Production management and control of job shops is an important and complex problem. This dissertation proposes a two-level hierarchical method for tactical production planning in such environments. We first develop a scheme to design the planning hierarchy by aggregating parts into families and resources into manufacturing cells; a priori aggregation of elementary time periods into aggregate time periods is assumed. Based on the results of the design stage, the medium- and short- term production planning problems are formulated. The objective of the production planning problems consists of minimizing the holding costs for the work-in-process and finished goods inventory and the backlogging costs for unfulfilled demand. The formulations are complemented by capacity constraints, inventory state equations, and constraints that ensure feasibility of the aggregate production plan. We decompose the global production planning problem such that the short-term planning sub-problems can be computed in parallel. An efficient solution algorithm is developed to solve the optimization problems of the hierarchy. The algorithm has been shown to converge to near-optimal solutions in a finite number of iterations. Furthermore, the algorithm provides optimal solutions for a special case of the planning problem. The hierarchical approach is evaluated with respect to computational complexity, memory requirements, and the quality of the resulting production plans. The hierarchical approach is also tested on an industrial case and the resulting plan is compared with the one obtained from the company's Manufacturing Resource Planning system.