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