Problem instances for outbound truck loading and scheduling problem

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 $\mathrm{\Delta }$], 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”.