Analyzing Discrete Event Simulation Models of Complex Manufacturing Systems: AComputational Complexity Approach

摘要

Discrete manufacturing process design optimization is a challenging problem forthe Air Force, due to the large number of manufacturing process design sequences that exist for a given part. This has forced researchers to develop heuristic strategies to address such design problems. This report summarizes the work done in developing a new general heuristic search strategy for discrete manufacturing process design optimization, called generalized hill climbing algorithms. Generalized hill climbing algorithms provide a unifying approach for addressing such problems, in particular, and intractable discrete optimization problems, in general. Heuristic strategies such as simulated annealing, threshold accepting, Monte Carlo search, local search, and tabu search (among others) are all formulated as particular generalized hill climbing algorithms. Computational results are reported with various generalized hill climbing algorithms applied to computer simulation models of discrete manufacturing process designs under study at the Materials Process Design Branch of Wright Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA). In particular, the design of an optimal manufacturing process for an integrated blade rotor part is studied, and computational results using various generalized hill climbing algorithm, applied to the manufacturing and production of the part, are reported.