optimization through simulation is considered anefficient tool in dealing with optimization problems. it’s a tool that iscapable to cover real world problems to a much more complete way than the otheroptimization tools. the problem of this tool is the necessity of heavyprocessing computations. this problem is caused not only since the simulationprocess is time consuming, but the objective funct...

a simulation-based optimization approach for the purpose of finding a near-optimal answer can be efficient and effective. In the present paper, first, the mathematical model for the project activity scheduling problem has been presented with a job shop approach. Then, using the Arena 14 software, the simulation model has been designed. Consequently, a numerical example has been solved via runni...

For large and complex reacting systems, computational efficiency becomes a critical issue in process simulation, optimization and model-based control. Mechanism simplification is often a necessity to improve computational speed. We present a novel approach to simplification of reaction networks that formulates the model reduction problem as an optimization problem and solves it using geneti...

in this paper, we consider a stochastic time-cost tradeoff problem (tctp) in pert networks for project management, in which all activities are subjected to a linear cost function and assumed to be exponentially distributed. the aim of this problem is to maximize the project completion probability with a pre-known deadline to a predefined probability such that the required additional cost is min...

Simulation optimization (SO) is the process of finding the best set of input variable values without explicitly evaluating each feasible set of these input variable values given an output criterion (Law, 2007; Fu, 1994). Input variables are called (controllable) inputs, parameter settings, values, variables, (proposed) solutions, designs, configurations, or factors (in design of experiments ter...