In many organisations, Just-In-Time (JIT) implementation plays a significant role in minimizing their excessive costs, and increasing their efficiency. However, the risks accompanying JIT strategies are often overlooked and affect system processes disrupting the entire chain of supply. This paper proposes an inventory model that can simultaneously reduce costs and risks in JIT systems. This model is developed in order to ascertain an optimal ordering strategy for procuring raw materials by using multi-external suppliers and local backup supplier to reduce the total cost of the products, and at the same time to reduce the risks associated with JIT supply within production systems. The effectiveness of the developed model is tested using an example problem with inbuilt disruption. A comparison between the cost of using the JIT system and using the inventory system shows the superiority of the use of the inventory policy.

No abstract

Just-In-Time (JIT) as a lean manufacturing approach plays a significant role in minimising costs and performances of products and services supplied to the 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 mathematical model for simultaneously minimising the total cost of a final product and the potential risks related to these benefits. Specifically, it demonstrates the effectiveness of a genetic algorithm in optimising the JIT model developed in our previous paper. Genetic operators adopted to improve the genetic search algorithm are introduced and discussed. Experiments are carried out to evaluate the performance of the proposed algorithm using a simplified example. Comparison of four selection methods is done to define the best method that can be used in the proposed GA. The findings demonstrate the superiority of the proposed approach in the JIT system with focus on simultaneous cost-risk reduction.

No abstract

This paper addresses the problem of simultaneously minimising the total costs of the final product produced by systems adopting a JIT approach. A cost which incorporates both the cost of production processes and the cost arising from the many potential risks associated with any reduction. A robust genetic approach is proposed in order to optimise the novel mathematical model published in [1]. Genetic operators adopted to improve the genetic search algorithm are introduced and discussed. Experiments are conducted to evaluate the performance of the proposed algorithm and an illustrative example is given. A comparison of the genetic operators used is made by means of evaluating different rates, to define the most suitable rate of crossover and mutation. The findings illustrate the effectiveness of the proposed approach in the JIT system with focus on simultaneous cost-risk reduction.