@misc{EulerBorndoerferStrunketal.2021, author = {Euler, Ricardo and Bornd{\"o}rfer, Ralf and Strunk, Timo and Takkula, Tuomo}, title = {ULD Build-Up Scheduling with Dynamic Batching in an Air Freight Hub}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-83482}, year = {2021}, abstract = {Air freight is usually shipped in standardized unit load devices (ULDs). The planning process for the consolidation of transit cargo from inbound flights or locally emerging shipments into ULDs for outbound flights is called build-up scheduling. More specifically, outbound ULDs must be assigned a time and a workstation subject to both workstation capacity constraints and the availability of shipments which in turn depends on break-down decisions for incoming ULDs. ULDs scheduled for the same outbound flight should be built up in temporal and spatial proximity. This serves both to minimize overhead in transportation times and to allow workers to move freight between ULDs. We propose to address this requirement by processing ULDs for the same outbound flight in batches. For the above build-up scheduling problem, we introduce a multi-commodity network design model. Outbound flights are modeled as commodities; transit cargo is represented by cargo flow volume and unpack and batch decisions are represented as design variables. The model is solved with standard MIP solvers on a set of benchmark data. For instances with a limited number of resource conflicts, near-optimal solutions are found in under two hours for a whole week of operations.}, language = {en} } @inproceedings{EulerBorndoerferPuchertetal.2024, author = {Euler, Ricardo and Bornd{\"o}rfer, Ralf and Puchert, Christian and Takkula, Tuomo}, title = {ULD Build-Up Scheduling with Logic-Based Benders Decomposition}, volume = {14742}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research}, doi = {10.1007/978-3-031-60597-0_17}, pages = {259 -- 276}, year = {2024}, abstract = {We study a complex planning and scheduling problem arising from the build-up process of air cargo pallets and containers, collectively referred to as unit load devices (ULD), in which ULDs must be assigned to workstations for loading. Since air freight usually becomes available gradually along the planning horizon, ULD build-ups must be scheduled neither too early to avoid underutilizing ULD capacity, nor too late to avoid resource conflicts with other flights. Whenever possible, ULDs should be built up in batches, thereby giving ground handlers more freedom to rearrange cargo and utilize the ULD's capacity efficiently. The resulting scheduling problem has an intricate cost function and produces large time-expanded models, especially for longer planning horizons. We propose a logic-based Benders decomposition approach that assigns batches to time intervals and workstations in the master problem, while the actual schedule is decided in a subproblem. By choosing appropriate intervals, the subproblem becomes a feasibility problem that decomposes over the workstations. Additionally, the similarity of many batches is exploited by a strengthening procedure for no-good cuts. We benchmark our approach against a time-expanded MIP formulation from the literature on a publicly available data set. It solves 15\% more instances to optimality and decreases run times by more than 50\% in the geometric mean. This improvement is especially pronounced for longer planning horizons of up to one week, where the Benders approach solves over 50\% instances more than the baseline}, language = {en} } @article{EulerMaristanydelasCasas2024, author = {Euler, Ricardo and Maristany de las Casas, Pedro}, title = {Labeling methods for partially ordered paths}, volume = {318}, journal = {European Journal of Operational Research}, number = {1}, edition = {1}, publisher = {Elsevier BV}, arxiv = {http://arxiv.org/abs/2307.10332}, doi = {10.1016/j.ejor.2024.05.002}, pages = {19 -- 30}, year = {2024}, abstract = {The landscape of applications and subroutines relying on shortest path computations continues to grow steadily. This growth is driven by the undeniable success of shortest path algorithms in theory and practice. It also introduces new challenges as the models and assessing the optimality of paths become more complicated. Hence, multiple recent publications in the field adapt existing labeling methods in an ad hoc fashion to their specific problem variant without considering the underlying general structure: they always deal with multi-criteria scenarios, and those criteria define different partial orders on the paths. In this paper, we introduce the partial order shortest path problem (POSP), a generalization of the multi-objective shortest path problem (MOSP) and in turn also of the classical shortest path problem. POSP captures the particular structure of many shortest path applications as special cases. In this generality, we study optimality conditions or the lack of them, depending on the objective functions' properties. Our final contribution is a big lookup table summarizing our findings and providing the reader with an easy way to choose among the most recent multi-criteria shortest path algorithms depending on their problems' weight structure. Examples range from time-dependent shortest path and bottleneck path problems to the electric vehicle shortest path problem with recharging and complex financial weight functions studied in the public transportation community. Our results hold for general digraphs and, therefore, surpass previous generalizations that were limited to acyclic graphs.}, language = {en} } @article{BorndoerferEulerKarbstein2021, author = {Bornd{\"o}rfer, Ralf and Euler, Ricardo and Karbstein, Marika}, title = {Ein Graphen-basiertes Modell zur Beschreibung von Preissystemen im {\"o}ffentlichen Nahverkehr}, volume = {002/127}, journal = {HEUREKA 21}, publisher = {FGSV}, address = {Stuttgart}, pages = {1 -- 15}, year = {2021}, abstract = {In dieser Arbeit wird ein graphenbasiertes Modell zur Einbindung von Preissystemen des {\"o}ffentlichen Nahverkehrs in Routing-Algorithmen vorgestellt. Jeder Knoten des Graphen repr{\"a}sentiert einen abstrakten Preiszustand einer Route und ist an einen tats{\"a}chlichen Preis gekoppelt. Damit sind sehr einfache und konzise Beschreibungen von Tarifstrukturen m{\"o}glich, diesich algorithmisch behandeln lassen. Durch das zeitgleiche Tracken eines Pfades im Routinggraphen im Ticketgraphen kann schon w{\"a}hrend einer Routenberechnung der Preis bestimmt werden. Dies erm{\"o}glicht die Berechnung von preisoptimalen Routen. An den Tarifsystemen der Verkehrsverb{\"u}nde MDV (Mitteldeutscher Verkehrsverbund) und VBB (Verkehrsverbund Berlin-Brandenburg) wird die Konstruktion des Modells detailliert erl{\"a}utert.}, language = {de} } @article{EulerLindnerBorndoerfer2024, author = {Euler, Ricardo and Lindner, Niels and Bornd{\"o}rfer, Ralf}, title = {Price optimal routing in public transportation}, volume = {13}, journal = {EURO Journal on Transportation and Logistics}, publisher = {Elsevier BV}, issn = {2192-4376}, doi = {10.1016/j.ejtl.2024.100128}, pages = {1 -- 15}, year = {2024}, language = {en} } @article{EulerMaristanydelasCasasBorndoerfer2024, author = {Euler, Ricardo and Maristany de las Casas, Pedro and Bornd{\"o}rfer, Ralf}, title = {Logic-Constrained Shortest Paths for Flight Planning}, arxiv = {http://arxiv.org/abs/2412.13235}, year = {2024}, abstract = {The Logic-Constrained Shortest Path Problem (LCSP) combines a one-to-one shortest path problem with satisfiability constraints imposed on the routing graph. This setting arises in flight planning, where air traffic control (ATC) authorities are enforcing a set of traffic flow restrictions (TFRs) on aircraft routes in order to increase safety and throughput. We propose a new branch and bound-based algorithm for the LCSP. The resulting algorithm has three main degrees of freedom: the node selection rule, the branching rule and the conflict. While node selection and branching rules have been long studied in the MIP and SAT communities, most of them cannot be applied out of the box for the LCSP. We review the existing literature and develop tailored variants of the most prominent rules. The conflict, the set of variables to which the branching rule is applied, is unique to the LCSP. We analyze its theoretical impact on the B\&B algorithm. In the second part of the paper, we show how to model the Flight Planning Problem with TFRs as an LCSP and solve it using the branch and bound algorithm. We demonstrate the algorithm's efficiency on a dataset consisting of a global flight graph and a set of around 20000 real TFRs obtained from our industry partner Lufthansa Systems GmbH. We make this dataset publicly available. Finally, we conduct an empirical in-depth analysis of node selection rules, branching rules and conflicts. Carefully choosing an appropriate combination yields an improvement of an order of magnitude compared to an uninformed choice.}, language = {en} } @article{EulerLindnerBorndoerfer2022, author = {Euler, Ricardo and Lindner, Niels and Bornd{\"o}rfer, Ralf}, title = {Price Optimal Routing in Public Transportation}, arxiv = {http://arxiv.org/abs/2204.01326}, doi = {https://doi.org/10.48550/arXiv.2204.01326}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-86414}, year = {2022}, abstract = {We consider the price-optimal earliest arrival problem in public transit (POEAP) in which we aim to calculate the Pareto-front of journeys with respect to ticket price and arrival time in a public transportation network. Public transit fare structures are often a combination of various fare strategies such as, e.g., distance-based fares, zone-based fares or flat fares. The rules that determine the actual ticket price are often very complex. Accordingly, fare structures are notoriously difficult to model as it is in general not sufficient to simply assign costs to arcs in a routing graph. Research into POEAP is scarce and usually either relies on heuristics or only considers restrictive fare models that are too limited to cover the full scope of most real-world applications. We therefore introduce conditional fare networks (CFNs), the first framework for representing a large number of real-world fare structures. We show that by relaxing label domination criteria, CFNs can be used as a building block in label-setting multi-objective shortest path algorithms. By the nature of their extensive modeling capabilities, optimizing over CFNs is NP-hard. However, we demonstrate that adapting the multi-criteria RAPTOR (MCRAP) algorithm for CFNs yields an algorithm capable of solving POEAP to optimality in less than 400 ms on average on a real-world data set. By restricting the size of the Pareto-set, running times are further reduced to below 10 ms.}, language = {en} } @inproceedings{EulerBorndoerferStrunketal.2022, author = {Euler, Ricardo and Bornd{\"o}rfer, Ralf and Strunk, Timo and Takkula, Tuomo}, title = {ULD Build-Up Scheduling with Dynamic Batching in an Air Freight Hub}, booktitle = {Operations Research Proceedings 2021}, doi = {10.1007/978-3-031-08623-6_38}, pages = {254 -- 260}, year = {2022}, abstract = {Air freight is usually shipped in standardized unit load devices (ULDs). The planning process for the consolidation of transit cargo from inbound flights or locally emerging shipments into ULDs for outbound flights is called build-up scheduling. More specifically, outbound ULDs must be assigned a time and a workstation subject to both workstation capacity constraints and the availability of shipments which in turn depends on break-down decisions for incoming ULDs. ULDs scheduled for the same outbound flight should be built up in temporal and spatial proximity. This serves both to minimize overhead in transportation times and to allow workers to move freight between ULDs. We propose to address this requirement by processing ULDs for the same outbound flight in batches. For the above build-up scheduling problem, we introduce a multi-commodity network design model. Outbound flights are modeled as commodities; transit cargo is represented by cargo flow volume and unpack and batch decisions are represented as design variables. The model is solved with a standard MIP solver on a set of benchmark data. For instances with a limited number of resource conflicts, near-optimal solutions are found in under two hours for a whole week of operations.}, language = {en} }