Bagley College of Engineering Publications and Scholarship
The goal of this study is to recognise various factors for responsive SCs that affect supply risk and model their impact on SC design and operation. We propose a conceptual model for SC responsiveness that encompasses practices such as flexibility, agility, internal integration, and visibility. This conceptual model is utilised to build up a multi-objective, multi-period SC design and operation model. A heuristic algorithm is developed to find the supplier, product, period, and production rate for the numerical problem. The improved genetic algorithm (GA) produces solutions with more accuracy in considerably less time than a traditional GA. Finally, an approach to prioritise the objective functions is developed that allows managers to focus on specific objective functions more than optimum values. This approach provides risk-averse, responsiveness-oriented, cost-effective managers the capability to set priorities based on their policies.
Taylor & Francis
James Worth Bagley College of Engineering
Department of Industrial and Systems Engineering
supply chain, responsiveness, risk, genetic algorithm
Vahid Nooraie, Mahdi Fathi, Masoud Narenji, Mahour Mellat Mellat-Parast, Panagote M. Pardalos & P. M. Stanfield (2019) A multi-objective model for risk mitigating in supply chain design, International Journal of Production Research, https://doi.org/10.1080/00207543.2019.1633024