
| α1                 | α2   | α3   | G       | x1      | x2      | x3    |
|--------------------|------|------|---------|---------|---------|-------|
| 1                  | 1    | *all | 86755.4 | 33422.5 | 53475.9 | 13369 |
| 1                  | 0.5  | all  | 86780.3 | 33422.5 | 53475.9 | 13369 |
| 0.5                | 1    | all  | 86767.5 | 33422.5 | 53475.9 | 13369 |
| 0.5                | 0.5  | all  | 86792.4 | 33422.5 | 53475.9 | 13369 |
| 0.3333             | 1    | all  | 86770.9 | 33422.5 | 53475.9 | 13369 |
| 0.3333             | 0.5  | all  | 86795.9 | 33422.5 | 53475.9 | 13369 |
| 0.25               | 1    | all  | 86772.5 | 33422.5 | 53475.9 | 13369 |
| 0.25               | 0.5  | all  | 86797.5 | 33422.5 | 53475.9 | 13369 |
| 0.2                | 1    | all  | 86773.5 | 33422.5 | 53475.9 | 13369 |
| 0.2                | 0.5  | all  | 86798.4 | 33422.5 | 53475.9 | 13369 |
| 0.1667             | 1    | all  | 86774.1 | 33422.5 | 53475.9 | 13369 |
| 0.1667             | 0.5  | all  | 86799.0 | 33422.5 | 53475.9 | 13369 |
| 0.1429             | 1    | all  | 86774.5 | 33422.5 | 53475.9 | 13369 |
| 0.1429             | 0.5  | all  | 86799.4 | 33422.5 | 53475.9 | 13369 |
| 0.125              | 1    | all  | 86774.8 | 33422.5 | 53475.9 | 13369 |
| 0.125              | 0.5  | all  | 86799.8 | 33422.5 | 53475.9 | 13369 |
| 0.1111             | 1    | all  | 86775.1 | 33422.5 | 53475.9 | 13369 |
| 0.1111             | 0.5  | all  | 86800.0 | 33422.5 | 53475.9 | 13369 |
| Note: *all∈ (0, 1) |      |      |         |         |         |       |

TABLE II ALPHA, OBJECTIVE FUNCTION
 AND DECISION VARIABLES FOR M=748 Table III summarizes the result for M = 748 to 751 for x1, x2 and x3 with maximum and minimum values of G. The overall maximum value for G is 86807.7 at M = 748 and the overall minimum value is 86408.0 at M = 751. 

TABLE III SUMMARY OF ITERATIONS, DECISION VARIABLES 
AND OBJECTIVE FUNCTION

| M   | x1      | x2      | x3      | G (max)   | G(min)   |
|-----|---------|---------|---------|-----------|----------|
| 748 | 33422.5 | 53475.9 | 13369   | 86807.7   | 86755.4  |
| 749 | 33377.8 | 53404.5 | 13351.1 | 86691.8   | 86639.5  |
| 750 | 33333.3 | 53333.3 | 13333.3 | 86576.2   | 86524.0  |
| 751 | 33288.9 | 53262.3 | 13315.6 | 86440.7   | 86408.0  |

Table IV compares the best objective function and decision variables x1, x2 and x3 of the proposed method with previous work by other researchers. 

Decision Variables 

Method 

The Best 

Objective 

Function x1 x2 x3 

Irfan [14] 64390.999 33825.16 40000.00 9374.760 Atanu [15] 66454.369 27766.99 40000.00 10233.01 Proposed Method 86807.700 33422.50 53475.90 13369.00 

TABLE IV COMPARATIVE ANALYSIS
From Table IV, the optimum value for the objective function using the proposed method outweighs the results obtained in [14] and [15]. It can be deduced that the recursive iteration method proposed here is an efficient and effective way to solve our example fuzzy problem of production planning in the textile industry. 

## V. Conclusion

This paper has discussed the use of fuzzy linear programming for solving a production planning problem in the textile industry. It can be concluded that the recursive method introduced is a promising method for solving such problems. The modified s-curve membership function provides various uncertainty levels which are very useful in the decision making process. In this paper, only a single s-curve membership function was considered. In the future, various other membership functions will be considered. Apart from providing an optimum solution for the objective functions, the proposed method ensures high productivity. In this regard, there is a good opportunity for developing an interactive selforganized decision making method by using hybrid soft computing techniques. 

## Acknowledgment

The authors would like to thank Universiti Teknologi PETRONAS and Swinburne University of Technology Sarawak Campus for supporting this work. 

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