1. A computer implemented method of generating and implementing an optimal training policy and an optimal promotion policy to accommodate an upcoming project, the computer implemented method comprising: a processor identifying new job positions that will be required for an upcoming project; the processor generating a skillset matrix that describes a current skill level of each worker in a work unit; the processor generating a promotion matrix that describes historical evaluations of how well each worker achieved predefined goals of past projects; the processor utilizing the skillset matrix to create an optimal training policy that identifies which workers are in need of additional training in order to qualify for one or more of the identified new job positions, wherein the optimal training policy is denoted as π t , and wherein the computer implemented method further comprises: the processor establishing the optimal training policy π t by mapping π t :s t →x t in order to maximize E[Σ t C(s t , x t )], where s t =(E t , I t , f t ) denotes a system state in time period t, E t is an efficiency matrix at a beginning of time period t, I t is a backlog information, f t contains job arrival information up to time period t, C(s t , x t ) denotes a cost function in time period t, and a system state process s t , and an amount of training x t evolves stochastically according to s t+1 =g(s t , x t , w t ), where w t is a random variable capturing a stochastic evolution of a system at time period t; the processor utilizing said promotion matrix to create an optimal promotion policy that identifies which workers will be candidates for being promoted into one or more of the identified new job positions; and the processor recommending the optimal training policy and the optimal promotion policy to management.