algorithm method, the minimum optimization of the time and discharge of irrigation water is obtained. While the visualization of these results can be displayed using the Matlab GUI software. The remaining disposal of irrigation water into the Keumireu watershed at one hour of irrigation has the minimum value of 600 Liters / Hour. The best irrigation time obtained is based on the calculation of 72 Genetic Algorithm methods in which the amount of water entering the rice field area is 1,038,211,600 Liters and 257,788,400 Wasted in the river. Based on the results of testing the application of the Keuliling Reservoir watershed irrigation scheduling in Aceh Besar that was built with Genetic Algorithms can solve optimal scheduling problems.

The coordination of strategic and tactical plans of industrial enterprises with their operating strategies significantly improves the production efficiency. The paper presents a methodological approach to the coordination of strategic and operational management for mass production of complex products with long production cycles based on the combination of optimization problems volume schedules, operational calendar and network planning. Usually for planning such productions is a network planning. However, finding the critical path does not solve the problem. When in network planning are resource limitations, there is a task for which there is still no efficient algorithm for its solution. Therefore, proposed the following task to solve by using a model that combines network and efficient scheduling. While approximately solved and the problem of accounting for the duration of works (operations) is not in integer units of time, and in the actual values. In General presents an original model of development plans for the production of complex products with a long process with regard to the approval of finished products and schedules production of parts, components products.