An Inventory-Location Model: Formulation, Solution Algorithm and Computational Results

By Mark S. Daskin, Collette R. Coullard, Zuo-Jun Max Shen. Annals of Operations Research, 2002

649 citations recorded by the source corpus.

Subfields: Building and Construction, Industrial and Manufacturing Engineering, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/0a02c5b83ec6ddc2f4c6d8ea2dc1c2b562d40800

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Citations in the corpus

Stochastic Transportation-Inventory Network Design Problem (2005, methodology): “As in Daskin et al. (2001), we generate an upperbound for the IP by generating a feasible solution in the following way: • Let z∗ be the optimal LP solution obtained by solving the problem using a partial set of columns.”
A multi-commodity supply chain design problem (2005, methodology): “…and technology-acquisition problem (Dasci and Verter, 2001), the multi-commodity version of the location-inventory problem (Shen, 2000; Daskin et al., 2002; Shen et al., 2003), the safety-stock optimization problem (Agarwal and Palekar, 2003), and the market-selection inventory…”
Planning for Disruptions in Supply Chain Networks (2006, background): “[27]), while under supply uncertainty, it may be optimal to operate more, smaller DCs so that a disruption to one of them has lesser impact.”
Integrated supply chain design models: a survey and future research directions (2007, background): “Shen, Coullard, Daskin [68]; Daskin, Coullard, Shen [22]), the safety stock optimization problem (Agarwal and Palekar [2]), and the market selection inventory problem (Geunes, Shen, Romeijn [30]).”
Stochastic p-robust location problems (2006, methodology): “Finally, we note that [39] applies the stochastic p-robustness measure to the location model with riskpooling (LMRP; [16,35]), a recent supply chain design model that is based on the UFLP but incorporates risk-pooling effects and economies of scale in inventory costs.”
Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case (2013, neutral): “demand-side uncertainty (Daskin et al., 2002; Shen and Qi, 2006; Shen and Daskin, 2005; Shu et al., 2005; Shen et al., 2003; Salema et al., 2007; Tsiakis et al., 2001; Leung et al., 2007; Romeijn et al., 2007; Pan and Nagi, 2010; Cardona-Valdés et al., 2011).”