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
Dynamic assortment optimization
Vineet Goyal, Retsef Levi, and Danny Segev. Near-optimal algorithms for the assortment planning problem under dynamic substitution and stochastic demand. Operations Research, 64(1):219-235, 2016.
Danny Segev. Assortment planning with nested preferences: Dynamic programming with distributions as states? Algorithmica, 81(1):393-417, 2019.
Ali Aouad, Retsef Levi, and Danny Segev. Approximation algorithms for dynamic assortment optimization models. Mathematics of Operations Research, 44(2):487-511, 2019.
Ali Aouad, Retsef Levi, and Danny Segev. Greedy-like algorithms for dynamic assortment planning under Multinomial Logit preferences. Operations Research, 66(5):1321-1345, 2018. This paper was a 2016 Nicholson Prize finalist as well as a 2021 MSOM best OR paper finalist.
Ali Aouad and Danny Segev. The stability of MNL-based demand under dynamic customer substitution and its algorithmic implications. Operations Research (forthcoming).
Static assortment optimization / parametric models
Antoine Desir, Vineet Goyal, Danny Segev, and Chun Ye. Capacity 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, Jacob Feldman, and Danny Segev. The Exponomial choice model for assortment optimization: An alternative to the MNL model? Management Science (forthcoming).
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.
Danny Segev. Approximation schemes for capacity-constrained assortment optimization under the Nested Logit model. Operations Research (forthcoming).
Product ranking and sequencing / permutation models / multi-purchase / pricing
Ali Aouad, Vivek Farias, Retsef Levi, and Danny Segev. The approximability of assortment optimization under ranking preferences. Operations Research, 66(6):1661-1669, 2018.
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.
Ali Aouad and Danny Segev. Display optimization for vertically differentiated locations under Multinomial Logit choice preferences. Management Science, 67(6): 3519-3550, 2020.
Jacob Feldman and Danny Segev. Improved approximation schemes for MNL-driven sequential assortment optimization. Operations Research, 70(4): 2162-2184, 2022.
Yicheng Bai, Jacob Feldman, Danny Segev, Huseyin Topaloglu, and Laura Wagner. Assortment optimization under the multi-purchase Multinomial Logit choice model. 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. This paper appeared as a spotlight presentation at the 21st Annual INFORMS Revenue Management and Pricing Section Conference.
Alon Rieger and Danny Segev. Quasi-polynomial time approximation schemes for assortment optimization under Mallows-based rankings. Working paper, 2022.
Sequential / incremental / dynamic optimization
Danny Segev and Yaron Shaposhnik. A polynomial-time approximation scheme for sequential batch-testing of series systems. Operations Research, 70(2):1153-1165, 2022.
Yuri Faenza, Danny Segev, and Lingyi Zhang. Approximation algorithms for the generalized incremental knapsack problem. Mathematical Programming (forthcoming).
Ali Aouad and Danny Segev. An approximate dynamic programming approach to the incremental knapsack problem. Operations Research (forthcoming).
Danny Segev. Near-optimal adaptive policies for serving stochastically departing customers. Working paper, 2022.
Guillermo Gallego and Danny Segev. A constructive prophet inequality approach to the adaptive ProbeMax problem. Working paper, 2022.
Additional cool stuff
Iftah Gamzu and Danny Segev. A sublogarithmic approximation for tollbooth pricing on trees. Mathematics of Operations Research, 42(2):377-388, 2017.
Lennart Baardman, Maxime Cohen, Kiran Panchamgam, Georgia Perakis, and Danny Segev. Scheduling promotion vehicles to boost profits. Management Science, 65(1):50-70, 2019. This paper was awarded the 2016 INFORMS best cluster paper prize (service science section).
Ali Aouad and Danny Segev. The ordered k-median problem: Surrogate models and approximation algorithms. Mathematical Programming, 177(1-2):55-83, 2019.
Iftah Gamzu and Danny Segev. A polynomial-time approximation scheme for the airplane refueling problem. Journal of Scheduling, 22(1):119-135, 2019.
Refael Hassin, R. Ravi, F. Sibel Salman, and Danny Segev. The approximability of multiple facility location on directed networks with random arc failures. Algorithmica, 82:2474-2501, 2020.