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Just-In-Time (JIT) as a lean manufacturing approach plays a significant role in minimising costs and performances of products and services supplied to t he global marketplace. However, there are many potential risks that cause significant disruptions to all supply chain members. This study proposes a genetic approach for optimising a novel mathematica l model for simultaneously minimising the total cost of a final product and the potential risks rel ated to these benefits. Specifically, it demonstrat es the effectiveness of a genetic algorithm in optimising the JIT model developed in our previous paper. Genetic operators adopted to improve the genetic se arch algorithm are introduced and discussed. Experiments are carried out to evaluate the perform ance of the proposed algorithm using a simplified example. Comparison of four selection methods is do ne to define the best method that can be used in the proposed GA. The findings demonstrate the super iority of the proposed approach in the JIT system with focus on simultaneous cost-risk reduction.

A GENETIC ALGORITHM FOR A SIMULTANEOUS OPTIMISATION OF COST-RISK REDUCTION UNDER A JUST-IN-TIME ADAPTION