My current research broadly focuses on the design and analysis of provably-good algorithms for optimization under various notions of uncertainty. Application-wise, I am mostly interested in computational revenue management, electronic commerce, and business operations.
Fields: Stochastic optimization, choice modeling, applied probability, combinatorial optimization.
Techniques: Approximate dynamic programming, submodular functions, randomization, mathematical programming, polyhedral combinatorics.
Applications: Assortment planning, inventory, pricing, bioinformatics, scheduling, facility location.
Selected Recent Publications
Appeared / Accepted
Alon Rieger and Danny Segev. Quasi-polynomial time approximation schemes for assortment optimization under Mallows-based rankings. Mathematical Programming (forthcoming).
Yicheng Bai, Jacob Feldman, Danny Segev, Huseyin Topaloglu, and Laura Wagner. Assortment optimization under the multi-purchase Multinomial Logit choice model. Operations Research (forthcoming).
Danny Segev. Approximation schemes for capacity-constrained assortment optimization under the Nested Logit model. Operations Research, 70(5):2820-2836, 2023.
Ali Aouad and Danny Segev. An approximate dynamic programming approach to the incremental knapsack problem. Operations Research, 71(4):1414-1433, 2023.
Ali Aouad, Jacob Feldman, and Danny Segev. The Exponomial choice model for assortment optimization: An alternative to the MNL model? Management Science, 69(5):2814-2832, 2023.
Yuri Faenza, Danny Segev, and Lingyi Zhang. Approximation algorithms for the generalized incremental knapsack problem. Mathematical Programming, 198:27-83, 2023.
Ali Aouad and Danny Segev. The stability of MNL-based demand under dynamic customer substitution and its algorithmic implications. Operations Research, 71(4):1216-1249, 2023.
Jacob Feldman and Danny Segev. The Multinomial Logit model with sequential offerings: Algorithmic frameworks for product recommendation displays. Operations Research, 70(4): 2162-2184, 2022.
Danny Segev and Yaron Shaposhnik. A polynomial-time approximation scheme for sequential batch-testing of series systems. Operations Research, 70(2):1153-1165, 2022.
Antoine Desir, Vineet Goyal, Srikanth Jagabathula, and Danny Segev. Mallows-smoothed distribution over rankings approach for modeling choice. Operations Research, 69(4):1206-1227, 2021.
Antoine Desir, Vineet Goyal, Danny Segev, and Chun Ye. Constrained assortment optimization under the Markov chain based choice model. Management Science, 66(2):698-721, 2020. This paper was a 2015 Nicholson Prize finalist.
Ali Aouad and Danny Segev. Display optimization for vertically differentiated locations under Multinomial Logit choice preferences. Management Science, 67(6): 3519-3550, 2020.
In review / Working papers
Aviya Goldstein Feder and Danny Segev. Dynamic assortment optimization of horizontally-differentiated products: Improved approximations and universal constructions. Working paper.
Omar El Housni, Marouane Ibn Brahim, and Danny Segev. Maximum load assortment optimization: Approximation algorithms and adaptivity gaps. Working paper.
Guillermo Gallego and Danny Segev. A constructive prophet inequality approach to the adaptive ProbeMax problem. Working paper.
Danny Segev and Sahil Singla. Efficient approximation schemes for stochastic probing and prophet problems. Working paper.
Danny Segev. The continuous-time joint replenishment problem: eps-optimal policies via pairwise alignment. In submission to Management Science.
Danny Segev. Near-optimal adaptive policies for serving stochastically departing customers. In submission to Operations Research.
Jacob Feldman and Danny Segev. Dynamic pricing with menu costs: Approximation schemes and applications to grocery retail. In submission to Operations Research.
Ali Aouad, Jacob Feldman, Danny Segev, and Dennis Zhang. Click-based MNL: Algorithmic frameworks for modeling click data in assortment optimization. In submission to Management Science.