One of the most complex construction projects is electrical substations. An electrical substation is an auxiliary station of an electricity generation, transmission and distribution system where voltage is transformed from high to low or the reverse using transformers. Construction of electrical substation includes civil works and electromechanical works. The scope of civil works includes construction of several buildings/components divided into parallel and overlapped working phases that require variety of resources and are generally quite costly and consume a considerable amount of time. Therefore, construction of substations faces complicated time-cost-resource optimization problems. On another hand, the construction industry is turning out to be progressively competitive throughout the years, whereby the need to persistently discover approaches to enhance construction performance. To address the previously stated afflictions, this dissertation makes the underlying strides and introduces a simulation and optimization model for the execution processes of civil works for an electrical substation based on database excel file for input data entry. The input data include bill of quantities, maximum available resources, production rates, unit cost of resources and indirect cost. The model is built on Anylogic software using discrete event simulation method. The model is divided into three zones working in parallel to each other. Each zone includes a group of buildings related to the same construction area. Each zone-model describes the execution process schedule for each building in the zone, the time consumed, percentage of utilization of equipment and manpower crews, amount of materials consumed and total direct and indirect cost. The model is then optimized to mainly minimize the project duration using parameter variation experiment and genetic algorithm java code implemented using Anylogic platform. The model used allocated resource parameters as decision variables and available resources as constraints. The model is verified on real case studies in Egypt and sensitivity analysis studies are incorporated. The model is also validated using a real case study and proves its efficiency by attaining a reduction in model time units between simulation and optimization experiments of 10.25% and reduction in total cost of 4.7%. Also, by comparing the optimization results by the actual data of the case study, the model attains a reduction in time and cost by 13.6% and 6.3% respectively. An analysis to determine the effect of each resource on reduction in cost is also presented
