| 15 | 2003 | M. A. Gamila & S. Motavalli [40] | mixed integer programming | Ø Minimizing completion time Ø Minimizing Material handling time Ø Minimizing total processing time | Results reported increased efficiency and performance of system | Computational results are compared with the previous findings |
| 16 | 2004 | T. Sawik [41] | Mixed integer programming | Ø Minimizing production time | Computational results reported better performance | Numerical examples and some computational results are compared with available literature |
| 17 | 2011 | M. I. Mgwatua [42] | Linear Mathematical Programming | Ø Maximizing throughput Ø Minimizing make span | More interactive decisions and well-balanced workload of the FMS can be achieved when sub-problems are solved jointly | Compared with results from previous literature |
| 18 | 2012 | A. M. Abazari, M. Solimanpur, & H. Sattari [43] | Linear mathematical programming | Ø Minimizing System unbalance | Genetic algorithm (GA) is proposed and performance of proposed GA is evaluated based on some benchmark problems | Performance is evaluated based on some benchmark problems adopted from the literature |
| b. Probabilistic | ||||||
| 3. Monte Carlo algorithms | ||||||
| 19 | 1998 | S. K. Mukhopadhyay et al. [44] | Simulated annealing (SA) approach | Ø Minimizing system imbalance | Tried to give global optimum solution | Computational results are compared with existing results |
| 20 | 2004 | R. Swarnkar & M. K. Tiwari [45] | Hybrid tabu search and simulated annealing based heuristic approach | Ø Minimizing system unbalance Ø Maximizing throughput | Results reported better performance | Tested on Standard problems and the results obtained are compared with those from some of the existing heuristics from literature |
| 21 | 2005 | M. M. Aldaihani & M. Savsar [46] | Stochastic model | Ø Minimizing total (FMC) flexible manufacturing cell cost per unit of production | Results reported better performance | Computational results were presented |
| 22 | 2006 | M. K. Tiwari, S. Kumar, S. Kumar, Prakash, & R. Shankar [47] | Constraints-Based Fast Simulated Annealing (SA) Algorithm | Ø Minimizing system unbalance Ø Maximizing throughput | Proposed algorithm enjoys the merits of simple SA and simple genetic algorithm | The application of the algorithm is tested on standard data sets |
| 23 | 2012 | M. Arıkan & S. Erol [48] | Hybrid simulated annealing-tabu search algorithm | Ø Maximizing weighted sum Ø Minimizing system unbalance Ø Balancing of workload | Results shows improved system performance compared to earlier results in literature | The results are compared with those developed earlier by the authors |
| 4. Evolutionary Computation (EC) | ||||||
| ü Evolutionary algorithms (EA) | ||||||
| 24 | 2000 | N. Kumar & K. Shanker [49] | Genetic algorithm (GA) | Ø Maximizing number of part types in a batch Ø Maximizing number of parts selected a batch Ø Maximizing mean machine utilization | Results reported reduced computational requirements | comparative study of Computational results |
| 25 | 2002 | H. Yong & Z. Wu [50] | GA-based integrated approach | Ø Balancing of workloads | Results shows that suggested approach perform better than existing | Computational results are compared with the previous findings |