A novel multi-objective co-evolutionary approach for supply chain gap analysis with consideration of uncertainties

Supply chain gap analysis is a practical method for quantitatively measuring the gap between the current state and a desired/ideal state in a supply chain, and generating a list of corrective actions to eliminate this gap and reach a desired/ideal level in supply chain goals. We propose a novel multi-objective co-evolutionary approach for supply chain gap analysis by hybridizing two well-known algorithms of non-dominated sorting geneticalgorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO). The proposed algorithm considers the best solution of NSGAII at each iteration and uses it as the initial population in MOPSO. We consider three objective functions, including the expected costs, the total time, and customer satisfaction. The house of quality and quality function deployment is used to transform customer requirements into product characteristics. We also use a response surface methodology with multi-objective decision making for tuning the parameters since metaheuristic methods are generally sensitive to input parameters. We finally generate several random problems with different scenarios to compare the performance of our hybrid approach with singular methods. Five performance measures (i.e., mean ideal distance, diversification metric, quality metric, data envelopment analysis, and hypervolume metric) are used for this comparison. The results show the hybrid approach proposed in this study outperforms singular NSGAII and MOPSO metaheuristics in most scenarios.