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
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
Omar El Housni, Marouane Ibn Brahim, and Danny Segev. Maximum load assortment optimization: Approximation algorithms and adaptivity gaps. Working paper.
Danny Segev. The continuous-time joint replenishment problem: eps-optimal policies via pairwise alignment. 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. Near-optimal adaptive policies for serving stochastically departing customers. In submission to Operations Research.
Alon Rieger and Danny Segev. Quasi-polynomial time approximation schemes for assortment optimization under Mallows-based rankings. In submission to Mathematical Programming.
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.