### Description This dataset contains instances for the Aircraft Landing Scheduling Problem, defined as follows: **Input:** a tuple $(m, C, F, S, R)$ where: * $m$ is the number of runways in the airport * $C$ is a set of aircraft types * $F$ is a collection of flights $f_1,\dots,f_n$, each of them characterized by * an aircraft type $a_i \in C$ * the earliest arrival time $t_{ij}$ to each of the *m* runways of the airport * S is a matrix of separation constraints $[s_{uv}]$, for $u,v\in C$ * R = $[R_1,\dots,R_m]$ is a collection of constraints, so that $R_j\subseteq C$ are the aircraft types that can land on runway $j$. **Solution:** a list of landing records $[(l_1,r_1), \dots, (l_n,r_n)]$, where each record $(l_i,r_i)$ indicates that flight $f_i$ lands on runway $r_i$ at time $l_i$, such that: * $l_i \geqslant t_{ir_i}$ * $a_i \in R_{r_i}$ * $l_{k'} \geqslant l_{k} + s_{a_ka_{k'}}$ if $r_k = r_{k'}$, $l_{k'} > l_k$, and $\nexists k'': (r_{k''} = r_k) \wedge (l_k < l_{k''} < l_{k´})$ **Objective:** minimize $\sum_{i=1}^n (l_i - \tau_i)^2$, where $\tau_i = \min_{1\leqslant j \leqslant m} t_{ij}$. ### Data format Each file has the following format: $n$ $m$ flightID1 $a_1$ $t_{11}$ ... $t_{1m}$ ... flightID$n$ $a_n$ $t_{n1}$ ... $t_{nm}$ $s_{11}$ ... $s_{13}$ ... $s_{31}$ ... $s_{33}$ All data are integers, except the flightIDs which are strings, and the aircraft classes $a_i$ which take values HEAVY, LARGE and SMALL (respectively indexed as 1, 2, 3). ### Naming Convention Instances are named as airportX_Y[r] where: * X is the number of flights $n$ * Y is the number of runways $m$ * r is a suffix whose presence indicates that the $m$-th runway is restricted and does not accept heavy flights. ### References The dataset has been used in the following works: 1. Cotta, C. Harnessing memetic algorithms: a practical guide. TOP (2025). https://doi.org/10.1007/s11750-024-00694-8