The following dataset contains problem instances for the outbound truck scheduling and loading problem, which are proposed in the work "Giorgi Tadumadze & Simon Emde (2021): Loading and scheduling outbound trucks at a dispatch warehouse. IISE Transactions, DOI: 10.1080/24725854.2021.1983923”. The problem instances are stored in table “instances”, where columns of tables can be interpreted as follows: ID: <running index>; name: <instance name, specifying the number of items \(m\), number of trucks \(n\), value of parameter \(\alpha\), [value of parameter \(\Delta\)], and the width of trucks’ time windows>; O: <number of served OEMs>; m: <number of items>; n: <number of trucks>; Q: <total number of available workers>; D: <total number of available dock doors>; w_i: <vector with \(m\) elements: the \(i\)-th element corresponds to the size (required space) of item \(i\)>; d_i: <vector with \(m\) elements: the \(i\)-th element corresponds to the deadline of item \(i\)>; r_i: <vector with \(m\) elements: the \(i\)-th element corresponds to the relative importance (penalty cost per time unit of earliness) of item \(i\)>; c_j: <vector with \(n\) elements: the \(j\)-th element corresponds to the capacity of truck \(j\)>; a_j: <vector with \(n\) elements: the \(j\)-th element corresponds to the earliest possible departure time of truck \(j\)>; b_j: <vector with \(n\) elements: the \(j\)-th element corresponds to the latest possible departure time of truck \(j\)>; q_i: <vector with \(m\) elements: the \(i\)-th element corresponds to the number of required workers to prepare and load item \(i\)>; rho_i: <vector with \(m\) elements: the \(i\)-th element corresponds to the handling time of item \(i\)>; B_i: < \(m \times n\) matrix: each entry in \(j\)-th column and \(i\)-th row corresponds to the binary parameter which has a value 1 if set of available trucks \(B_i\) contains truck \(j\) (i.e., if truck \(j\) departs towards the OEM, who ordered item); 0 otherwise>; The first 270 entries (ID between 1-270) contain OTSLP instances with different instance sizes, used for the computational performance experiments (Section 5.1). The following 100 entries (ID between 271-370) contain 40 OTSLP instances with the varying time window width for each truck (ID between 271-310), 30 OTSLP instances with the varying level of available workers (ID between 311-340), and 30 OTSLP instances with the varying level of available dock doors (ID between 341-370), used for the managerial inside experiments (Section 5.2). The detailed computational results for each instance and solution approach are reported in tables, which are named with the following convention: <results_<approach>”. Specifically, we report the required computational runtime in CPU seconds, status of the found solution (“Optimal”, “Infeasible”, “Feasible” / “AbortTimeLim”), as well as the best found upper (and lower) bound in columns “runtime”, “status”, “UB” and “LB”.