DOI: 10.5281/zenodo.4680988
title: Problem instances for the hub-arrival-departure problem

description: We systematically generated problem instances for the hub-arrival-departure problem introduced in the article “Optimizing consolidation processes in hubs: The hub-arrival-departure problem. <em>Working Paper Friedrich-Schiller-Universität Jena</em>, 2020”. For a detailed description of the instance generation procedure, we would like to refer to Section 6.1 of the aforementioned article. In order to evaluate the performance of our proposed solution methods, we generated 900 instances of HAD-MinLateShipments and 540 instances of HAD-MaxConsolidationTime which we provide in two separate datasets containing the respective instances as text files. Each instance is assigned a running index and is labeled as “&lt;problem&gt;_&lt;ID&gt;.txt”, with &lt;problem&gt; being either MLS (HAD-MinLateShipments) or MCT (HAD-MaxConsolidationTime) and with &lt;ID&gt; being the corresponding running index. Each file provides the same basic instance data: the number of vehicles \(n\), the set \(\Omega\) of vehicle paris \((i,j)\) exchanging goods among each other and the weight matrix \(w_{ij}\) indicating the number of shipments exchanged by vehicle pair \((i,j)\in \Omega\) . Additionally, for HAD-MinLateShipments, we provide the number of empty slots \(e\) as well as the consolidation time \(\delta\). Besides the basic parameter, we need to provide the number of allowed late shipments \(S\) for HAD-MaxConsolidationTime. The text-files for <strong>HAD-MinLateShipments </strong>are structured as follows: ID = &lt;integer number: running index&gt;. count_vehicles = &lt;integer number: number of vehicles \(n\)&gt;. count_emptySlots = &lt;integer number: empty slots \(e\)&gt;. delta = &lt;integer number: consolidation time \(\delta\)&gt;. omega = &lt;matrix with \(n\times n\) integers: \((i,j)\) = True, if vehicle \(i\) exchenges goods with vehicle \(j\), False otherwise&gt;. weight = &lt;matrix with \(n\times n\) integers: w(i,j) indicates the amount of goods exchanged by vehicle pairs \((i,j)\in \Omega\) &gt;. The text-files for <strong>HAD-MaxConsolidationTime</strong> are structured as follows: ID = &lt;integer number: running index&gt;. count_vehicles = &lt;integer number: number of vehicles \(n\)&gt;. count_allowedMissedShipments = &lt;integer number: number of allowed missed shipments \(S\)&gt;. omega = &lt;matrix with \(n\times n\) integers: \((i,j)\) = True, if vehicle \(i\) exchenges goods with vehicle \(j\), False otherwise&gt;. weight = &lt;matrix with \(n\times n\) integers: w(i,j) indicates the amount of goods exchanged by vehicle pairs \((i,j)\in \Omega\) &gt;. In our article we also aim to evaluate the robustness of different objectives. Therefore, another 180 instances are generated. Naming convention as well as the structure of the text-files is in line with the previously introduced data-sets. The text-files for <strong>simulation study</strong> are structured as follows: ID = &lt;integer number: running index&gt;. count_vehicles = &lt;integer number: number of vehicles \(n\)&gt;. count_emptySlots = &lt;integer number: empty slots \(e\)&gt;. delta = &lt;integer number: consolidation time \(\delta\)&gt;. count_allowedMissedShipments = &lt;integer number: number of allowed missed shipments \(S\)&gt;. omega = &lt;matrix with \(n\times n\) integers: \((i,j)\) = True, if vehicle \(i\) exchenges goods with vehicle \(j\), False otherwise&gt;. weight = &lt;matrix with \(n\times n\) integers: w(i,j) indicates the amount of goods exchanged by vehicle pairs \((i,j)\in \Omega\) &gt;.