@techreport{SchmidtFuegenschuh2025, type = {Working Paper}, author = {Schmidt, Johannes and F{\"u}genschuh, Armin}, title = {Inter-sample avoidance in trajectory planning via an integer-polytope formulation for mixed-integer optimization}, doi = {10.26127/BTUOpen-7169}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-71697}, year = {2025}, abstract = {Autonomous vehicles such as drones or mobile robots must plan trajectories that avoid static obstacles. Their dynamics are captured by Newton's laws and discretized numerically, so the vehicle's position is tracked only at finitely many sample points. The resulting information gap allows a path to cut through an obstacle while still appearing feasible at all samples, a phenomenon called corner cutting. Finer time grids mitigate but never eliminate this artefact and increase computation time. We propose a new inter-sample avoidance scheme for polyhedral obstacles. The facet-defining half-spaces partition the three-dimensional mission space into sub-domains that we encode with binary sign vectors. Feasible one-step moves correspond to a specific subset of these vectors; we (i) enumerate that set exhaustively and (ii) give an integer-polytope formulation that enforces it without additional state variables or extra time steps. For planar motion with a single triangular no-fly zone we prove that the formulation is tight-the polytope equals the convex hull of its 0/1 points. The method is compared qualitatively with existing inter-sample avoidance techniques and its limitations are discussed. Computational experiments with a mixed-integer linear programming model for UAV mission planning, solved by Gurobi, demonstrate that the new formulation achieves realistic trajectories at competitive, or lower, solution times.}, subject = {Mixed-integer optimization; Inter-sample avoidance; Trajectory planning; Convex hull; Unmanned aerial vehicles; Gemischt-ganzzahlige Optimierung; Trajektorienplanung; Konvexe H{\"u}lle; Unbemannte Luftfahrzeuge; Drohne (Flugk{\"o}rper); Autonomes System; Bahnplanung; Gemischt-ganzzahlige Optimierung; Konvexe H{\"u}lle}, language = {en} } @techreport{SchmidtFuegenschuhBoeschow2025, type = {Working Paper}, author = {Schmidt, Johannes and F{\"u}genschuh, Armin and B{\"o}schow, Moritz M.}, title = {Sports league scheduling with minitournaments}, doi = {10.26127/BTUOpen-7029}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-70293}, year = {2025}, abstract = {In amateur or youth sports leagues, the teams play all matches during their leisure time. Thus, a schedule with a smaller number of game days is preferred and the teams are willing to partly renounce on the fairness for this by playing minitournaments instead of single matches. In this format, multiple teams meet at one of them and play against each other, reducing the number of necessary game days and required referees at the cost of unevenly distributed home field advantages. The travel times of all teams now depend on their assignment to the respective minitournaments and the choice of the home team. We present a binary linear optimization model to schedule a sports league as a double Round Robin tournament with minitournaments and most evenly distributed home field advantages, yielding a feasible league schedule with minimal total traveling distances for all teams. After adjusting orbital shrinking to break the occurring symmetries in the possible assignments, we discuss the computational efficiency and evaluate an existing schedule for the "Basketball Senioren Landesliga Brandenburg" amateur basketball league in Germany.}, subject = {Discrete optimization; Linear optimization; Orbital shrinking; Sport scheduling; Diskrete Optimierung; Lineare Optimierung; Sportligenplanung; Lineare Optimierung; Diskrete Optimierung; Wettkampfklasse; Amateursport; Jugendsport; Spielplan }, language = {en} } @techreport{ZellFuegenschuh2025, type = {Working Paper}, author = {Zell, Sascha and F{\"u}genschuh, Armin}, title = {Optimizing autonomous unmanned aircraft system deployment locations for enhanced wildfire detection and monitoring}, doi = {10.26127/BTUOpen-6942}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-69422}, year = {2025}, abstract = {The increase in the frequency and severity of wildfires as a symptom of climate change requires innovative methods of wildfire fighting. For this reason, we propose a framework for an autonomous Unmanned Aircraft System (UAS), consisting of a fleet of Micro Air Vehicles (MAVs) stored in purpose-built hangars. The intention is to deploy the UAS as the first responder to an alarm and have the sensor-equipped MAVs monitor the target area even before other standard firefighting vehicles have arrived. The focus of this paper is primarily on the development and application of a location-allocation optimization Mixed-Integer Linear Programming (MILP) model that selects different MAV and hangar types and locates them, with the objective of approaching the target area as quickly as possible while guaranteeing a certain monitoring time at the scene. The model is applied to a large, sparsely populated, rural operational area, around a third of which consists of forest in the South of Brandenburg, Germany. The spatial demand is measured through an easily reproducible and transferable open data approach. Finally, several instances with different fixed numbers of hangars and MAVs to be set up are solved by the commercial state-of-the-art solver CPLEX and analyzed for their computation time.}, subject = {Mixed-lnteger linear programming; Unmanned aircraft systems; Micro air vehicles; Autonomous wildfire monitoring; Location-allocation optimization; Gemischt-ganzzahlige Programmierung; Drohnen; Drohnenhangar; Standortproblem; Autonome Waldbrandbek{\"a}mpfung; Waldbrand; {\"U}berwachung; Drohne (Flugk{\"o}rper); Autonomes System; Standortproblem; Lineare Optimierung}, language = {en} } @techreport{ZellSchneidereitFuegenschuhetal.2024, type = {Working Paper}, author = {Zell, Sascha and Schneidereit, Toni and F{\"u}genschuh, Armin and Breuß, Michael}, title = {Advanced search and rescue operations for drowning swimmers using autonomous unmanned aircraft systems : location optimization, flight trajectory planning and image-based localization}, doi = {10.26127/BTUOpen-6866}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-68669}, year = {2024}, abstract = {Drowning is among the most prevalent causes of death from unintentional injuries worldwide. Because of the time-sensitive nature of swimming accidents and the shortage of lifeguard staff resulting in unsupervised swimming areas, interest in supportive rescue methods increases. In this paper, we propose an autonomous Unmanned Aircraft System (UAS) usable by Emergency Medical Service (EMS) providers in swimmer rescue scenarios additionally to Standard Rescue Operation (SRO) equipment. The UAS consists of Unmanned Aerial Vehicles (UAVs) and purpose-built hangars located near the swimming area to store the UAVs. When receiving an alert, the UAVs autonomously navigate to the emergency site to conduct a Search and Rescue (S\&R) operation for the drowning person. We introduce a Mixed-Integer Linear Programming (MILP) model to address the Facility Location Problem (FLP), assisting with identification of accessibility-optimal UAV hangar placements near the swimming area. Additionally, we present a MILP model to optimize the UAV flight trajectories in advance of the operation, allowing for efficient coordination of a heterogeneous UAV fleet. We apply the presented MILP models to a real-world scenario in the Lusatian Lake District using the state-of-the-art commercial solver CPLEX to solve the instances. Furthermore, we present a method for automated image-based swimmer localization using the state-of-the-art neural network You Only Look Once (YOLO). Finally, we use a Discrete-Event Simulation (DES) approach to quantify how much time is saved by using additional resources.}, subject = {Unmanned Aerial Vehicle; Unmanned Aircraft System; Water Rescue; Mixed-Integer Linear Programming; You Only Look Once; Drohne; Drohnenhangar; Wasserrettung; Gemischt-ganzzahlige Programmierung; Standortoptimierung; Drohne ; Wasserrettung; Lineare Optimierung}, language = {en} } @techreport{SchmidtFuegenschuh2023, type = {Working Paper}, author = {Schmidt, Johannes and F{\"u}genschuh, Armin}, title = {Trajectory optimization for arbitrary layered geometries in wire-arc additive manufacturing}, doi = {10.26127/BTUOpen-6267}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-62678}, year = {2023}, abstract = {In wire-arc additive manufacturing, a wire is molten by an electrical or laser arc and deposited droplet-by-droplet to construct the desired workpiece, given as a set of two-dimensional layers. The weld source can move freely over a substrate plate, processing each layer, but there is also the possibility of moving without welding. A primary reason for stress inside the material is the large thermal gradient caused by the weld source, resulting in lower product quality. Thus, it is desirable to control the temperature of the workpiece during the process. One way of its optimization is the trajectory of the weld source. We consider the problem of finding a trajectory of the moving weld source for a single layer of an arbitrary workpiece that maximizes the quality of the part and derive a novel mixed-integer PDE-constrained model, including the calculation of a detailed temperature distribution measuring the overall quality. The resulting optimization problem is linearized and solved using the state-of-the-art numerical solver IBM CPLEX. Its performance is examined by several computational studies.}, subject = {Mixed-integer programming; Trajektorie ; Partielle Differentialgleichung; Finite-Volumen-Methode; W{\"a}rmeleitung; Wire are additive manufacturing; Trajectory planning; Partial differential equations; Finite element method; Heat conduction; Gemischt-ganzzahlige Programmierung; Additive Draht-Lichtbogen-Fertigung,; Trajektorienplanung; Partielle Differenzialgleichungen; Finite-Elemente-Methode; W{\"a}rmeleitung}, language = {en} } @techreport{BeisegelBuhlIsraretal.2021, type = {Working Paper}, author = {Beisegel, Jesse and Buhl, Johannes and Israr, Rameez and Schmidt, Johannes and Bambach, Markus and F{\"u}genschuh, Armin}, title = {Mixed-integer programming for additive manufacturing}, doi = {10.26127/BTUOpen-5731}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-57317}, year = {2021}, abstract = {Since the beginning of its development in the 1950s, mixed integer programming (MIP) has been used for a variety of practical application problems, such as sequence optimization. Exact solution techniques for MIPs, most prominently branch-and-cut techniques, have the advantage (compared to heuristics such as genetic algorithms) that they can generate solutions with optimality certificates. The novel process of additive manufacturing opens up a further perspective for their use. With the two common techniques, Wire Arc Additive Manufacturing (WAAM) and Laser Powder Bed Fusion (LPBD), the sequence in which a given component geometry must be manufactured can be planned. In particular, the heat transfer within the component must be taken into account here, since excessive temperature gradients can lead to internal stresses and warpage after cooling. In order to integrate the temperature, heat transfer models (heat conduction, heat radiation) are integrated into a sequencing model. This leads to the problem class of MIPDECO: MIPs with partial differential equations (PDEs) as further constraints. We present these model approaches for both manufacturing techniques and carry out test calculations for sample geometries in order to demonstrate the feasibility of the approach.}, subject = {Wire arc additive manufacturing; Laser powder bed fusion; Mixed-integer programming; Partial differential equations; Finite element method; Additive Fertigung mit Drahtlichtbogen; Laser-Pulverbett-Schmelzen; Gemischt-ganzzahlige Programmierung; Partielle Differenzialgleichungen; Finite-Elemente-Methode; Rapid Prototyping ; Ganzzahlige Optimierung; Finite-Elemente-Methode; Partielle Differentialgleichung}, language = {en} } @techreport{DeeFuegenschuhKaimakamis2021, type = {Working Paper}, author = {Dee, Robin and F{\"u}genschuh, Armin and Kaimakamis, George}, title = {The unit re-balancing problem}, doi = {10.26127/BTUOpen-5658}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-56587}, year = {2021}, abstract = {We describe the problem of re-balancing a number of units distributed over a geographic area. Each unit consists of a number of components. A value between 0 and 1 describes the current rating of each component. By a piecewise linear function this value is converted into a nominal status assessment. The lowest of the statuses determines the efficiency of a unit, and the highest status its cost. An unbalanced unit has a gap between these two. To re-balance the units, components can be transferred. The goal is to maximize the efficiency of all units. On a secondary level, the cost for the re-balancing should be minimal. We present a mixed-integer nonlinear programming formulation for this problem, which describes the potential movement of components as a multi-commodity flow. The piecewise linear functions needed to obtain the status values are reformulated using inequalities and binary variables. This results in a mixed-integer linear program, and numerical standard solvers are able to compute proven optimal solutions for instances with up to 100 units. We present numerical solutions for a set of test instances and a bi-criteria objective function, and discuss the trade-off between cost and efficiency.}, subject = {Re-balancing problem; Efficiency; Mixed-integer linear programming; Bi-criteria optimization; Umverteilungsproblem; Effizienz; Gemischt-ganzzahlige Programmierung; Bikriterielle Optimierung; Ganzzahlige Optimierung; Optimierungsproblem; Effizienz}, language = {en} } @techreport{SchmidtFuegenschuh2021, type = {Working Paper}, author = {Schmidt, Johannes and F{\"u}genschuh, Armin}, title = {Planning inspection flights with an inhomogeneous fleet of micro aerial vehicles}, doi = {10.26127/BTUOpen-5656}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-56569}, year = {2021}, abstract = {We consider the problem of planning an inspection flight to a given set of waypo- ints using an inhomogeneous fleet of multirotor, battery-driven micro aerial vehicles (MAVs). Therein, two subproblems must be solved. On the one side, the detailed trajectories of all MAVs must be planned, taking technical and environmental restrictions into account and on the other side, the MAVs must be assigned to the waypoints considering their installed equipment. The goal is to visit all waypoints in minimal time. The strong interaction of the two subproblems makes it necessary to tackle them simultaneously. Several aspects are taken into account to allow realistic solutions. A two-level time grid approach is applied to achieve smooth trajectories, while the flight dynamics of the MAVs are modeled in great detail. Safety distances must be maintained between them and they can recharge at charging stations located within the mission area. There can be polyhedral restricted air spaces that must be avoided. Furthermore, weather conditions are incorporated by polyhedral wind zones affecting the drones and each waypoint has a time window within it must be visited. We formulate this problem as a mixed-integer linear program and show whether the state-of-the-art numerical solver Gurobi is applicable to solve model instances.}, subject = {Mixed integer linear programming; Inspection path planning; Trajectory planning; Micro aerial vehicles; Gemischt-ganzzahlige Programmierung; Inspektionspfadplanung; Trajektorienplanung; Kleinstdrohnen; Ganzzahlige Optimierung; Tourenplanung; Drohne }, language = {en} } @techreport{SchmidtBuhlFuegenschuh2021, type = {Working Paper}, author = {Schmidt, Johannes and Buhl, Johannes and F{\"u}genschuh, Armin}, title = {A finite element approach for trajectory optimization in Wire-Arc Additive Manufacturing}, editor = {F{\"u}genschuh, Armin}, doi = {10.26127/BTUOpen-5575}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-55753}, year = {2021}, abstract = {In wire-arc additive manufacturing (WAAM), the desired workpiece is built layerwise by a moving heat source depositing droplets of molten wire on a substrate plate. To reduce material accumulations, the trajectory of the weld source should be continuous, but transit moves without welding, called deadheading, are possible. The enormous heat of the weld source causes large temperature gradients, leading to a strain distribution in the welded material which can lead even to cracks. In summary, it can be concluded that the temperature gradient reduce the quality of the workpiece. We consider the problem of finding a trajectory of the weld source with minimal temperature deviation from a given target temperature for one layer of a workpiece with welding segments broader than the width of the weld pool. The temperature distribution is modeled using the finite element method. We formulate this problem as a mixed-integer linear programming model and demonstrate its solvability by a standard mixed-integer solver.}, subject = {Additive manufacturing; Heat equation; Path optimization; Finite element method; Mixed-integer linear programming; Additive Fertigung; Pfadoptimierung; W{\"a}rmeleitungsgleichung; Finite-Elemente-Methode; Gemischt-ganzzahlige lineare Programmierung; Rapid Prototyping ; W{\"a}rmeleitungsgleichung; Bahnplanung; Ganzzahlige lineare Optimierung; Finite-Elemente-Methode}, language = {en} } @techreport{GnegelSchaudtClausenetal.2021, type = {Working Paper}, author = {Gnegel, Fabian and Schaudt, Stefan and Clausen, Uwe and F{\"u}genschuh, Armin}, title = {A 2D layered graph approach for scheduling delivery robots}, editor = {F{\"u}genschuh, Armin}, issn = {2627-6100}, doi = {10.26127/BTUOpen-5493}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-54931}, year = {2021}, abstract = {In recent years parcel volumes reached record highs. The logistics industry is seeking new innovative concepts to keep pace. For densely populated areas delivery robots are a promising alternative to conventional trucking. These electric robots drive autonomously on sidewalks and deliver urgent goods, such as express parcels, medicine, or meals. The limited cargo space and battery capacity of these vehicles necessitates a depot visit after each customer served. The problem can be formulated as an electric vehicle routing problem with soft time windows and a single unit capacity. The goal is to serve all customers such that the quadratic sum of delays is minimized and each vehicle operates within its battery bounds. To solve this problem, we formulate an MIQP and present an expanded formulation based on a layered graph. For this layered graph we derive two solution approaches based on relaxations, which use less nodes and arcs. The first, Iterative Refinement, always solves the current relaxation to optimality and refines the graph if the solution is not feasible for the expanded formulation. This is repeated until a proven optimal solution is found. The second, Branch and Refine, integrates the graph refinement into a branch and bound framework avoiding restarts. Computational experiments performed on modified Solomon instances demonstrate the advantage of using our solution approaches and show that Branch and Refine outperforms Iterative Refinement in all studied parameter configurations.}, subject = {Delivery robots; Electric vehicle routing problem; Graph refinement; Layered graphs; Partial recharging; Lieferroboter; Streckenplanung f{\"u}r elektrische Fahrzeuge; Graphenverfeinerung; Geschichtete Graphen; Partielles Aufladen; Mobiler Roboter; Tourenplanung; Optimierung}, language = {en} } @techreport{SchmidtFuegenschuh2021, type = {Working Paper}, author = {Schmidt, Johannes and F{\"u}genschuh, Armin}, title = {A two-time-level model for mission and flight planning of an inhomogeneous fleet of unmanned aerial vehicles}, editor = {F{\"u}genschuh, Armin}, doi = {10.26127/BTUOpen-5461}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-54619}, year = {2021}, abstract = {We consider the mission and flight planning problem for an inhomogeneous fleet of unmanned aerial vehicles (UAVs). Therein, the mission planning problem of assigning targets to a fleet of UAVs and the flight planning problem of finding optimal flight trajectories between a given set of waypoints are combined into one model and solved simultaneously. Thus, trajectories of an inhomogeneous fleet of UAVs have to be specified such that the sum of waypoint-related scores is maximized, considering technical and environmental constraints. Several aspects of an existing basic model are expanded to achieve a more detailed solution. A two-level time grid approach is presented to smooth the computed trajectories. The three-dimensional mission area can contain convex-shaped restricted airspaces and convex subareas where wind affects the flight trajectories. Furthermore, the flight dynamics are related to the mass change, due to fuel consumption, and the operating range of every UAV is altitude-dependent. A class of benchmark instances for collision avoidance is adapted and expanded to fit our model and we prove an upper bound on its objective value. Finally, the presented features and results are tested and discussed on several test instances using GUROBI as a state-of-the-art numerical solver.}, subject = {Mixed-integer nonlinear programming; Mission Planning; Inhomogeneous Fleet; Time Windows; Linearization methods; Gemischt-ganzzahlige nichtlineare Programming; Missionsplanung; Inhomogene Flotte; Zeitfenster; Linearisierungsmethoden; Nichtlineare Optimierung; Tourenplanung; Flugk{\"o}rper}, language = {en} } @techreport{GnegelFuegenschuh2020, type = {Working Paper}, author = {Gnegel, Fabian and F{\"u}genschuh, Armin}, title = {Branch-and-refine for solving time-dependent problems}, editor = {F{\"u}genschuh, Armin}, issn = {2627-6100}, doi = {10.26127/BTUOpen-5199}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-51995}, year = {2020}, abstract = {Einer der Standardans{\"a}tze zur L{\"o}sung zeitabh{\"a}ngiger diskreter Optimierungsprobleme, wie z.B. das Problem des Handlungsreisenden mit Zeitfenstern oder das K{\"u}rzeste Wege Problem mit Zeitfenstern, ist die Herleitung einer sogenannten zeitindizierten Formulierung. Wenn dem Problem eine Struktur zu Grunde liegt, die durch einen Graphen beschrieben werden kann, basiert die zeitindizierte Formulierung normalerweise auf einem anderen, erweiterten Graphen, der in der Literatur als zeitexpandierter Graph bezeichnet wird. Der zeitexpandierte Graph kann oft so generiert werden, dass alle Zeitbeschr{\"a}nkungen bereits aufgrund seiner Topologie erf{\"u}llt sind und somit Algorithmen f{\"u}r die entsprechende zeitunabh{\"a}ngige Variante angewendet werden k{\"o}nnen. Der Nachteil dieses Ansatzes ist, dass die Mengen der Ecken und B{\"o}gen des zeitexpandierten Graphen viel gr{\"o}ßer sind als die des urspr{\"u}nglichen Graphen. In neueren Arbeiten hat sich jedoch gezeigt, dass f{\"u}r viele praktische Anwendungen eine partielle Expandierung des Graphen, die m{\"o}glicherweise zeitunm{\"o}gliche Pfade zul{\"a}sst, oft ausreicht, um eine beweisbar optimale L{\"o}sung zu finden. Diese Ans{\"a}tze verfeinern iterativ den urspr{\"u}nglichen Graphen und l{\"o}sen in jeder Iteration eine Relaxierung der zeitexpandierten Formulierung. Wenn die L{\"o}sung der aktuellen Relaxation alle Zeitbeschr{\"a}nkungen erf{\"u}llt, kann daraus eine optimale L{\"o}sung abgeleitet werden, und der Algorithmus terminiert. In dieser Arbeit stellen wir neue Ideen vor, die das {\"U}bertragen von Informationen {\"u}ber die optimale L{\"o}sung eines gr{\"o}beren Graphen zu einem verfeinerten Graphen erm{\"o}glichen und zeigen, wie diese in Algorithmen verwendet werden k{\"o}nnen. Genauer gesagt stellen wir einen neuen Algorithmus zur L{\"o}sung von MILP-Formulierungen (Mixed Integer Linear Program) von zeitabh{\"a}ngigen Problemen vor, der es erm{\"o}glicht, die Graphenverfeinerung w{\"a}hrend der Untersuchung des Branch-and-Bound Baums durchzuf{\"u}hren, anstatt jedes Mal neu zu starten, wenn die optimale L{\"o}sung sich als nicht zul{\"a}ssig herausgestellt hat. Um die praktische Relevanz dieses Algorithmus zu demonstrieren, pr{\"a}sentieren wir Ergebnisse von numerische Experimenten seiner Anwendung auf das K{\"u}rzeste Wege Problem mit Zeitfenstern und das Problem des Handlungsreisenden mit Zeitfenstern.}, subject = {Graphenverfeinerung; Branch-and-Bound; K{\"u}rzeste Wege Problem mit Zeitfenstern; Problem des Handlungsreisenden mit Zeitfenstern; Graph refinement; Branch-and-bound; Shortest path problem with time-windows; Travelling salesman problem with time-windows; Branch-and-Bound-Methode; K{\"u}rzester-Weg-Problem; Travelling-salesman-Problem}, language = {en} } @techreport{OclooFuegenschuhPamen2020, type = {Working Paper}, author = {Ocloo, Valentina E. and F{\"u}genschuh, Armin and Pamen, Olivier M.}, title = {A new mathematical model for a 3D container packing problem}, editor = {F{\"u}genschuh, Armin}, issn = {2627-6100}, doi = {10.26127/BTUOpen-5088}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-50880}, year = {2020}, abstract = {Wir betrachten das Problem der Einzelcontainerpackung eines Unternehmens, das seine Kunden bedienen muss, indem es zuerst die Produkte in Kartons legt und diese dann in einen Container l{\"a}dt. F{\"u}r dieses Problem entwickeln und l{\"o}sen wir ein lineares gemischt-ganzzahliges Modell. Unser Modell ber{\"u}cksichtigt geometrische Randbedingungen, beispielsweise {\"U}berlappungsverbote, Orientierungs-Bedingungen und Randbedingungen f{\"u}r die relative Positionierung der Kartons. Wir betrachten auch die Erweiterung des Modells durch die Integration der Schwerpunktsabweichung der Packung vom Containermittelpunkt. Das Modell wurde an einer großen Anzahl von realen Instanzen getestet, die bis zu 41 Kartons enthalten. In den meisten F{\"a}llen wurden optimale L{\"o}sungen erzielt bzw. nah-optimale L{\"o}sungen mit beweisbar kleiner Optimalit{\"a}tsl{\"u}cke.}, subject = {Container packing problem; Mixed-integer programming; Box orientation; Non-overlapping; Center of gravity deviation; Optimierungsproblem; Container; Lineare Optimierung; Logistik}, language = {en} }