We study the joint problem of fleet sizing and tactical deployment in aerial wildfire suppression under wind uncertainty. The strategic decision selects a heterogeneous fleet of helicopters, amphibious water scoopers, and land-based airtankers, and assigns these airframes across operational bases. The tactical decision routes each airframe over a type-specific time-space-state network and schedules water and retardant drops, forming a contiguous containment line anchored to non-flammable terrain. We formulate the problem as a two-stage stochastic integer program in which the second stage couples a wind-driven cellular-automaton fire model with a multi-commodity routing problem and a discrete control-line construction problem. Wind scenarios are sampled from a regime-switching vector autoregression with Gaussian-process spatial downscaling that captures temporal regime persistence and spatial coherence of the wind field. We solve the program by an exact integer L-shaped decomposition strengthened by monotonicity-based dominance cuts that certify entire down-sets of the binary fleet lattice per scenario evaluation, with each scenario subproblem evaluated as a compact mixed-integer program. Computational experiments on a calibrated multi-type instance show that the cost-minimising fleet pairs a single helicopter as an early-response unit with three land-based airtankers as a perimeter-installation unit, exploiting the complementary roles of temporary water suppression and permanent retardant lines while satisfying airspace non-overlap constraints.

Fleet assignment choices influence passenger booking behavior, but conventional robust optimization overlooks this decision-demand dependency, leading to suboptimal revenue performance. This paper addresses this gap by proposing a data-driven contextual robust optimization framework for the fleet assignment problem (FAP) under demand uncertainty. Leveraging historical operational data, we construct a context-dependent demand prediction model in which both fleet assignment variables and ticket prices serve as fixed-design covariates. The demand uncertainty set is parameterized by a budget of uncertainty controlled by a protection level parameter, enabling a tunable trade-off between revenue maximization and robustness against demand fluctuations. The resulting model is formulated as a mixed-integer linear program (MILP). For large-scale instances, we propose a column-and-constraint generation (C&CG) algorithm enhanced with fix-and-optimize heuristics. Numerical experiments on real-world airline data demonstrate that our contextual robust model outperforms deterministic and non-contextual robust benchmarks, improving worst-case revenues by 12–18% with acceptable computational overhead.

Reviewer Assignment and Scheduling in large-scale innovation events requires selecting suitable reviewers (juries), forming panels, assigning entries (papers) to panels, and scheduling the event. Multiple criteria, such as reviewer expertise, experience, and availability, must be considered for a feasible reviewer assignment and event schedule. This paper defines the Reviewer Assignment and Scheduling Problem (RAPS), a combined variant of the reviewer assignment and conference scheduling problems discussed in the prior art. We propose a three-stage integer programming framework to solve RAPS: the first stage forms feasible jury panels, the second assigns entries to panels using a composite matching score, and the third schedules panel-entry pairs across available time slots. Computational experiments on representative instances show that the proposed approach generates high-coverage, high-utilization, and practically usable schedules for enterprise-scale settings. The framework serves as a decision-support system for improving the efficiency and quality of reviewer assignment and scheduling.