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.
Highlights
Minimising the total product cost (Paksoy and Chang, 2010)
The RCGA is run 10 times for each method to obtain the optimum solution for minimising the total cost of the final product and the risk effects in JIT systems in a short time
This study presented a genetic algorithm approach to solve the problem for the objective of a simultaneous cost-risk reduction in JIT systems
Summary
Minimising the total product cost (Paksoy and Chang, 2010). Optimisation is the process of adapting the Optimisation problems arise in case discrete choices inputs of a device and mathematical process to find must be made and solving them amounts to find an the minimum or maximum of the output. The key purpose of optimisation is that can be used for obtaining the optimum solutions to find the global optima (maximum or minimum) of a within a reasonable amount of time Many meta-heuristics optimisation Optimisation problems are used for acquiring good approaches have been applied in the supply chain component parameters to be set into activities by management field to solve engineering optimisation humans or machines (Malhotra et al, 2011). Issues (Wang and Wang, 2008) These approaches are Numerous industrial engineering design problems are associated with transportation/distribution networks very complex and intractable for conventional consider the location of the organisation, design of the optimisation techniques. Evolutionary Algorithms (EAs) network configuration and customer satisfaction by are population-based meta-heuristic optimisation. The evolution process initiates from a population of generated individuals and occurs in generations (Kannaiah et al, 2011)
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