Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach

By Aharon Ben-Tal, Boaz Golany, Arkadi Nemirovski, Jean-Philippe Vial. Manufacturing & Service Operations Management, 2005

360 citations recorded by the source corpus.

Subfields: Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2e87a5ffd6fc759293a25b97d0d61441772683bf

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In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

Evacuation Transportation Planning Under Uncertainty: A Robust Optimization Approach (2009, background): “However, in some cases, RC is only feasible at unrealistic small uncertainty level or generates too conservative solution (Ben-Tal et al. 2004, 2005).”
On Complexity of Stochastic Programming Problems (2005, background): “Note, however, that the numerical results for inventory management models reported in [7, 8] demonstrate that affinity may well be not as a severe restriction as one could expect it to be.”
A Robust Optimization Perspective on Stochastic Programming (2007, background): “Some applications of robust optimization in a dynamic environment include inventory management (Bertsimas and Thiele 2006, Ben-Tal et al. 2004) and supply contracts (Ben-Tal et al. 2005).”
Data-driven robust optimization (2013, background): “These features suggest that a host of other robust optimization applications in information theory [3], supply-chain management [7] and revenue management [44] might benefit from this retrofitting.”
A practical guide to robust optimization (2015, methodology): “, supply chain management (Ben-Tal et al. 2005), project management (Ben-Tal et al.”
Differentiable Convex Optimization Layers (2019, background): “We focus on convex optimization because it is a powerful modeling tool, with applications in control [20, 16, 71], finance [57, 19], energy management [63], supply chain [17, 15], physics [51, 8], computational geometry [73], aeronautics [48], and circuit design [47, 21], among other fields.”
Strategic facility location, capacity acquisition, and technology choice decisions under demand uncertainty: Robust vs. non-robust optimization approaches (2017, background): “However, in the literature a constant ratio between the values of maximum deviations and the values of nominal demands (d̂ = αd̄) is sometimes assumed (e.g., Ben-Tal et al., 2005; Baron et al., 2011).”
Robust empirical optimization is almost the same as mean-variance optimization (2018, neutral): “This also explains why substantial variance reduction has been observed in a number of applications in the literature [3, 6, 7, 11].”