This paper addresses the scheduling of machines, an Automated Guided Vehicle (AGV) and two robots in a Flexible Manufacturing System (FMS) formed in three loop layouts, with objectives to minimize the makespan, mean flow time and mean tardiness. The scheduling optimization is carried out using Sheep Flock Heredity Algorithm (SFHA) and Artificial Immune System (AIS) algorithm. AGV is used for carrying jobs between the Load/Unload station and the machines. The robots are used for loading and unloading the jobs in the machines, and also used for transferring jobs between the machines. The algorithms are applied for test problems taken from the literature and the results obtained using the two algorithms are compared. The results indicate that SFHA performs better than AIS for this problem.
Job Shop Scheduling Problem (JSSP) and Flow Shop Scheduling Problem (FSSP) are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA) is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R) heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.