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Recall management in pharmaceutical industry through supply chain coordination

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Abstract

This paper is motivated by a real pharmaceutical supply chain (PSC) of Valsartan, facing two major concerns. First, implementing effective policies for product recall management due to disruption occurring in the manufacturing process, including impurity in raw material and packaging errors. Second, determining the optimal selling price and quality of raw material affecting the quality of the final compound, Valsartan, due to the effect of these decisions on Valsartan demand. In this paper, we analytically evaluate the impacts of production disruption, defective products recall, and decentralization among the members on the performance of the multi-echelon PSC. Moreover, for the first time, we address the context of channel coordination as a risk-mitigating strategy in case of disruption occurrence by proposing an altered revenue-sharing contract. Therefore, decisions are analyzed comparing decentralized, centralized, and coordinated decision-making structures under a range of scenarios. The results reveal that the proposed coordination scheme not only shares the incurred costs by disruption occurrence among all members but also improves their profitability compared to that of the decentralized structure, which leads to implementing effective recall management.

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Notes

  1. Pharmacy and Poisons Board House (2006), “Guidelines for Product Recall and Product Withdrawal” available at: http://www.apps.who.int/medicinedocs/documents/s17110e/s17110e.pdf. Accessed 27 August 2018).

References

  • Ali, S. M., Rahman, M. H., Tumpa, T. J., Rifat, A. A. M., & Paul, S. K. (2018). Examining price and service competition among retailers in a supply chain under potential demand disruption. Journal of Retailing and Consumer Services, 40, 40–47.

    Google Scholar 

  • Aljazzar, S. M., Jaber, M. Y., & Moussawi-Haidar, L. (2016). Coordination of a three-level supply chain (supplier–manufacturer–retailer) with permissible delay in payments. Applied Mathematical Modelling, 40(21–22), 9594–9614.

    Google Scholar 

  • Aslani, A., & Heydari, J. (2019). Transshipment contract for coordination of a green dual-channel supply chain under channel disruption. Journal of Cleaner Production, 223, 596–609.

    Google Scholar 

  • Awasthi, A., & Omrani, H. (2018). A goal-oriented approach based on fuzzy axiomatic design for sustainable mobility project selection. International Journal of Systems Science: Operations and Logistics, 6(1), 86–98.

    Google Scholar 

  • Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199–215.

    Google Scholar 

  • Bernstein, F., & Federgruen, A. (2007). Coordination mechanisms for supply chains under price and service competition. Manufacturing and Service Operations Management, 9(3), 242–262.

    Google Scholar 

  • Bertsimas, D., & Thiele, A. (2004). A robust optimization approach to supply chain management. In International conference on integer programming and combinatorial optimization (pp. 86–100). Berlin: Springer.

  • Branke, J., Farid, S. S., & Shah, N. (2016). Industry 4.0: A vision for personalized medicine supply chains? Cell and Gene Therapy Insights, 2, 263–270.

    Google Scholar 

  • Cachon, G. P. (2003). Supply chain coordination with contracts. Handbooks in Operations Research and Management Science, 11, 227–339.

    Google Scholar 

  • Cachon, G. P., & Lariviere, M. A. (2005). Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Management Science, 51(1), 30–44.

    Google Scholar 

  • Chakraborty, T., Chauhan, S. S., & Ouhimmou, M. (2019). Cost-sharing mechanism for product quality improvement in a supply chain under competition. International Journal of Production Economics, 208, 566–587.

    Google Scholar 

  • Chen, K., & Xiao, T. (2015). Outsourcing strategy and production disruption of supply chain with demand and capacity allocation uncertainties. International Journal of Production Economics, 170, 243–257.

    Google Scholar 

  • Chernonog, T., & Avinadav, T. (2017). Pricing and advertising in a supply chain of perishable products under asymmetric information. International Journal of Production Economics, 209, 249–264.

    Google Scholar 

  • Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Sciences, 38(1), 131–156.

    Google Scholar 

  • Ding, B. (2018). Pharma industry 4.0: Literature review and research opportunities in sustainable pharmaceutical supply chains. Process Safety and Environmental Protection, 119, 115–130.

    Google Scholar 

  • Duan, C., Deng, C., Gharaei, A., Wu, J., & Wang, B. (2018). Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions. International Journal of Production Research, 56(23), 7160–7178.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Sushil, T., & Singh, (2015). Building theory of sustainable manufacturing using total interpretive structural modelling. International Journal of Systems Science: Operations and Logistics, 2(4), 231–247.

    Google Scholar 

  • Ebrahimi, S., Hosseini-Motlagh, S.-M., & Nematollahi, M. (2017). Proposing a delay in payment contract for coordinating a two-echelon periodic review supply chain with stochastic promotional effort dependent demand. International Journal of Machine Learning and Cybernetics, 10, 1–14.

    Google Scholar 

  • Ebrahimi, S., Hosseini-Motlagh, S.-M., & Nematollahi, M. (2019). Proposing a delay in payment contract for coordinating a two-echelon periodic review supply chain with stochastic promotional effort dependent demand. International Journal of Machine Learning and Cybernetics, 10(5), 1037–1050.

    Google Scholar 

  • El Ouardighi, F., & Pasin, F. (2006). Quality improvement and goodwill accumulation in a dynamic duopoly. European Journal of Operational Research, 175(2), 1021–1032.

    Google Scholar 

  • Fessi, B. A., Benabdallah, S., Boudriga, N., & Hamdi, M. (2014). A multi-attribute decision model for intrusion response system. Information Sciences, 270, 237–254.

    Google Scholar 

  • Fleming, P., & Konstantaras, I. (2014). International Journal of Systems Science: Operations and Logistics. London: Taylor & Francis.

    Google Scholar 

  • Gharaei, A., Hoseini Shekarabi, S. A., & Karimi, M. (2019a). Modelling and optimal lot-sizing of the replenishments in constrained, multi-product and bi-objective EPQ models with defective products: Generalised cross decomposition. International Journal of Systems Science: Operations and Logistics, 2019, 1–13.

    Google Scholar 

  • Gharaei, A., Hoseini Shekarabi, S. A., Karimi, M., Pourjavad, E., & Amjadian, A. (2019b). An integrated stochastic EPQ model under quality and green policies: Generalised cross decomposition under the separability approach. International Journal of Systems Science: Operations and Logistics, 2019, 1–13.

    Google Scholar 

  • Gharaei, A., Karimi, M., & Hoseini Shekarabi, S. A. (2019c). Joint economic lot-sizing in multi-product multi-level integrated supply chains: Generalized benders decomposition. International Journal of Systems Science: Operations and Logistics, 2019, 1–17.

    Google Scholar 

  • Gharaei, A., Karimi, M., & Shekarabi, S. A. H. (2019d). An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: The outer approximation with equality relaxation and augmented penalty algorithm. Applied Mathematical Modelling, 69, 223–254.

    Google Scholar 

  • Giannoccaro, I., & Pontrandolfo, P. (2004). Supply chain coordination by revenue sharing contracts. International Journal of Production Economics, 89(2), 131–139.

    Google Scholar 

  • Giri, B., & Bardhan, S. (2014). Coordinating a supply chain with backup supplier through buyback contract under supply disruption and uncertain demand. International Journal of Systems Science: Operations and Logistics, 1(4), 193–204.

    Google Scholar 

  • Giri, B., & Masanta, M. (2018). Developing a closed-loop supply chain model with price and quality dependent demand and learning in production in a stochastic environment. International Journal of Systems Science: Operations and Logistics, 2018, 1–17.

    Google Scholar 

  • Giri, B., Roy, B., & Maiti, T. (2017). Coordinating a three-echelon supply chain under price and quality dependent demand with sub-supply chain and RFM strategies. Applied Mathematical Modelling, 52, 747–769.

    Google Scholar 

  • Giri, B., & Sarker, B. R. (2016). Coordinating a two-echelon supply chain under production disruption when retailers compete with price and service level. Operational Research, 16(1), 71–88.

    Google Scholar 

  • Govindan, K., Agarwal, V., Darbari, J. D., & Jha, P. (2019). An integrated decision making model for the selection of sustainable forward and reverse logistic providers. Annals of Operations Research, 273(1–2), 607–650.

    Google Scholar 

  • Hao, Y., Helo, P., & Shamsuzzoha, A. (2016). Virtual factory system design and implementation: Integrated sustainable manufacturing. International Journal of Systems Science: Operations and Logistics, 5(2), 116–132.

    Google Scholar 

  • Heydari, J. (2014). Supply chain coordination using time-based temporary price discounts. Computers and Industrial Engineering, 75, 96–101.

    Google Scholar 

  • Heydari, J., & Ghasemi, M. (2018). A revenue sharing contract for reverse supply chain coordination under stochastic quality of returned products and uncertain remanufacturing capacity. Journal of Cleaner Production, 197, 607–615.

    Google Scholar 

  • Hoseini Shekarabi, S. A., Gharaei, A., & Karimi, M. (2018). Modelling and optimal lot-sizing of integrated multi-level multi-wholesaler supply chains under the shortage and limited warehouse space: Generalised outer approximation. International Journal of Systems Science: Operations AND Logistics, 6(3), 237–257.

    Google Scholar 

  • Hosseini-Motlagh, S.-M., Nematollahi, M., & Nouri, M. (2018). Coordination of green quality and green warranty decisions in a two-echelon competitive supply chain with substitutable products. Journal of Cleaner Production, 196, 961–984.

    Google Scholar 

  • Huang, S., Yang, C., & Liu, H. (2013). Pricing and production decisions in a dual-channel supply chain when production costs are disrupted. Economic Modelling, 30, 521–538.

    Google Scholar 

  • Jazinaninejad, M., Seyedhosseini, S. M., Hosseini-Motlagh, S.-M., & Nematollahi, M. (2017). Coordinated decision-making on manufacturer’s EPQ-based and buyer’s period review inventory policies with stochastic price-sensitive demand: A credit option approach. RAIRO-Operations Research, 53, 1129–1154.

    Google Scholar 

  • Jazinaninejad, M., Seyedhosseini, S. M., Hosseini-Motlagh, S.-M., & Nematollahi, M. (2019). Coordinated decision-making on manufacturer’s EPQ-based and buyer’s period review inventory policies with stochastic price-sensitive demand: A credit option approach. RAIRO-Operations Research, 53(4), 1129–1154.

    Google Scholar 

  • Johari, M., & Hosseini-Motlagh, S.-M. (2019). Coordination of social welfare, collecting, recycling and pricing decisions in a competitive sustainable closed-loop supply chain: A case for lead-acid battery. Annals of Operations Research, 2019, 1–36.

    Google Scholar 

  • Johari, M., Hosseini-Motlagh, S.-M., & Nematollahi, M. (2017). Supply chain coordination using different modes of transportation considering stochastic price-dependent demand and periodic review replenishment policy. International Journal of Transportation Engineering, 5(2), 137–165.

    Google Scholar 

  • Johari, M., Hosseini-Motlagh, S.-M., Nematollahi, M., Goh, M., & Ignatius, J. (2018). Bi-level credit period coordination for periodic review inventory system with price-credit dependent demand under time value of money. Transportation Research Part E: Logistics and Transportation Review, 114, 270–291.

    Google Scholar 

  • Kanda, A., & Deshmukh, S. (2008). Supply chain coordination: Perspectives, empirical studies and research directions. International Journal of Production Economics, 115(2), 316–335.

    Google Scholar 

  • Kazemi, N., Abdul-Rashid, S. H., Ghazilla, R. A. R., Shekarian, E., & Zanoni, S. (2016). Economic order quantity models for items with imperfect quality and emission considerations. International Journal of Systems Science: Operations and Logistics, 5(2), 99–115.

    Google Scholar 

  • Ketchen, D. J., Jr., Wowak, K. D., & Craighead, C. W. (2014). Resource gaps and resource orchestration shortfalls in supply chain management: The case of product recalls. Journal of Supply Chain Management, 50(3), 6–15.

    Google Scholar 

  • Lee, C. H., Rhee, B.-D., & Cheng, T. (2013). Quality uncertainty and quality-compensation contract for supply chain coordination. European Journal of Operational Research, 228(3), 582–591.

    Google Scholar 

  • Li, W., Chen, J., & Chen, B. (2018). Supply chain coordination with customer returns and retailer’s store brand product. International Journal of Production Economics, 203, 69–82.

    Google Scholar 

  • Li, S., Zhu, Z., & Huang, L. (2009). Supply chain coordination and decision making under consignment contract with revenue sharing. International Journal of Production Economics, 120(1), 88–99.

    Google Scholar 

  • Lin, C., Chow, W. S., Madu, C. N., Kuei, C.-H., & Yu, P. P. (2005). A structural equation model of supply chain quality management and organizational performance. International Journal of Production Economics, 96(3), 355–365.

    Google Scholar 

  • Liu, P., & Yi, S.-P. (2018). Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era. Annals of Operations Research, 270(1–2), 255–271.

    Google Scholar 

  • Mizgier, K. J. (2017). Global sensitivity analysis and aggregation of risk in multi-product supply chain networks. International Journal of Production Research, 55(1), 130–144.

    Google Scholar 

  • Mizgier, K. J., Hora, M., Wagner, S. M., & Jüttner, M. P. (2015). Managing operational disruptions through capital adequacy and process improvement. European Journal of Operational Research, 245(1), 320–332.

    Google Scholar 

  • Mohammadzadeh, N., & Zegordi, S. H. (2016). Coordination in a triple sourcing supply chain using a cooperative mechanism under disruption. Computers and Industrial Engineering, 101, 194–215.

    Google Scholar 

  • Nematollahi, M., Hosseini-Motlagh, S.-M., & Heydari, J. (2017a). Coordination of social responsibility and order quantity in a two-echelon supply chain: A collaborative decision-making perspective. International Journal of Production Economics, 184, 107–121.

    Google Scholar 

  • Nematollahi, M., Hosseini-Motlagh, S.-M., & Heydari, J. (2017b). Economic and social collaborative decision-making on visit interval and service level in a two-echelon pharmaceutical supply chain. Journal of Cleaner Production, 142, 3956–3969.

    Google Scholar 

  • Nematollahi, M., Hosseini-Motlagh, S.-M., Ignatius, J., Goh, M., & Nia, M. S. (2018). Coordinating a socially responsible pharmaceutical supply chain under periodic review replenishment policies. Journal of Cleaner Production, 172, 2876–2891.

    Google Scholar 

  • Paszko, C. (2018). Quality assurance: Laboratory information management systems. London: Elsevier.

    Google Scholar 

  • Qi, X., Bard, J. F., & Yu, G. (2004). Supply chain coordination with demand disruptions. Omega, 32(4), 301–312.

    Google Scholar 

  • Rabbani, M., Foroozesh, N., Mousavi, S. M., & Farrokhi-Asl, H. (2017). Sustainable supplier selection by a new decision model based on interval-valued fuzzy sets and possibilistic statistical reference point systems under uncertainty. International Journal of Systems Science: Operations and Logistics, 6(2), 162–178.

    Google Scholar 

  • Rabbani, M., Hosseini-Mokhallesun, S. A. A., Ordibazar, A. H., & Farrokhi-Asl, H. (2018). A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design. International Journal of Systems Science: Operations and Logistics, 7(1), 60–75.

    Google Scholar 

  • Rahmani, K., & Yavari, M. (2019). Pricing policies for a dual-channel green supply chain under demand disruptions. Computers and Industrial Engineering, 127, 493–510.

    Google Scholar 

  • Rossetti, C. L., Handfield, R., & Dooley, K. J. (2011). Forces, trends, and decisions in pharmaceutical supply chain management. International Journal of Physical Distribution and Logistics Management, 41(6), 601–622.

    Google Scholar 

  • Sarathi, G. P., Sarmah, S., & Jenamani, M. (2014). An integrated revenue sharing and quantity discounts contract for coordinating a supply chain dealing with short life-cycle products. Applied Mathematical Modelling, 38(15–16), 4120–4136.

    Google Scholar 

  • Sarkar, S., & Giri, B. (2018). Stochastic supply chain model with imperfect production and controllable defective rate. International Journal of Systems Science: Operations and Logistics, 2018, 1–14.

    Google Scholar 

  • Sayyadi, R., & Awasthi, A. (2016). A simulation-based optimisation approach for identifying key determinants for sustainable transportation planning. International Journal of Systems Science: Operations and Logistics, 5(2), 161–174.

    Google Scholar 

  • Sayyadi, R., & Awasthi, A. (2018). An integrated approach based on system dynamics and ANP for evaluating sustainable transportation policies. International Journal of Systems Science: Operations and Logistics, 2018, 1–10.

    Google Scholar 

  • Seyedhosseini, S. M., Hosseini-Motlagh, S.-M., Johari, M., & Jazinaninejad, M. (2019). Social price-sensitivity of demand for competitive supply chain coordination. Computers and Industrial Engineering, 135, 1103–1126.

    Google Scholar 

  • Shah, N. H., Chaudhari, U., & Cárdenas-Barrón, L. E. (2018). Integrating credit and replenishment policies for deteriorating items under quadratic demand in a three echelon supply chain. International Journal of Systems Science: Operations and Logistics, 7(1), 34–45.

    Google Scholar 

  • Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9(1), 33–45.

    Google Scholar 

  • Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52(5), 639–657.

    Google Scholar 

  • Tsao, Y.-C. (2015). Design of a carbon-efficient supply-chain network under trade credits. International Journal of Systems Science: Operations and Logistics, 2(3), 177–186.

    Google Scholar 

  • Viegas, C. V., Bond, A., Vaz, C. R., & Bertolo, R. J. (2019). Reverse flows within the pharmaceutical supply chain: A classificatory review from the perspective of end-of-use and end-of-life medicines. Journal of Cleaner Production, 238, 117719.

    Google Scholar 

  • Wagner, S. M., Mizgier, K. J., & Arnez, P. (2014). Disruptions in tightly coupled supply chain networks: The case of the US offshore oil industry. Production Planning and Control, 25(6), 494–508.

    Google Scholar 

  • Weraikat, D., Zanjani, M. K., & Lehoux, N. (2016a). Coordinating a green reverse supply chain in pharmaceutical sector by negotiation. Computers and Industrial Engineering, 93, 67–77.

    Google Scholar 

  • Weraikat, D., Zanjani, M. K., & Lehoux, N. (2016b). Two-echelon pharmaceutical reverse supply chain coordination with customers incentives. International Journal of Production Economics, 176, 41–52.

    Google Scholar 

  • Xiao, T., & Qi, X. (2008). Price competition, cost and demand disruptions and coordination of a supply chain with one manufacturer and two competing retailers. Omega, 36(5), 741–753.

    Google Scholar 

  • Yan, B., Wu, X.-H., Ye, B., & Zhang, Y.-W. (2017). Three-level supply chain coordination of fresh agricultural products in the Internet of Things. Industrial Management and Data Systems, 117(9), 1842–1865.

    Google Scholar 

  • Yin, S., Nishi, T., & Zhang, G. (2016). A game theoretic model for coordination of single manufacturer and multiple suppliers with quality variations under uncertain demands. International Journal of Systems Science: Operations and Logistics, 3(2), 79–91.

    Google Scholar 

  • Zhang, W.-G., Zhang, Q., Mizgier, K. J., & Zhang, Y. (2017). Integrating the customers’ perceived risks and benefits into the triple-channel retailing. International Journal of Production Research, 55(22), 6676–6690.

    Google Scholar 

  • Zhao, X., Li, Y., & Flynn, B. B. (2013). The financial impact of product recall announcements in China. International Journal of Production Economics, 142(1), 115–123.

    Google Scholar 

  • Zhao, S., & Zhu, Q. (2017). Remanufacturing supply chain coordination under the stochastic remanufacturability rate and the random demand. Annals of Operations Research, 257(1–2), 661–695.

    Google Scholar 

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Appendices

Appendix A

$$ \frac{{\partial q_{M} \left( {q_{A} } \right)}}{{\partial q_{A} }} = \frac{{2\theta q_{A}^{\theta - 1} }}{{\left( {1 + q_{A}^{\theta } } \right)^{2} }} $$
(47)
$$ \frac{{\partial^{2} q_{M} \left( {q_{A} } \right)}}{{\partial \left( {q_{A} } \right)^{2} }} = \frac{{2\theta \left( {\left( {\theta - 1} \right)q_{A}^{\theta - 2} } \right)}}{{\left( {1 + q_{A}^{\theta } } \right)^{2} }} - \frac{{\left( {2\theta q_{A}^{\theta - 1} } \right)^{2} }}{{\left( {1 + q_{A}^{\theta } } \right)^{3} }} $$
(48)

It can be inferred that if \( (\theta > 1) \), the function \( q_{M} \left( {q_{A} } \right) \) is convex and if \( (0 < \theta \le 1) \) the function \( q_{M} \left( {q_{A} } \right) \) is concave. This means that the product’s quality increases with a decreasing rate if \( (0 < \theta \le 1) \) and with an increasing rate if \( (\theta > 1) \).

Appendix B

$$ \frac{{\partial E(\pi_{A} \left( {q_{A} )} \right)^{dec} }}{{\partial q_{A} }} = \left[ { - P_{A} \left( {RP} \right)_{A} + \left( {w_{A} - c_{A} } \right)} \right]\left[ {\frac{{3\gamma q_{A}^{2\theta - 1} }}{{\left( {1 + q_{A}^{\theta } } \right)^{2} }}} \right] - \vartheta_{1} q_{A} + g_{A} $$
(49)
$$ \frac{{\partial^{2} E(\pi_{A} \left( {q_{A} } \right))^{dec} }}{{\partial q_{A}^{2} }} = \left[ { - P_{A} \left( {RP} \right)_{A} + \left( {w_{A} - c_{A} } \right)} \right]\left[ {\frac{{3\gamma \left( {2\theta - 1} \right)q_{A}^{2\theta - 2} \left( {1 + q_{A}^{\theta } } \right) - 2q_{A}^{\theta - 1} }}{{\left( {1 + q_{A}^{\theta } } \right)^{3} }}} \right] - \vartheta_{1} \le 0 $$
(50)

It can be concluded that if \( \left( {\theta \le 0.5} \right) \) then \( \frac{{\partial^{2} E(\pi_{A} \left( {q_{A} } \right))^{dec} }}{{\partial q_{A}^{2} }} \) is negative and \( E(\pi_{A} \left( {q_{A} )} \right)^{dec} \) is concave with respect to \( \left( {q_{A} } \right) \). In our investigated case, the above-mentioned condition is satisfied.

Appendix C

Proof of Proposition 12

From Eq. (37) we derive: \( \frac{\partial p}{\partial \alpha } = \frac{1}{2\beta } + \frac{{\partial q_{A} }}{\partial \alpha }\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). On the other hand, from Eq. (36) we have: \( \frac{{\partial q_{A} }}{\partial \alpha }{\text{A}} = \frac{\partial p}{\partial \alpha } \), where \( A = \frac{{\left( {B + \frac{{g_{M} }}{\gamma } - \frac{{2q_{A}^{cen^{\theta} } \vartheta_{2} }}{{\gamma \left( {1 + q_{A}^{cen^{\theta} } } \right)}}} \right)\left( {1 - \theta + \frac{{2\theta q_{A}^{cen^{\theta} } }}{{1 + q_{A}^{cen^{\theta} } }}} \right)}}{{q_{A}^{cen} }} + 2\frac{{\vartheta_{2} \theta }}{\gamma }\frac{{q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} + \frac{{\vartheta_{1} \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{\gamma 2\theta q_{A}^{cen^{\theta - 1}} }} \) and \( B = p - c_{A} - P_{A} \left( {RP} \right)_{A} - P_{M} \left( {RP} \right)_{M} - P_{A} d - P_{A} S_{RA} - P_{M} S_{RM} - c_{M} - S_{D} \). Note that \( A \) and \( B \) are positive when \( p \) is sufficiently high. Therefore, we obtain \( \frac{\partial p}{\partial \alpha } = \frac{{A\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{{\partial q_{A} }}{\partial \alpha } = \frac{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are positive if \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) holds. Similarly, \( \frac{\partial p}{\partial \beta } = - \frac{\alpha }{{2\beta^{2} }} - \frac{{\gamma q_{A}^{cen^{\theta} } }}{{\beta^{2} \left( {1 + q_{A}^{cen^{\theta} } } \right)}} + \frac{{\partial q_{A} }}{\partial \beta }\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) and \( \frac{{\partial q_{A} }}{\partial \beta }{\text{A}} = \frac{\partial p}{\partial \beta } \) are derived from Eqs. (37) and (36), respectively. Accordingly, we have \( \frac{\partial p}{\partial \beta } = - \left( {\frac{\alpha }{{2\beta^{2} }} + \gamma \frac{{q_{A}^{cen^{\theta} } }}{{\beta^{2} \left( {1 + q_{A}^{cen^{\theta} } } \right)}}} \right)\frac{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{{\partial q_{A} }}{\partial \beta } = - \left( {\frac{\alpha }{{2\beta^{2} }} + \gamma \frac{{q_{A}^{cen^{\theta} } }}{{\beta^{2} \left( {1 + q_{A}^{cen^{\theta} } } \right)}}} \right)\frac{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are negative considering \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). Moreover, from the decentralized system we have (see Proposition 7): \( \frac{\partial p}{\partial \beta } = - \frac{{\alpha + \gamma \left( {1 - \frac{{1 - q_{A}^{dec^\theta } }}{{1 + q_{A}^{dec^\theta } }}} \right)}}{{2\beta^{2} }} \), which is lower than that of the centralized model, since \( \frac{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} }} < 1 \) and thus by increasing \( \beta \) the reduction rate of \( p \) decreases, after centralization. The proof is complete.□

Proof of Proposition 13

From Eq. (37) we have: \( \frac{\partial p}{\partial \gamma } = \frac{{q_{A}^{\text{cen}^{\theta} } }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)}} + \frac{{\partial q_{A} }}{\partial \gamma }\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). On the other hand, from Eq. (36) we derive: \( A\frac{{\partial q_{A} }}{\partial \gamma } - \frac{B}{\gamma } = \frac{\partial p}{\partial \gamma } \). Therefore, we obtain \( \frac{\partial p}{\partial \gamma } = \frac{{q_{A}^{cen^{\theta} } A\left( {1 + q_{A}^{cen^{\theta} } } \right) + \theta q_{A}^{cen^{\theta - 1}} B}}{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{{\partial q_{A} }}{\partial \gamma } = \frac{{q_{A}^{cen^{\theta} } \left( {1 + q_{A}^{cen^{\theta} } } \right) + \frac{B\theta }{A}q_{A}^{cen^{\theta - 1}} }}{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} }} + \frac{B}{\gamma A} \), which are positive considering \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). The proof is complete.□

Proof of Proposition 14

From Eq. (37) we derive: \( \frac{\partial p}{{\partial S_{D} }} = \frac{1}{2} + \frac{{\partial q_{A} }}{{\partial S_{D} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \), \( \frac{\partial p}{{\partial c_{A} }} = \frac{1}{2} + \frac{{\partial q_{A} }}{{\partial c_{A} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \), and \( \frac{\partial p}{{\partial c_{M} }} = \frac{1}{2} + \frac{{\partial q_{A} }}{{\partial c_{M} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). Also, \( \frac{\partial p}{{\partial S_{D} }} = A\frac{{\partial q_{A} }}{{\partial S_{D} }} + 1 \), \( \frac{\partial p}{{\partial c_{A} }} = A\frac{{\partial q_{A} }}{{\partial c_{A} }} + 1 \), and \( \frac{\partial p}{{\partial c_{M} }} = A\frac{{\partial q_{A} }}{{\partial c_{M} }} + 1 \) are derived from Eq. (36). Accordingly, we calculate \( \frac{\partial p}{{\partial S_{D} }} = \frac{\partial p}{{\partial c_{A} }} = \frac{\partial p}{{\partial c_{M} }} = \frac{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are positive if \( \frac{A\beta }{2\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) holds; and \( \frac{{\partial q_{A} }}{{\partial S_{D} }} = \frac{{\partial q_{A} }}{{\partial c_{A} }} = \frac{{\partial q_{A} }}{{\partial c_{M} }} = \frac{{ - \beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are negative if \( \frac{A\beta }{2\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) holds. The proof is complete.□

Proof of Proposition 15

From Eq. (37) we derive: \( \frac{\partial p}{\partial d} = \frac{{P_{A} }}{2} + \frac{{\partial q_{A} }}{\partial d}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \), \( \frac{\partial p}{{\partial S_{RA} }} = \frac{{P_{A} }}{2} + \frac{{\partial q_{A} }}{{\partial S_{RA} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \), \( \frac{\partial p}{{\partial S_{RM} }} = \frac{{P_{M} }}{2} + \frac{{\partial q_{A} }}{{\partial S_{RM} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \), \( \frac{\partial p}{{\partial \left( {RP} \right)_{A} }} = \frac{{P_{A} }}{2} + \frac{{\partial q_{A} }}{{\partial \left( {RP} \right)_{A} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \), and \( \frac{\partial p}{{\partial \left( {RP} \right)_{M} }} = \frac{{P_{M} }}{2} + \frac{{\partial q_{A} }}{{\partial \left( {RP} \right)_{M} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). Additionally, according to Eq. (36) we have: \( \frac{\partial p}{\partial d} = A\frac{{\partial q_{A} }}{\partial d} + P_{A} \), \( \frac{\partial p}{{\partial S_{RA} }} = A\frac{{\partial q_{A} }}{{\partial S_{RA} }} + P_{A} \), \( \frac{\partial p}{{\partial \left( {RP} \right)_{A} }} = A\frac{{\partial q_{A} }}{{\partial \left( {RP} \right)_{A} }} + P_{A} \), \( \frac{\partial p}{{\partial S_{RM} }} = A\frac{{\partial q_{A} }}{{\partial S_{RM} }} + P_{M} \), and \( \frac{\partial p}{{\partial \left( {RP} \right)_{M} }} = A\frac{{\partial q_{A} }}{{\partial \left( {RP} \right)_{M} }} + P_{M} \). Therefore, we calculate \( \frac{\partial p}{\partial d} = \frac{\partial p}{{\partial S_{RA} }} = \frac{\partial p}{{\partial \left( {RP} \right)_{A} }} = P_{A} \frac{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{\partial p}{{\partial S_{RM} }} = \frac{\partial p}{{\partial \left( {RP} \right)_{M} }} = P_{M} \frac{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are positive when \( \frac{A\beta }{2\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) holds. Similarly, we derive: \( \frac{{\partial q_{A} }}{\partial d} = \frac{{\partial q_{A} }}{{\partial S_{RA} }} = \frac{{\partial q_{A} }}{{\partial \left( {RP} \right)_{A} }} = - \frac{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} P_{A} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{{\partial q_{A} }}{{\partial S_{RM} }} = \frac{{\partial q_{A} }}{{\partial \left( {RP} \right)_{M} }} = - \frac{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} P_{M} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are negative when \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) holds. Moreover, under the decentralized model we have (see Proposition 8): \( \frac{\partial p}{{\partial S_{D} }} = \frac{1}{2} \), \( \frac{\partial p}{{\partial S_{RA} }} = \frac{{P_{A} }}{2} \), and \( \frac{\partial p}{{\partial S_{RM} }} = \frac{{P_{M} }}{2} \), which are higher than those of the centralized system considering \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). The proof is complete.□

Proof of Proposition 16

\( \frac{\partial p}{{\partial P_{M} }} = \frac{1}{2}\left( {\left( {RP} \right)_{M} + S_{RM} } \right) + \frac{{\partial q_{A} }}{{\partial P_{M} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) and \( \frac{\partial p}{{\partial P_{A} }} = \frac{1}{2}\left( {\left( {RP} \right)_{A} + S_{RA} + d} \right) + \frac{{\partial q_{A} }}{{\partial P_{A} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) are derived from Eq. (37). On the other hand, from Eq. (36) we have: \( \frac{\partial p}{{\partial P_{A} }} = A\frac{{\partial q_{A} }}{{\partial P_{A} }} + \left( {RP} \right)_{A} + S_{RA} + d \) and \( \frac{\partial p}{{\partial P_{M} }} = A\frac{{\partial q_{A} }}{{\partial P_{M} }} + \left( {RP} \right)_{M} + S_{RM} \). Hence, we obtain \( \frac{\partial p}{{\partial P_{M} }} = \left( {\left( {RP} \right)_{M} + S_{RM} } \right)\frac{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{\partial p}{{\partial P_{A} }} = \left( {\left( {RP} \right)_{A} + S_{RA} + d} \right)\frac{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are positive if \( \frac{A\beta }{2\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) holds. Similarly, we have: \( \frac{{\partial q_{A} }}{{\partial P_{M} }} = - \frac{{\left( {\left( {RP} \right)_{M} + S_{RM} } \right)\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{{\partial q_{A} }}{{\partial P_{A} }} = - \frac{{\left( {\left( {RP} \right)_{A} + S_{RA} + d} \right)\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \), which are negative if \( \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} < \frac{A\beta }{\gamma } \) holds. The proof is complete.□

Proof of Proposition 17

\( \frac{\partial p}{{\partial g_{M} }} = \frac{{\partial q_{A} }}{{\partial g_{M} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) and \( \frac{{\partial q_{A} }}{{\partial g_{M} }}A - \frac{1}{\gamma } = \frac{\partial p}{{\partial g_{M} }} \) are derived from Eqs. (37) and (36), respectively. Therefore, we obtain \( \frac{\partial p}{{\partial g_{M} }} = \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{{\partial q_{A} }}{{\partial g_{M} }} = \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{A\left( {A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} } \right)}} + \frac{1}{\gamma A} \), which are positive considering \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). Besides, using a similar method we have \( \frac{\partial p}{{\partial g_{A} }} = \frac{{\partial q_{A} }}{{\partial g_{A} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) and \( A\frac{{\partial q_{A} }}{{\partial g_{A} }} - \frac{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{\gamma 2\theta q_{A}^{cen^{\theta - 1}} }} = \frac{\partial p}{{\partial g_{A} }} \), which gives \( \frac{\partial p}{{\partial g_{A} }} = \frac{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} > 0 \) and \( \frac{{\partial q_{A} }}{{\partial g_{A} }} = \frac{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\left( {A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - \gamma \theta q_{A}^{cen^{\theta - 1}} } \right)}} + \frac{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{A\gamma 2\theta q_{A}^{cen^{\theta - 1}} }} > 0 \). The proof is complete.□

Proof of Proposition 18

\( \frac{\partial p}{{\partial \vartheta_{1} }} = \frac{{\partial q_{A} }}{{\partial \vartheta_{1} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) and \( \frac{\partial p}{{\partial \vartheta_{2} }} = \frac{{\partial q_{A} }}{{\partial \vartheta_{2} }}\frac{{\gamma \theta q_{A}^{cen^{\theta - 1}} }}{{\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) are derived from Eq. (37). Also, from Eq. (36) we have: \( A\frac{{\partial q_{A} }}{{\partial \vartheta_{1} }} + \frac{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2\gamma \theta q_{A}^{cen^{\theta} - 2} }} = \frac{\partial p}{{\partial \vartheta_{1} }} \) and \( A\frac{{\partial q_{A} }}{{\partial \vartheta_{2} }} + \frac{{2q_{A}^{cen^{\theta} } }}{{\gamma \left( {1 + q_{A}^{cen^{\theta} } } \right)}} = \frac{\partial p}{{\partial \vartheta_{2} }} \). Accordingly, we calculate \( \frac{\partial p}{{\partial \vartheta_{1} }} = \frac{{ - \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} q_{A}^{cen} }}{{2A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2\gamma \theta q_{A}^{cen^{\theta - 1}} }} \) and \( \frac{\partial p}{{\partial \vartheta_{2} }} = \frac{{ - 2\theta q_{A}^{cen^{2\theta - 1}} }}{{A\beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{3} - \gamma \theta q_{A}^{cen^{\theta - 1}} \left( {1 + q_{A}^{cen^{\theta} } } \right)}} \), which are negative if \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \) holds. Similarly, we obtain \( \frac{{\partial q_{A} }}{{\partial \vartheta_{1} }} = \frac{{ - \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} q_{A}^{cen} }}{{2A^{2} \beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} - 2A\gamma \theta q_{A}^{cen^{\theta - 1}} }} - \frac{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }}{{2A\gamma \theta q_{A}^{cen^{\theta} - 2} }} \) and \( \frac{{\partial q_{A} }}{{\partial \vartheta_{2} }} = \frac{{ - 2\theta q_{A}^{cen^{2\theta - 1} }}}{{A^{2} \beta \left( {1 + q_{A}^{cen^{\theta} } } \right)^{3} - A\gamma \theta q_{A}^{cen^{\theta - 1}} \left( {1 + q_{A}^{cen^{\theta} } } \right)}} - \frac{{2q_{A}^{cen^{\theta} } }}{{A\gamma \left( {1 + q_{A}^{cen^{\theta} } } \right)}} \), which are negative considering \( \frac{A\beta }{\gamma } > \frac{{\theta q_{A}^{cen^{\theta - 1}} }}{{\left( {1 + q_{A}^{cen^{\theta} } } \right)^{2} }} \). The proof is complete.□

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Hosseini-Motlagh, SM., Jazinaninejad, M. & Nami, N. Recall management in pharmaceutical industry through supply chain coordination. Ann Oper Res 324, 1183–1221 (2023). https://doi.org/10.1007/s10479-020-03720-7

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