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A stochastic periodic review inventory model for vendor–buyer system with setup cost reduction and service–level constraint

Wakhid Ahmad JauhariIndustrial Engineering Department, Universitas Sebelas Maret, Surakarta, IndonesiaCorrespondence[email protected]

http://orcid.org/0000-0001-8677-5402

Rendy Surya SagaIndustrial Engineering Department, Universitas Sebelas Maret, Surakarta, Indonesia

Pages 371-389 | Received 21 Aug 2017, Accepted 03 Nov 2017, Published online: 15 Nov 2017

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https://doi.org/10.1080/21693277.2017.1401965
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Table 1. A comparison of the proposed model and some of the published stochastic vendor–buyer models.

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Table 10. The impact of the changes in ξ on the proposed model (for P_min = 1500 and τ = .2).

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Lin, Y.J. (2010) “A stochastic periodic review integrated inventory model involving defective items, backorder price discount, and variable lead time.” 4OR, 8, 281–297.10.1007/s10288-010-0124-x

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Jauhari, W. A., Sejati, N., & Rosyidi, C. N. (2016a). A collaborative supply chain inventory model with defective items, adjusted production rate and variable lead time. International Journal of Procurement Management, 9, 733–750.10.1504/IJPM.2016.079983

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Jauhari, W. A., Mayangsari, S., Kurdhi, N. A., & Wong, K. Y. (2017). A fuzzy periodic review integrated inventory model involving stochastic demand, imperfect production process and inspection errors. Cogent Engineering, 4, 1308653.

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Kurdhi, N. A., Lestari, S. M. P., & Susanti, Y. (2015). A fuzzy collaborative supply chain inventory model with controllable setup cost and service level constraint for imperfect items. International Journal of Applied Management Science, 7(2), 93–122.10.1504/IJAMS.2015.069265

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Castellano, D., Gallo, M., Santillo, L. C., & Song, D. (2017). A periodic review policy with quality improvement, setup cost reduction, backorder price discount and controllable lead time. Production & Manufacturing Research, 5(1), 328–350.10.1080/21693277.2017.1382397

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Taleizadeh, A. A., Niaki, S. T. A., & Wee, H. M. (2013). Joint single vendor-single buyer supply chain problem with stochastic demand and fuzzy lead-time. Knowledge-Based Systems, 48, 1–9.10.1016/j.knosys.2013.03.011

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A stochastic periodic review inventory model for vendor–buyer system with setup cost reduction and service–level constraint

Table 1. A comparison of the proposed model and some of the published stochastic vendor–buyer models.

Table 2. Optimization results for the proposed model.

Table 3. The impact of the changes in q₁ on the proposed model (for q₂ = 50).

Table 4. The impact of the changes in q₂ on the proposed model (for q₁ = 100).

Table 5. The impact of the changes in p₂ on the proposed model (for p₁ = 20).

Table 6. The impact of the changes in p₁ on the proposed model (for p₂ = 50).

Table 7. The impact of the changes in δ on the proposed model.

Table 8. The impact of the changes in e₁ on the proposed model (for e₂ = .04).

Table 9. The impact of the changes in e₂ on the proposed model (for e₁ = .04).

Table 10. The impact of the changes in ξ on the proposed model (for P_min = 1500 and τ = .2).

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A stochastic periodic review inventory model for vendor–buyer system with setup cost reduction and service–level constraint

Figures & data

Table 1. A comparison of the proposed model and some of the published stochastic vendor–buyer models.

Table 2. Optimization results for the proposed model.

Table 3. The impact of the changes in q1 on the proposed model (for q2 = 50).

Table 4. The impact of the changes in q2 on the proposed model (for q1 = 100).

Table 5. The impact of the changes in p2 on the proposed model (for p1 = 20).

Table 6. The impact of the changes in p1 on the proposed model (for p2 = 50).

Table 7. The impact of the changes in δ on the proposed model.

Table 8. The impact of the changes in e1 on the proposed model (for e2 = .04).

Table 9. The impact of the changes in e2 on the proposed model (for e1 = .04).

Table 10. The impact of the changes in ξ on the proposed model (for Pmin = 1500 and τ = .2).

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Table 3. The impact of the changes in q₁ on the proposed model (for q₂ = 50).

Table 4. The impact of the changes in q₂ on the proposed model (for q₁ = 100).

Table 5. The impact of the changes in p₂ on the proposed model (for p₁ = 20).

Table 6. The impact of the changes in p₁ on the proposed model (for p₂ = 50).

Table 8. The impact of the changes in e₁ on the proposed model (for e₂ = .04).

Table 9. The impact of the changes in e₂ on the proposed model (for e₁ = .04).

Table 10. The impact of the changes in ξ on the proposed model (for P_min = 1500 and τ = .2).