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Grid tied renewable energy stations are more and more used. In countries where theelectric energy is far away to satisfy the energy demand, small energy suppliers arestarting up to generate more power in such a way to satisfy the demand.Electricity market could know some disturbances due to some problems. Theseproblems could be transient or permanent. In case of lack of electric energy from themain provider, small providers will essay to fill in the gap. In such cases where theproviders are limited energy feeders, they put constraints and conditions to supplypower. As examples, limited power, price and time, for each client. If there are moreclients, the providers schedule the loads to be fed separately. The loads may vary fromclient to another, some loads could be equal to energy of a provider.To overpass these constraints, a flexible job shop scheduling distribution system isconsidered to optimize the supply of different clients depending on their demands. Inorder to adapt to energy demands, suppliers need to quickly adjust switching loadsand production plans to meet demand . Therefore, the production mode of make-to-order (MTO) with multi-variety and small-batch demand is increasingly popular, andflexible job shop scheduling has become the focus of current research, especially forintelligent manufacturing systems with MTO requirements. Schedule powerdistribution is a timetable for both clients and providers. Resolving schedulingmethods require substantial and extensive complex mathematical knowledge. Sincejob shop scheduling problems fall into the class of NP-hard problems, they aredifficult to formulate and solve.In this paper, an approach to the resolution of the flexible job shop scheduling powerdistribution system is described. This approach combines the Ant system optimizationmeta-heuristic (AS) with local search methods, including tabu search. The results forthe scheduling problems show that the ant systems with local search meta-heuristiccan find optimal solutions for different problems that can be adapted to deal with theflexible job shop scheduling problem. To illustrate the effectiveness and performanceof the algorithm proposed in this paper, flexible job shop scheduling probleminstances based on probalistic data (price of kWh) have been selected to compute. Theresults show that the solutions obtained are generally acceptable and satisfactory.