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

Author

Listed:
  • Mohammad Hossein Zavvar Sabegh

    (Kharazmi University)

  • Mohammad Mohammadi

    (Kharazmi University)

  • Bahman Naderi

    (Kharazmi University)

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.

Suggested Citation

  • Mohammad Hossein Zavvar Sabegh & Mohammad Mohammadi & Bahman Naderi, 2017. "Multi-objective optimization considering quality concepts in a green healthcare supply chain for natural disaster response: neural network approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1689-1703, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-017-0645-1
    DOI: 10.1007/s13198-017-0645-1
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    References listed on IDEAS

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    1. Tautenhain, Camila P.S. & Barbosa-Povoa, Ana Paula & Mota, Bruna & Nascimento, Mariá C.V., 2021. "An efficient Lagrangian-based heuristic to solve a multi-objective sustainable supply chain problem," European Journal of Operational Research, Elsevier, vol. 294(1), pages 70-90.
    2. Seyed Reza Abazari & Fariborz Jolai & Amir Aghsami, 2022. "Designing a humanitarian relief network considering governmental and non-governmental operations under uncertainty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1430-1452, June.
    3. Mohammed Alkahtani & Muhammad Omair & Qazi Salman Khalid & Ghulam Hussain & Imran Ahmad & Catalin Pruncu, 2021. "A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions," IJERPH, MDPI, vol. 18(4), pages 1-23, February.

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