Genetic optimization of fabric scheduling

To apply GAs in solving an industrial optimization problem, it is usually assumed that a potential solution to the problem may be represented as a set of variables. These variables ( genes ) are joined together to form a string of values 

In GAs, a fitness function is defined to measure the fitness of each individual chromosome so as to determine which will reproduce and survive into the next generation. Given a particular chromosome, the fitness depends on how well that individual solves a specific problem. Maximizing degree of satisfaction of the downstream production units is the prime scheduling objective in JIT production. 

The JIT fabric-cutting schedule can be optimized using GAs such that the overall degree of satisfaction. 

Let n denote the set of all feasible sequences. For a given sequence asU, the overall fitness is defined as 

To optimize a fuzzy fabric-cutting schedule by GAs, the operation procedure begins by randomly generating an initial population of integer strings in which each string represents a job processing sequence, as shown in Fig. 7.4. Evolution 

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