Flexible Manufacturing Systems (FMSs) have evolved rapidly over the last decade. One important factor that influences the performance of FMS is scheduling. Scheduling in an FMS differs from that in a conventional job-shop because each operation of a job may be performed by any one of several machines. Most studies of scheduling in FMS in the past have assumed that each part has a unique process plan. However, in an actual manufacturing environment, it is possible for each part to have more than one process plan and each operation required of a part can be performed on alternative machines. In this thesis, the problem of scheduling parts in a General Flexible Manufacturing System (GFMS) in static environments is investigated when each part can have alternative process plans and each operation required of a part can be performed on alternative machines. The model and a solution algorithm have been developed for the problem. The mathematical model is formulated as the mixed-(binary) integer programming problem, and is proven NP-hard in the strong sense. An implicit-enumerative algorithm such as the branch-and-bound technique could not be used to find the optimal solution within a reasonable time even for a small-sized problem. The heuristic algorithm based on the concept known as tabu search is proposed to efficiently find the optimal/near-optimal solution for large-sized problems within a reasonable computational time. The steps involved with the heuristic algorithm and its application to an example problem are presented. Six different versions of tabu search-based heuristics (TSH 1-TSH 6) are compared to investigate the impact of using long-term memory and the use of fixed versus variable tabu-list sizes. A statistical experiment, based on randomized-block design, is carefully constructed to test the performance of the heuristics on 4 problem structures ranging from 4 parts to 14 parts. The results reveal that the tabu search-based heuristic with fixed tabu-list size and long-term memory (TSH 3) is preferred to other heuristics as the problem size increases. This research substantiates the fact that there is clearly a need for efficient heuristics to solve the complex scheduling problems in FMSs. One cannot rely on the implicit-enumerative procedure such as the branch-and-bound technique to solve problems that have practical significance.