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Multi-objective optimization considering quality concepts in a green healthcare supply chain for natural disaster response: neural network approaches

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Abstract

This study proposes a new multi-objective mathematical model in pharmaceutical supply chain for natural disaster response considering quality, green concepts. The proposed model includes three objective functions. The first minimizes total manufacturing costs including production costs, purchasing costs, opening manufacturing plant costs, opening distribution centers costs, transportation costs and cost of poor quality (appraisal and prevention costs). The second minimizes environmental effects of products and transportations. The third maximizes humanitarian forces. Before disaster occurrence, to efficiently predict the objective functions values, we apply the back propagation (BP)—neural network, hybrid genetic algorithm (GA)—artificial neural network and particle swarm optimization (PSO). Finally, the effectiveness of the proposed solution shows the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology. The obtained results illustrate that the BP had high performance, which its R 2 was 0.99. Managerial implications of this research focus on improving the efficiency and effectiveness of the healthcare supply chain for natural disaster response: saving time, minimizing costs, minimizing environmental impact, utilizing resources more effectively (e.g. financial, human, technical, assets, transportation), showing social responsibility for communities affected by the disaster and continuously improving healthcare supply chain management.

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Correspondence to Mohammad Mohammadi.

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Zavvar Sabegh, M.H., Mohammadi, M. & Naderi, B. Multi-objective optimization considering quality concepts in a green healthcare supply chain for natural disaster response: neural network approaches. Int J Syst Assur Eng Manag 8 (Suppl 2), 1689–1703 (2017). https://doi.org/10.1007/s13198-017-0645-1

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