26 | 2006 | A. Kumar, Prakash, M. K. Tiwari, R. Shankar, & A. Baveja [51] | Constraint based genetic algorithm (CBGA) | Ø Balancing machine processing time Ø Minimizing number of movements Ø Balancing of workload Ø Unbalancing of workload Ø Filling the tool magazines as densely as possible Ø Maximizing sum of operations priorities | The methodology developed here helps avoid getting trapped at local minima | The application of the algorithm is tested on standard data sets from available literature. |
27 | 2007 | A. Turkcan, M. S. Akturk, & R. H. Storer [52] | Genetic Algorithm (GA) | Ø Minimizing manufacturing cost Ø Total weighted tardiness | Approach improves CNC machine efficiency & responsiveness to customer due date requirements | compared with the performance of most commonly used approach in the literature |
28 | 2008 | V. Tyagi & A. Jain [53] | Genetic algorithm based methodology | Ø Minimizing system unbalance | For a given number of tool copies of each tool type tool loading is affected by the availability of flexible process plans | An illustrative example |
29 | 2012 | U. K. Yusof, R. Budiarto, & S. Deris [54] | Constraint-chromosome genetic algorithm | Ø Minimizing system unbalance Ø Maximizing throughput | Overall combined objective function increased by 3.60% from previous best result | tested on 10 sample problems available in the FMS literature and compared with existing solution methods |
ü Memetic (hybrid) Algorithms | ||||||
30 | 2000 | M. K. Tiwari & N. K. Vidyarthi [55] | Genetic Algorithm (GA) based (HA) Heuristic Approach | Ø Minimizing system unbalance Ø Maximizing throughput | Optimal solution to problem | Tested on ten sample problems and the computational results obtained have been compared with those of existing methods |
31 | 2009 | M. Yogeswaran, S. G. Ponnambalam, & M. K. Tiwari [56] | Hybrid genetic algorithm simulated annealing algorithm (GASAA) | Ø Minimising system unbalance Ø Maximising throughput | Results support better performance of GASA over algorithms reported in literature | results compared with reported in the literature |
32 | 2010 | S. K. Mandal, M. K. Pandey, & M. K. Tiwari [57] | Genetic algorithm simulated annealing Heuristics approach | Ø Minimizing breakdowns Ø Minimizing system unbalance Ø Minimizing make span Ø Maximizing throughput | Results incurred under breakdowns validate robustness of developed model for dynamic ambient of FMS | Compared with dataset from previous literature |
33 | 2012 | V. M Kumar, A. Murthy, & K Chandrashekara [58] | Meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization | Ø Minimizing system unbalance Ø Maximizing throughput | Model efficiency and performance of system is comparable with results compared to literature | Computational results are presented |
34 | 2012 | C. Basnet [59] | Hybrid genetic algorithm | Ø Minimizing system unbalance | Better solutions for system unbalance | Computational comparison between the genetic algorithm and previous algorithms is presented |
35 | 2012 | D. Kosucuoglu & U. Bilge [60] | Genetic algorithm based mathematical programming (GAMP) | Ø Minimizing total distance travelled by parts during production | GALP integration works successfully for this hard-to-solve problem | tested through extensive numerical experiments |
ü Swarm Optimization | ||||||
36 | 2007 | S. Biswas & S. S. Mahapatra [61] | Swarm Optimization Approach | Ø Minimizing system unbalance | Results reported improved system balance | compared with existing techniques for ten standard problems available in literature representing three different FMS scenarios |