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Joint location-inventory problem with differentiated service levels using critical level policy

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  • Escalona, P.
  • Ordóñez, F.
  • Marianov, V.

Abstract

This paper analyzes the design of a distribution network for fast-moving items able to provide differentiated service levels in terms of product availability for two demand classes (high and low priority) using a critical level policy. The model is formulated as a MINLP with chance constraints for which we propose a heuristic to solve it. Although the heuristic does not guarantee an optimal solution, our computational experiments have shown that it provides good-quality solutions that are on average 0.8% and at worst 2.7% from the optimal solution.

Suggested Citation

  • Escalona, P. & Ordóñez, F. & Marianov, V., 2015. "Joint location-inventory problem with differentiated service levels using critical level policy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 141-157.
    • Handle: RePEc:eee:transe:v:83:y:2015:i:c:p:141-157
    DOI: 10.1016/j.tre.2015.09.009
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    References listed on IDEAS

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    Cited by:

    1. Escalona, P. & Marianov, V. & Ordóñez, F. & Stegmaier, R., 2018. "On the effect of inventory policies on distribution network design with several demand classes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 229-240.
    2. Tapia-Ubeda, Francisco J. & Miranda, Pablo A. & Macchi, Marco, 2018. "A Generalized Benders Decomposition based algorithm for an inventory location problem with stochastic inventory capacity constraints," European Journal of Operational Research, Elsevier, vol. 267(3), pages 806-817.
    3. Sauvey, Christophe & Melo, Teresa & Correia, Isabel, 2019. "Two-phase heuristics for a multi-period capacitated facility location problem with service-differentiated customers," Technical Reports on Logistics of the Saarland Business School 16, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    4. Dai, Zhuo & Aqlan, Faisal & Gao, Kuo, 2017. "Optimizing multi-echelon inventory with three types of demand in supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 141-177.
    5. Straubert, Christian, 2024. "A continuous approximation location-inventory model with exact inventory costs and nonlinear delivery lead time penalties," International Journal of Production Economics, Elsevier, vol. 268(C).
    6. Rafael Bernardo Carmona-Benítez, 2020. "Dimensionality-reduction Procedure for the Capacitated p-Median Transportation Inventory Problem," Mathematics, MDPI, vol. 8(4), pages 1-16, March.
    7. Amiri-Aref, Mehdi & Klibi, Walid & Babai, M. Zied, 2018. "The multi-sourcing location inventory problem with stochastic demand," European Journal of Operational Research, Elsevier, vol. 266(1), pages 72-87.
    8. Roberto León & Pablo A. Miranda-Gonzalez & Francisco J. Tapia-Ubeda & Elias Olivares-Benitez, 2024. "An Inventory Service-Level Optimization Problem for a Multi-Warehouse Supply Chain Network with Stochastic Demands," Mathematics, MDPI, vol. 12(16), pages 1-20, August.
    9. Van der Heide, G. & Buijs, P. & Roodbergen, K.J. & Vis, I.F.A., 2018. "Dynamic shipments of inventories in shared warehouse and transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 240-257.
    10. Wang, Minke & Wu, Jiang & Kafa, Nadine & Klibi, Walid, 2020. "Carbon emission-compliance green location-inventory problem with demand and carbon price uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    11. Tingting Ji & Shoufeng Ji & Yuanyuan Ji & Hongyu Liu, 2022. "Study on Sustainable Combined Location-Inventory-Routing Problem Based on Demand Forecasting," Sustainability, MDPI, vol. 14(23), pages 1-21, December.

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