الفهرس 
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Abstract
In all industrial sectors, the continuous struggle for increasing competitiveness calls for the ongoing improvement of the quality/cost ratio of the products offered to the market. This involves, among other factors, very close supervision of the production costs. However, factory managers often pay little attention to energy costs in general, and electricity costs in particular, because they are simply regarded as ”non-manageable”.
By applying electrical load management techniques industrial facilities could achieve cost saving in electrical energy consumption due to reducing of the peak demand. This achievement could be reached by optimally scheduling the electrical loads/processes. The goal of electrical load management is to maintain, as nearly as possible, a constant level of load.
The main objective of the current work is the construction ofload management model that capable of managing electrical loads in industrial facilities, while relaxing the limitations found in the published models. The proposed model has the objective of minimizing the total electrical cost by level the energy consumption and reduces the peak demand. The model consider electrical related constraints, production related constrains, precedence relationship between the production stages, and process flow constraints.
The proposed model is solved using LINGO® optimization package that uses an exact algorithm to solve different instances of the problem till optimality. The verification and testing of the model were done using four different test models. These models use hypothetical data which adjusted so that the output could be expected in advance. The load management model successfully dealt with different problem sizes.
Finally, load management model is applied to a real world case study for a metal forming factory. For this particular case, reducing of electrical energy consumption as well as electrical energy cost are important issues to the factory’s management. These could be achieved by applying the load management model in the factory. It works as decision support tool that help in determining the optimum operating plan for the machines in order to optimize electrical consumption and cost regarding production, sequential and process flow constraints. The model was successfully applied and the obtained objective function shows a reduction in the maximum electrical demand by 28% and a total electrical cost reduction of 20%.
The suggested model could be applied in other facilities especially when reduction of electrical consumption and cost are a must.