A. Bombelli
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Efficient and seamless airport access is a critical yet often overlooked process of airport operations. Strong connectivity, especially during disruption periods, significantly reduces passenger delays and potential revenue losses. Tackling these challenges demands coordinated disruption management strategies. To that end, we model coordination in a system comprising two traffic orchestrators, each responsible for managing their respective domains: airside and landside. The airside orchestrator can implement tactical flight delays, while the landside orchestrator can apply rerouting to assist passengers at-risk of missing their flights. Through negotiation between these orchestrators, the approach aims to minimize missed flights and passenger delays, while also exploring a fair distribution of costs. The negotiation process is structured using a game-theoretic framework, and an agent-based simulation is used to evaluate the effects on airport operations. A case study demonstrates the effectiveness of these measures in enhancing airport operations while balancing costs.
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Efficient and seamless airport access is a critical yet often overlooked process of airport operations. Strong connectivity, especially during disruption periods, significantly reduces passenger delays and potential revenue losses. Tackling these challenges demands coordinated disruption management strategies. To that end, we model coordination in a system comprising two traffic orchestrators, each responsible for managing their respective domains: airside and landside. The airside orchestrator can implement tactical flight delays, while the landside orchestrator can apply rerouting to assist passengers at-risk of missing their flights. Through negotiation between these orchestrators, the approach aims to minimize missed flights and passenger delays, while also exploring a fair distribution of costs. The negotiation process is structured using a game-theoretic framework, and an agent-based simulation is used to evaluate the effects on airport operations. A case study demonstrates the effectiveness of these measures in enhancing airport operations while balancing costs.
Air freight logistics under uncertainty
Integrated tail assignment, flight departure time adjustment, and shipment routing
Journal article
(2026)
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Shurui Zhu, Huijun Sun, Felipe Delgado, A. Bombelli, Lorant Tavasszy, Xin Guo, Jianjun Wu
Air cargo operations face significant challenges due to flight time variability, which can disrupt schedules and delay shipments. This is especially critical for time-sensitive cargo and in the context of express delivery. In this paper, we integrate aircraft tail assignment, flight departure time adjustment, and cargo routing decisions under flight time uncertainty. We formulate the problem as a two-stage stochastic programming model: the first-stage determines the sequence of flight legs assigned to each aircraft, while the second-stage, after flight times are realized, determines the demand to be served, its routing, and flight departure times. To improve computational performance, we develop tailored algorithms for demand itinerary generation and implement a backward scenario aggregation algorithm that preserves uncertainty characteristics while reducing problem dimensionality. Scenarios are generated using three years of historical data, allowing realistic temporal and spatial dependencies to be retained. Using data inspired by the domestic network of a major Chinese express air cargo carrier, we conduct experiments across multiple seasons. The proposed approach consistently outperforms deterministic benchmarks based on average and minimum flight times, with profit improvements of up to 4.3% and 3.8%, respectively, during the Winter Monsoon season, when variability is highest. Moreover, we show that the value of stochastic planning increases significantly when the network operates under tighter connectivity conditions. Under reduced fleet availability, profit improvements rise to 7.0% and 9.7% relative to the benchmarks based on average and minimum flight times, respectively, highlighting how delay propagation in tightly coupled aircraft rotations makes deterministic plans particularly fragile. Overall, the results demonstrate that anticipating uncertainty at the tactical planning stage improves both operational robustness and revenue performance in large-scale air cargo networks.
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Air cargo operations face significant challenges due to flight time variability, which can disrupt schedules and delay shipments. This is especially critical for time-sensitive cargo and in the context of express delivery. In this paper, we integrate aircraft tail assignment, flight departure time adjustment, and cargo routing decisions under flight time uncertainty. We formulate the problem as a two-stage stochastic programming model: the first-stage determines the sequence of flight legs assigned to each aircraft, while the second-stage, after flight times are realized, determines the demand to be served, its routing, and flight departure times. To improve computational performance, we develop tailored algorithms for demand itinerary generation and implement a backward scenario aggregation algorithm that preserves uncertainty characteristics while reducing problem dimensionality. Scenarios are generated using three years of historical data, allowing realistic temporal and spatial dependencies to be retained. Using data inspired by the domestic network of a major Chinese express air cargo carrier, we conduct experiments across multiple seasons. The proposed approach consistently outperforms deterministic benchmarks based on average and minimum flight times, with profit improvements of up to 4.3% and 3.8%, respectively, during the Winter Monsoon season, when variability is highest. Moreover, we show that the value of stochastic planning increases significantly when the network operates under tighter connectivity conditions. Under reduced fleet availability, profit improvements rise to 7.0% and 9.7% relative to the benchmarks based on average and minimum flight times, respectively, highlighting how delay propagation in tightly coupled aircraft rotations makes deterministic plans particularly fragile. Overall, the results demonstrate that anticipating uncertainty at the tactical planning stage improves both operational robustness and revenue performance in large-scale air cargo networks.
This paper presents a constrained calibration framework that reconstructs itinerary-level air cargo flows by disaggregating Origin-Destination (OD) demand across feasible road-air routing options under behavioral and operational assumptions. The model integrates observed leg-level payloads with detailed supply attributes, such as airline network structure, flight schedules, aircraft capacities, and first- and last-mile road-feeder services, to capture hub roles, carrier strategies, transshipment constraints, and catchment area effects. For each OD pair, we generate choice sets of feasible itineraries subject to transfer rules, hub sequencing, airport geography, and journey-time bounds. Itinerary attractiveness is determined by a constant-elasticity term that combines generalized time and schedule depth, and flows are assigned using an attractiveness-based allocation, while ensuring routing feasibility and capacity limits are enforced. Calibration is posed as a scalarized dual-objective non-linear optimization that balances accuracy in observed leg loads (via absolute deviation penalties) against over-allocation of capacity (via hinge penalties), yielding capacity-consistent reconstructions at the network scale. Applied to a large real-world schedule and capacity snapshot, the framework reproduces realistic leg loads and itinerary patterns, delivering interpretable insights, including load factors, hub throughput, transit-time distributions, indirect routing, catchment areas, and network imbalances. In practice, the model functions as a demand-to-itinerary disaggregation layer that (i) can feed downstream optimization, emissions inventories, and policy analysis, or (ii) can be embedded within a joint network-design loop in which capacity, timing, and disaggregation co-evolve. Validation against publicly available leg-level data and robustness analyses support the approach. In the absence of itinerary-level ground truth, results are interpreted as model-implied, feasibility-consistent reconstructions for decision support and scenario testing (e.g., capacity shocks or schedule changes).
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This paper presents a constrained calibration framework that reconstructs itinerary-level air cargo flows by disaggregating Origin-Destination (OD) demand across feasible road-air routing options under behavioral and operational assumptions. The model integrates observed leg-level payloads with detailed supply attributes, such as airline network structure, flight schedules, aircraft capacities, and first- and last-mile road-feeder services, to capture hub roles, carrier strategies, transshipment constraints, and catchment area effects. For each OD pair, we generate choice sets of feasible itineraries subject to transfer rules, hub sequencing, airport geography, and journey-time bounds. Itinerary attractiveness is determined by a constant-elasticity term that combines generalized time and schedule depth, and flows are assigned using an attractiveness-based allocation, while ensuring routing feasibility and capacity limits are enforced. Calibration is posed as a scalarized dual-objective non-linear optimization that balances accuracy in observed leg loads (via absolute deviation penalties) against over-allocation of capacity (via hinge penalties), yielding capacity-consistent reconstructions at the network scale. Applied to a large real-world schedule and capacity snapshot, the framework reproduces realistic leg loads and itinerary patterns, delivering interpretable insights, including load factors, hub throughput, transit-time distributions, indirect routing, catchment areas, and network imbalances. In practice, the model functions as a demand-to-itinerary disaggregation layer that (i) can feed downstream optimization, emissions inventories, and policy analysis, or (ii) can be embedded within a joint network-design loop in which capacity, timing, and disaggregation co-evolve. Validation against publicly available leg-level data and robustness analyses support the approach. In the absence of itinerary-level ground truth, results are interpreted as model-implied, feasibility-consistent reconstructions for decision support and scenario testing (e.g., capacity shocks or schedule changes).
Background: The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. Methods: This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. Results: Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. Conclusions: By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments.
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Background: The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. Methods: This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. Results: Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. Conclusions: By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments.
This book serves as a comprehensive roadmap for navigating the realm of Operations Research (OR). From laying down fundamental mathematical principles to crafting precise modeling techniques and their solution methods, it culminates in a panoramic view of OR models mirroring real-world operations. Delving into diverse applications-from assignment problems to network problems like graph coloring and minimum spanning trees, and navigating through routing problems that are very common in logistics-the book equips readers with practical insights. Each model is accompanied by meticulously detailed examples, seamlessly integrated with hyperlinked codes accessible via an open repository. Moreover, it introduces an engaging dimension with hyperlinks to three serious games replicating some cornerstone OR models, offering a playful yet educational environment for solo or group experimentation.
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This book serves as a comprehensive roadmap for navigating the realm of Operations Research (OR). From laying down fundamental mathematical principles to crafting precise modeling techniques and their solution methods, it culminates in a panoramic view of OR models mirroring real-world operations. Delving into diverse applications-from assignment problems to network problems like graph coloring and minimum spanning trees, and navigating through routing problems that are very common in logistics-the book equips readers with practical insights. Each model is accompanied by meticulously detailed examples, seamlessly integrated with hyperlinked codes accessible via an open repository. Moreover, it introduces an engaging dimension with hyperlinks to three serious games replicating some cornerstone OR models, offering a playful yet educational environment for solo or group experimentation.
Optimizing the routes of firefighting aircraft can lead to better containment of wildfires, hence yielding great environmental and societal value. In this paper, a novel formulation of the Vehicle Routing Problem (VRP) is customized to address the needs of Aerial FireFighting (AFF). The resulting formulation, named Aerial FireFighting Vehicle Routing Problem (AFFVRP), is a capacitated multi-trip VRP with time-windows and hierarchical objectives. The primary objective is to minimize the time of carrying out all requested water drops (to extinguish wildfires quicker), and the secondary objective is to minimize the total flight time. The multi-trip nuance is adopted to be able to model different aircraft types that might require to revisit the depot for after refueling. Because the model is intended to operate as a decision-making tool to support firefighters, users can input the number and types of aircraft available, the location of the airfield, fires, nearest water body, intensity of each fire, etc. Several random cases and case studied based on real wildfires were solved within the expert-recommended time limit of 5 minutes, yielding good-quality solutions in terms of gap optimality. The problem is scalable and sizes ranging from one to 80 water drops were tested and solved within 22 minutes. Strategic fleet planning is also demonstrated in a case study with the use of Monte Carlo simulation, in order to compare the performance of different fleet options for a given setting. Therefore, the model is not only applicable in live situations, but can also be used as a supportive tool in planning for upcoming fire seasons, or reviewing and learning from past fires.
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Optimizing the routes of firefighting aircraft can lead to better containment of wildfires, hence yielding great environmental and societal value. In this paper, a novel formulation of the Vehicle Routing Problem (VRP) is customized to address the needs of Aerial FireFighting (AFF). The resulting formulation, named Aerial FireFighting Vehicle Routing Problem (AFFVRP), is a capacitated multi-trip VRP with time-windows and hierarchical objectives. The primary objective is to minimize the time of carrying out all requested water drops (to extinguish wildfires quicker), and the secondary objective is to minimize the total flight time. The multi-trip nuance is adopted to be able to model different aircraft types that might require to revisit the depot for after refueling. Because the model is intended to operate as a decision-making tool to support firefighters, users can input the number and types of aircraft available, the location of the airfield, fires, nearest water body, intensity of each fire, etc. Several random cases and case studied based on real wildfires were solved within the expert-recommended time limit of 5 minutes, yielding good-quality solutions in terms of gap optimality. The problem is scalable and sizes ranging from one to 80 water drops were tested and solved within 22 minutes. Strategic fleet planning is also demonstrated in a case study with the use of Monte Carlo simulation, in order to compare the performance of different fleet options for a given setting. Therefore, the model is not only applicable in live situations, but can also be used as a supportive tool in planning for upcoming fire seasons, or reviewing and learning from past fires.
Background: Increased maritime trade has led to a surge in drayage operations, causing congestion and environmental issues in port areas. Truck Appointment Systems (TASs) are commonly used to manage truck arrival rates, yet transparency and equity in slot allocation remain problematic, fostering distrust between Licensed Motor Carriers (LMCs) and Marine Terminal Operators (MTOs). Methods: This study proposes a polycentric approach to improve truck scheduling and ensure that those impacted by decisions are involved in the decision-making process. A single-round auction mechanism focused on optimizing the truck hauling process through a pricing policy that promotes sincere bidding is introduced. The proposed approach employs an optimization strategy to achieve equitable coordination in truck synchronization through means of adaptable capacity management. Results: Numerical experiments assessing scenarios of noncollaborative behavior against partial collaboration between MTOs and LMCs demonstrate the effectiveness of the proposed approach in enhancing user satisfaction and terminal conditions for a case study focused on a medium-sized terminal. Collaboration between trucking companies is shown to increase utility per monetary unit spent on slot acquisition. Conclusions: The polycentric strategy offers a solution to TAS limitations by ensuring stakeholder participation with respect to flexibility and transparency by ensuring that those impacted by decisions are involved in the decision-making process.
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Background: Increased maritime trade has led to a surge in drayage operations, causing congestion and environmental issues in port areas. Truck Appointment Systems (TASs) are commonly used to manage truck arrival rates, yet transparency and equity in slot allocation remain problematic, fostering distrust between Licensed Motor Carriers (LMCs) and Marine Terminal Operators (MTOs). Methods: This study proposes a polycentric approach to improve truck scheduling and ensure that those impacted by decisions are involved in the decision-making process. A single-round auction mechanism focused on optimizing the truck hauling process through a pricing policy that promotes sincere bidding is introduced. The proposed approach employs an optimization strategy to achieve equitable coordination in truck synchronization through means of adaptable capacity management. Results: Numerical experiments assessing scenarios of noncollaborative behavior against partial collaboration between MTOs and LMCs demonstrate the effectiveness of the proposed approach in enhancing user satisfaction and terminal conditions for a case study focused on a medium-sized terminal. Collaboration between trucking companies is shown to increase utility per monetary unit spent on slot acquisition. Conclusions: The polycentric strategy offers a solution to TAS limitations by ensuring stakeholder participation with respect to flexibility and transparency by ensuring that those impacted by decisions are involved in the decision-making process.
Flight delays are one of the most discussed, yet not fully understood, topics in the aviation industry. In this paper, we shed more light into propagation of flight delays by providing a spatio-temporal analysis of flight departure delays of the US domestic air network for the year 2017. The analysis focuses on four US air carriers (full-service and low-cost) and two time events characterized by extreme weather conditions, in addition to a baseline case free of extreme weather conditions. We constructed a Delay Propagation Network (DPN) for each (time event, airline) pair detecting patterns of causality between hourly delays in airports using a Granger Causality approach. In addition, we identified four (time event, airline) pairs with a volume of cancellations large enough to construct a Cancellation Propagation Network (CPN), analogously to DPNs. For the baseline case, we observed that central nodes of the airport network (i.e., hubs) usually act as absorbers or intermediary nodes in the DPN. DPNs were more homogeneously distributed in space for point-to-point than for hub-and-spoke networks. For extreme weather events, we observed that the size of a DPN increases with the percentage of canceled flights as long as this stays below 10%. Conversely, it suddenly decreases when the percentage exceeds such tipping point because most causal relationships among delays are lost due to the volume of cancellations. We also observed that some airports located in the region of the extreme weather event were among the central nodes of the DPN. Those airports, together with the hub airports, acted as the top generators, absorbers, or intermediary nodes of the DPN. On the other hand, CPNs monotonously increased in size with the proportion of canceled flights. CPNs are less noisy and therefore easier to interpret than DPNs, as cancellations stem primarily from the extreme weather event only. In CPNs, hubs act as cancellation absorbers, due to the larger volume of resources that airlines allocate there.
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Flight delays are one of the most discussed, yet not fully understood, topics in the aviation industry. In this paper, we shed more light into propagation of flight delays by providing a spatio-temporal analysis of flight departure delays of the US domestic air network for the year 2017. The analysis focuses on four US air carriers (full-service and low-cost) and two time events characterized by extreme weather conditions, in addition to a baseline case free of extreme weather conditions. We constructed a Delay Propagation Network (DPN) for each (time event, airline) pair detecting patterns of causality between hourly delays in airports using a Granger Causality approach. In addition, we identified four (time event, airline) pairs with a volume of cancellations large enough to construct a Cancellation Propagation Network (CPN), analogously to DPNs. For the baseline case, we observed that central nodes of the airport network (i.e., hubs) usually act as absorbers or intermediary nodes in the DPN. DPNs were more homogeneously distributed in space for point-to-point than for hub-and-spoke networks. For extreme weather events, we observed that the size of a DPN increases with the percentage of canceled flights as long as this stays below 10%. Conversely, it suddenly decreases when the percentage exceeds such tipping point because most causal relationships among delays are lost due to the volume of cancellations. We also observed that some airports located in the region of the extreme weather event were among the central nodes of the DPN. Those airports, together with the hub airports, acted as the top generators, absorbers, or intermediary nodes of the DPN. On the other hand, CPNs monotonously increased in size with the proportion of canceled flights. CPNs are less noisy and therefore easier to interpret than DPNs, as cancellations stem primarily from the extreme weather event only. In CPNs, hubs act as cancellation absorbers, due to the larger volume of resources that airlines allocate there.
Journal article
(2023)
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Ziyulong Wang, Ketong Huang, Renzo Massobrio, Alessandro Bombelli, Oded Cats
Network hierarchy describes the relative arrangement of network elements and reflects its fundamental structure. We propose a multi-dimensional topology-based method for quantifying and comparing the extent to which different Public Transport Networks (PTNs) exhibit a hierarchical structure. The proposed method considers the uneven distribution of node importance with different definitions (e.g., degree centrality and betweenness centrality) in a PTN, the clustering of nodes and the node connection patterns. We apply the developed method on 63 high-capacity PTNs worldwide using General Transit Feed Specification (GTFS) data. In addition to global indicators, we use the goodness-of-fit between the probability density function of local indicators and a skew-normal distribution to quantify the extent of PTN hierarchy. Results show that the scale-free network structure and preferential attachment do not vary much across PTNs. In contrast, stop accessibility and traffic intermediacy vary considerably across PTNs as reflected by the closeness centrality and betweenness centrality distributions. Lastly, metro systems exhibit a more hierarchical structure than their tram and Bus Rapid Transit (BRT) counterparts. This work makes a first step towards a better mapping and comparison of different PTNs, which can assist academics and practitioners in better (re)designing and planning the PTNs of the future.
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Network hierarchy describes the relative arrangement of network elements and reflects its fundamental structure. We propose a multi-dimensional topology-based method for quantifying and comparing the extent to which different Public Transport Networks (PTNs) exhibit a hierarchical structure. The proposed method considers the uneven distribution of node importance with different definitions (e.g., degree centrality and betweenness centrality) in a PTN, the clustering of nodes and the node connection patterns. We apply the developed method on 63 high-capacity PTNs worldwide using General Transit Feed Specification (GTFS) data. In addition to global indicators, we use the goodness-of-fit between the probability density function of local indicators and a skew-normal distribution to quantify the extent of PTN hierarchy. Results show that the scale-free network structure and preferential attachment do not vary much across PTNs. In contrast, stop accessibility and traffic intermediacy vary considerably across PTNs as reflected by the closeness centrality and betweenness centrality distributions. Lastly, metro systems exhibit a more hierarchical structure than their tram and Bus Rapid Transit (BRT) counterparts. This work makes a first step towards a better mapping and comparison of different PTNs, which can assist academics and practitioners in better (re)designing and planning the PTNs of the future.
Accessibility is one of the key performance indicators in the evaluation of a multimodal transport system and, as a result, transport planning has become increasingly more oriented towards it. Demand Responsive Transport (DRT) services have been proposed as a measure for increasing accessibility of a Public Transit (PT) network by servicing users in inaccessible areas. Through multimodal planning and coordination, a DRT service can be integrated within the extended PT network and supply the network optimally. In the context of PT users headed toward airports, an integrated DRT service is proposed for those with extended first-mile connections. This service makes use of taxis to transport users to transit points of a dedicated train line supplying a major European airport. Ride-sharing is considered, while optimal order of service and transit points for modal change are determined. To capture the decentralized nature of matching taxis to users, a multi-agent-based algorithm based on Distributed Constraint optimization Problems (DCOPs) is developed. Real-time information about routes and fixed schedules of the PT network are extracted via a dedicated routing Application Programming Interface (API). Experiments validate the applicability of the proposed solution by reporting a decrease in users’ first-mile travel time that is approximately analogous to the modal share the service captures.
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Accessibility is one of the key performance indicators in the evaluation of a multimodal transport system and, as a result, transport planning has become increasingly more oriented towards it. Demand Responsive Transport (DRT) services have been proposed as a measure for increasing accessibility of a Public Transit (PT) network by servicing users in inaccessible areas. Through multimodal planning and coordination, a DRT service can be integrated within the extended PT network and supply the network optimally. In the context of PT users headed toward airports, an integrated DRT service is proposed for those with extended first-mile connections. This service makes use of taxis to transport users to transit points of a dedicated train line supplying a major European airport. Ride-sharing is considered, while optimal order of service and transit points for modal change are determined. To capture the decentralized nature of matching taxis to users, a multi-agent-based algorithm based on Distributed Constraint optimization Problems (DCOPs) is developed. Real-time information about routes and fixed schedules of the PT network are extracted via a dedicated routing Application Programming Interface (API). Experiments validate the applicability of the proposed solution by reporting a decrease in users’ first-mile travel time that is approximately analogous to the modal share the service captures.
The ground handler dock capacitated pickup and delivery problem with time windows
A collaborative framework for air cargo operations
We study a typical problem within the air cargo supply chain, concerning the transportation of standard Unit Load Devices (ULDs) from freight forwarders’ to ground handlers’ warehouses. First, ULDs are picked up by a set of available trucks at the freight forwarders’ premises within a time window. Next, they are delivered to the ground handlers, also within a time window, and discharged according to a Last In First Out (LIFO) policy. Due to space constraints, ground handlers have limited capacity to serve the trucks and waiting times may arise, especially in case freight forwarders do not coordinate their operations. Therefore, in this paper we consider a cooperative framework where this transportation is coordinated by a central planner. The goal of the planner is to find a proper routing and scheduling that minimizes the sum of the transportation and waiting times at the ground handlers’ warehouses, while satisfying the capacity of the trucks. We propose two mathematical formulations, one based on the routing and the other based on the packing aspect of the problem. To solve large instances of the problem, an Adaptive Large Neighborhood Search algorithm is also developed. With numerical experiments, we compare the performances of the two models and the metaheuristic, and we quantify the benefits of the proposed framework to reduce waiting times.
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We study a typical problem within the air cargo supply chain, concerning the transportation of standard Unit Load Devices (ULDs) from freight forwarders’ to ground handlers’ warehouses. First, ULDs are picked up by a set of available trucks at the freight forwarders’ premises within a time window. Next, they are delivered to the ground handlers, also within a time window, and discharged according to a Last In First Out (LIFO) policy. Due to space constraints, ground handlers have limited capacity to serve the trucks and waiting times may arise, especially in case freight forwarders do not coordinate their operations. Therefore, in this paper we consider a cooperative framework where this transportation is coordinated by a central planner. The goal of the planner is to find a proper routing and scheduling that minimizes the sum of the transportation and waiting times at the ground handlers’ warehouses, while satisfying the capacity of the trucks. We propose two mathematical formulations, one based on the routing and the other based on the packing aspect of the problem. To solve large instances of the problem, an Adaptive Large Neighborhood Search algorithm is also developed. With numerical experiments, we compare the performances of the two models and the metaheuristic, and we quantify the benefits of the proposed framework to reduce waiting times.
In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received in a discrete fashion during the booking horizon) and uncertain (for some bookings the three dimensions are not provided, while the actual belly space available for cargo is only revealed a few hours before departure). Hence, analysts base decisions on historical data or human experience, which might yield sub-optimal or infeasible solutions due to the aforementioned uncertainties. We tackle them by proposing data-driven algorithms to predict available cargo space and shipment dimensions. A packing problem is solved sequentially once a new booking request is received, predicting shipment dimensions, if necessary, and considering the uncertainty of such prediction. The booking is accepted if it results in a feasible loading configuration where no previously accepted booking is offloaded. When applied in a deterministic context, our packing method outperformed the one used by the partner airline, increasing the loaded volume up to 20%. The framework was also tested assuming unknown shipment dimensions, comparing a risk-prone and a risk-averse strategy, with the latter accounting for uncertainty in dimension predictions and the former using mean values. While the average loaded volume decreases in the risk-averse case, the number of unplanned offloadings due to under-predicted dimensions decreases from 54% to 12% of the simulated cases, hence yielding a more robust acceptance strategy.
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In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received in a discrete fashion during the booking horizon) and uncertain (for some bookings the three dimensions are not provided, while the actual belly space available for cargo is only revealed a few hours before departure). Hence, analysts base decisions on historical data or human experience, which might yield sub-optimal or infeasible solutions due to the aforementioned uncertainties. We tackle them by proposing data-driven algorithms to predict available cargo space and shipment dimensions. A packing problem is solved sequentially once a new booking request is received, predicting shipment dimensions, if necessary, and considering the uncertainty of such prediction. The booking is accepted if it results in a feasible loading configuration where no previously accepted booking is offloaded. When applied in a deterministic context, our packing method outperformed the one used by the partner airline, increasing the loaded volume up to 20%. The framework was also tested assuming unknown shipment dimensions, comparing a risk-prone and a risk-averse strategy, with the latter accounting for uncertainty in dimension predictions and the former using mean values. While the average loaded volume decreases in the risk-averse case, the number of unplanned offloadings due to under-predicted dimensions decreases from 54% to 12% of the simulated cases, hence yielding a more robust acceptance strategy.
Increased competitiveness with other transport systems and declining operation margins have motivated freight forwarders in the air cargo transport industry to look into horizontal collaboration. This paper focuses on developing a fully integrated five-phase auction-based coopetition model, a form of horizontal collaboration where competition is preserved. A combinatorial auction is used to exchange transportation requests without having to reveal critical company information. Freight forwarders submit requests into an auction pool, where they are grouped into attractive bundles by a central planner and offered for auction. The request selection and bundling procedures are based on the time windows of the request deliveries. A freight forwarder’s bid on each bundle is equal to the marginal profit of that bundle, which is obtained by solving two NP-hard routing problems with a simulated annealing and large neighborhood search meta-heuristic. A unique aspect of the auction is that the dock capacity of the ground handlers is taken into account, which helps to alleviate truck congestion at the ground handlers. The potential of the auction-based coopetition model is shown for an air cargo supply chain scenario. There is a clear increase in profitability for the collaborating freight forwarders because the auction model decreases the transportation costs for the entire coalition. This cost reduction is achieved by an increase in transport efficiency, while the collaboration disadvantages, as seen in the literature, are limited.
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Increased competitiveness with other transport systems and declining operation margins have motivated freight forwarders in the air cargo transport industry to look into horizontal collaboration. This paper focuses on developing a fully integrated five-phase auction-based coopetition model, a form of horizontal collaboration where competition is preserved. A combinatorial auction is used to exchange transportation requests without having to reveal critical company information. Freight forwarders submit requests into an auction pool, where they are grouped into attractive bundles by a central planner and offered for auction. The request selection and bundling procedures are based on the time windows of the request deliveries. A freight forwarder’s bid on each bundle is equal to the marginal profit of that bundle, which is obtained by solving two NP-hard routing problems with a simulated annealing and large neighborhood search meta-heuristic. A unique aspect of the auction is that the dock capacity of the ground handlers is taken into account, which helps to alleviate truck congestion at the ground handlers. The potential of the auction-based coopetition model is shown for an air cargo supply chain scenario. There is a clear increase in profitability for the collaborating freight forwarders because the auction model decreases the transportation costs for the entire coalition. This cost reduction is achieved by an increase in transport efficiency, while the collaboration disadvantages, as seen in the literature, are limited.
Landside operations in the air cargo industry are subject to strongly fluctuating demand with daily peak- and off-peak hours, which result in delays, unreliability and high costs for the entire chain. The purpose of this study is to develop and evaluate (unassisted) off-peak hour pickup and delivery (OHP&D) schemes that are supported by decision-makers and improve the performance. The methodology combines value-focused thinking (VFT) and the Bayesian Best-Worst Method (BWM). VFT ensures that the actor-specific objectives of decision-makers are central throughout the entire decision process. BWM determines the objective weights based on actor-specific sets and evaluations. We find that the risk level is an important evaluation criterion for all actors in this industry and that weights vary significantly across actors. Nine possible OHP&D schemes were generated with the value-based approach. When aggregating the utilities of the individual DMs, the results show that an unassisted OHP&D scheme with dedicated transport, information sharing, a priority lane and Carriage paid to liability agreements is the preferred concept. This concept has the potential to decrease costs up to 65%, driven by a reduction in truck waiting hours of 63%.
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Landside operations in the air cargo industry are subject to strongly fluctuating demand with daily peak- and off-peak hours, which result in delays, unreliability and high costs for the entire chain. The purpose of this study is to develop and evaluate (unassisted) off-peak hour pickup and delivery (OHP&D) schemes that are supported by decision-makers and improve the performance. The methodology combines value-focused thinking (VFT) and the Bayesian Best-Worst Method (BWM). VFT ensures that the actor-specific objectives of decision-makers are central throughout the entire decision process. BWM determines the objective weights based on actor-specific sets and evaluations. We find that the risk level is an important evaluation criterion for all actors in this industry and that weights vary significantly across actors. Nine possible OHP&D schemes were generated with the value-based approach. When aggregating the utilities of the individual DMs, the results show that an unassisted OHP&D scheme with dedicated transport, information sharing, a priority lane and Carriage paid to liability agreements is the preferred concept. This concept has the potential to decrease costs up to 65%, driven by a reduction in truck waiting hours of 63%.
Integrators' global networks
A topology analysis with insights into the effect of the COVID-19 pandemic
In this paper we propose, to the best of our knowledge, the first analysis of the global networks of integrators FedEx, UPS, and DHL using network science. While noticing that all three networks rely on a “hub-and-spoke” structure, the network configuration of DHL leans towards a multi-“hub-and-spoke” structure that reflects the different business strategy of the integrator. We also analyzed the robustness of the networks, identified the most critical airports per integrator, and assessed that the network of DHL is the most robust according to our definition of robustness. Finally, given the unprecedented historical time that the airline industry is facing at the moment of writing, we provided some insights into how the COVID-19 pandemic affected the global capacity of integrators and other cargo airlines. Our results suggest that full-cargo airlines and, much more dramatically, combination airlines were impacted by the pandemic. On the other hand, apart from fluctuations in offered capacity due to travel bans that were quickly recovered thanks to the resilience of their networks, integrators seem to have escaped the early months of the pandemic unscathed.
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In this paper we propose, to the best of our knowledge, the first analysis of the global networks of integrators FedEx, UPS, and DHL using network science. While noticing that all three networks rely on a “hub-and-spoke” structure, the network configuration of DHL leans towards a multi-“hub-and-spoke” structure that reflects the different business strategy of the integrator. We also analyzed the robustness of the networks, identified the most critical airports per integrator, and assessed that the network of DHL is the most robust according to our definition of robustness. Finally, given the unprecedented historical time that the airline industry is facing at the moment of writing, we provided some insights into how the COVID-19 pandemic affected the global capacity of integrators and other cargo airlines. Our results suggest that full-cargo airlines and, much more dramatically, combination airlines were impacted by the pandemic. On the other hand, apart from fluctuations in offered capacity due to travel bans that were quickly recovered thanks to the resilience of their networks, integrators seem to have escaped the early months of the pandemic unscathed.
In this paper, we present a complex network analysis of the air transport network using the air cargo, instead of the passenger, perspective. To the best of our knowledge, this is the first work where a global cargo network comprising passenger airlines, full-cargo airlines, and integrators’ capacity was studied. We used estimated yearly cargo capacity between airport pairs as input to the model. After assessing network characteristics of the sub-networks representing different carrier types, the full network was obtained as a super-imposition of the individual sub-networks. The resulting network has both small-world and scale-free characteristics. Its topological properties resulted in a higher flow imbalance and concentration with respect to its passenger counterpart, with a smaller characteristic path length and diameter. This result is consistent with the larger catchment area of cargo airports, which heavily rely on road feeder services for the ground leg. Finally, we showed how different attack strategies result in hubs of hub-and-spoke systems or airports behaving as bridges between communities being attacked first. We believe this work to be of relevance both for academics and for practitioners in an era where, due to the soaring of e-commerce and next day delivery, new players are entering the air cargo business and competition is constantly increasing.
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In this paper, we present a complex network analysis of the air transport network using the air cargo, instead of the passenger, perspective. To the best of our knowledge, this is the first work where a global cargo network comprising passenger airlines, full-cargo airlines, and integrators’ capacity was studied. We used estimated yearly cargo capacity between airport pairs as input to the model. After assessing network characteristics of the sub-networks representing different carrier types, the full network was obtained as a super-imposition of the individual sub-networks. The resulting network has both small-world and scale-free characteristics. Its topological properties resulted in a higher flow imbalance and concentration with respect to its passenger counterpart, with a smaller characteristic path length and diameter. This result is consistent with the larger catchment area of cargo airports, which heavily rely on road feeder services for the ground leg. Finally, we showed how different attack strategies result in hubs of hub-and-spoke systems or airports behaving as bridges between communities being attacked first. We believe this work to be of relevance both for academics and for practitioners in an era where, due to the soaring of e-commerce and next day delivery, new players are entering the air cargo business and competition is constantly increasing.
Journal article
(2018)
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Alessandro Bombelli, Adria Segarra Torne, Eric Trumbauer, Kenneth D. Mease
A new approach is developed for identifying and approximating well-traveled routes in a historical dataset of flight trajectories. The approximate routes are intended for use in a route-based Eulerian model of air traffic flow for strategic planning but are useful for other route-based strategic planning. The approach involves coarse clustering, outlier detection, fine clustering, and aggregate route construction. Coarse clustering is based on common origin, destination, and average cruise speed. Fine clustering, based on the Fréchet distance between pairs of trajectories, is applied to each coarse cluster to subdivide it, if appropriate. The coarse-clustering step reduces the number of trajectory pairs for which the Fréchet distance must be computed. The number of fine clusters is automatically determined using a combination of three performance indices. Outliers are identified using previously developed methods. The outliers could be discarded or assessed to identify potential routes for avoiding areas that are flight-constrained. The effectiveness of the approach for determining aggregate well-traveled routes is demonstrated on a historical dataset for a domain composed of six centers with a total of 19 airports.
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A new approach is developed for identifying and approximating well-traveled routes in a historical dataset of flight trajectories. The approximate routes are intended for use in a route-based Eulerian model of air traffic flow for strategic planning but are useful for other route-based strategic planning. The approach involves coarse clustering, outlier detection, fine clustering, and aggregate route construction. Coarse clustering is based on common origin, destination, and average cruise speed. Fine clustering, based on the Fréchet distance between pairs of trajectories, is applied to each coarse cluster to subdivide it, if appropriate. The coarse-clustering step reduces the number of trajectory pairs for which the Fréchet distance must be computed. The number of fine clusters is automatically determined using a combination of three performance indices. Outliers are identified using previously developed methods. The outliers could be discarded or assessed to identify potential routes for avoiding areas that are flight-constrained. The effectiveness of the approach for determining aggregate well-traveled routes is demonstrated on a historical dataset for a domain composed of six centers with a total of 19 airports.
Journal article
(2017)
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Alessandro Bombelli, Lluis Soler, Eric Trumbauer, Kenneth Mease
An aggregate route model for strategic air traffic flow management is presented. It is an Eulerian model, describing the flow between segments of unidirectional point-to-point routes. Aggregate routes are created from flight trajectory data based on similarity measures. Spatial similarity is determined using the Fréchet distance and temporal similarity by comparing average ground speeds. The aggregate routes approximate actual traffic patterns. By specifying the model resolution, an appropriate balance between model accuracy and model dimension can be achieved. The dynamics of the traffic flow on the network of aggregate routes take the form of a discrete-time linear time-invariant system. The traffic flow controls are ground holding and predeparture rerouting. Strategic planning, to use the controls to modify the future traffic flow when local capacity violations are anticipated, is posed as an integer linear programming problem of minimizing a weighted sum of flight delays subject to capacity constraints. Two examples demonstrate the model formulation and results of strategic planning. First, ground delays are introduced to manage high demand in the Los Angeles center; second, ground holding and predeparture rerouting are used to manage a convective weather scenario in the same center.
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An aggregate route model for strategic air traffic flow management is presented. It is an Eulerian model, describing the flow between segments of unidirectional point-to-point routes. Aggregate routes are created from flight trajectory data based on similarity measures. Spatial similarity is determined using the Fréchet distance and temporal similarity by comparing average ground speeds. The aggregate routes approximate actual traffic patterns. By specifying the model resolution, an appropriate balance between model accuracy and model dimension can be achieved. The dynamics of the traffic flow on the network of aggregate routes take the form of a discrete-time linear time-invariant system. The traffic flow controls are ground holding and predeparture rerouting. Strategic planning, to use the controls to modify the future traffic flow when local capacity violations are anticipated, is posed as an integer linear programming problem of minimizing a weighted sum of flight delays subject to capacity constraints. Two examples demonstrate the model formulation and results of strategic planning. First, ground delays are introduced to manage high demand in the Los Angeles center; second, ground holding and predeparture rerouting are used to manage a convective weather scenario in the same center.
Conference paper
(2017)
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Alessandro Bombelli, Adria Segarra Torne, Eric Trumbauer, Kenneth Mease
An approach for identifying and approximating well-traveled routes in a historical dataset of flight trajectories is presented. The intent is to use these routes to construct a network model of air traffic flow for use in strategic planning. The flight trajectories are clustered based on spatial and dynamic similarity measures. A coarse clustering process first groups trajectories using origin, destination, and average ground speed information; then a fine clustering process uses the Fréchet distance between pairs of trajectories in the coarse clusters as the more accurate measure of spatial similarity to further divide the trajectories into smaller clusters. The number of resulting clusters is automatically determined by employing a combination of three performance indices. This two-step clustering process has two benefits. It reduces the computational burden because the Fréchet distance computations are required for fewer trajectory pairs due to the initial coarse clustering step. Secondly no manual tuning is required to determine the final number of clusters. A method of detecting and categorizing outliers is presented which fits well with the clustering
process. The clustering and outlier processes are demonstrated on a historical dataset for a region composed of 6 Centers with 21 airports. ...
process. The clustering and outlier processes are demonstrated on a historical dataset for a region composed of 6 Centers with 21 airports. ...
An approach for identifying and approximating well-traveled routes in a historical dataset of flight trajectories is presented. The intent is to use these routes to construct a network model of air traffic flow for use in strategic planning. The flight trajectories are clustered based on spatial and dynamic similarity measures. A coarse clustering process first groups trajectories using origin, destination, and average ground speed information; then a fine clustering process uses the Fréchet distance between pairs of trajectories in the coarse clusters as the more accurate measure of spatial similarity to further divide the trajectories into smaller clusters. The number of resulting clusters is automatically determined by employing a combination of three performance indices. This two-step clustering process has two benefits. It reduces the computational burden because the Fréchet distance computations are required for fewer trajectory pairs due to the initial coarse clustering step. Secondly no manual tuning is required to determine the final number of clusters. A method of detecting and categorizing outliers is presented which fits well with the clustering
process. The clustering and outlier processes are demonstrated on a historical dataset for a region composed of 6 Centers with 21 airports.
process. The clustering and outlier processes are demonstrated on a historical dataset for a region composed of 6 Centers with 21 airports.
Conference paper
(2017)
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Heather Arneson, Alessandro Bombelli, Adria Segarra Torne, Elmer Tse
In response to severe weather conditions, Traffic Managers specify flow constraints and reroutes to route air traffic around affected regions of airspace. Providing analysis and recommendations of available reroute options and associated airspace capacities would assist Traffic Managers in making more efficient decisions in response to convective weather. These recommendations can be developed by examining historical data to determine which previous reroute options were used in similar weather and traffic conditions. This paper
describes the initial steps and methodology used towards this goal. The focus of this work is flights departing from Fort Worth Center destined for New York Center. Dominant routing structures used in the absence of convective weather are identified. A method to extract relevant features from the large volume of weather data available to quantify the impact of convective weather on this routing structure over a given time range is presented. Finally, a method of estimating flow rate capacity along commonly used routes during convective
weather events is described. Results show that the flow rates drop exponentially
as a function of the values of the proposed feature and that convective weather on the final third of the route was found to have a greater impact on the flow rate restriction than other portions of the route. ...
describes the initial steps and methodology used towards this goal. The focus of this work is flights departing from Fort Worth Center destined for New York Center. Dominant routing structures used in the absence of convective weather are identified. A method to extract relevant features from the large volume of weather data available to quantify the impact of convective weather on this routing structure over a given time range is presented. Finally, a method of estimating flow rate capacity along commonly used routes during convective
weather events is described. Results show that the flow rates drop exponentially
as a function of the values of the proposed feature and that convective weather on the final third of the route was found to have a greater impact on the flow rate restriction than other portions of the route. ...
In response to severe weather conditions, Traffic Managers specify flow constraints and reroutes to route air traffic around affected regions of airspace. Providing analysis and recommendations of available reroute options and associated airspace capacities would assist Traffic Managers in making more efficient decisions in response to convective weather. These recommendations can be developed by examining historical data to determine which previous reroute options were used in similar weather and traffic conditions. This paper
describes the initial steps and methodology used towards this goal. The focus of this work is flights departing from Fort Worth Center destined for New York Center. Dominant routing structures used in the absence of convective weather are identified. A method to extract relevant features from the large volume of weather data available to quantify the impact of convective weather on this routing structure over a given time range is presented. Finally, a method of estimating flow rate capacity along commonly used routes during convective
weather events is described. Results show that the flow rates drop exponentially
as a function of the values of the proposed feature and that convective weather on the final third of the route was found to have a greater impact on the flow rate restriction than other portions of the route.
describes the initial steps and methodology used towards this goal. The focus of this work is flights departing from Fort Worth Center destined for New York Center. Dominant routing structures used in the absence of convective weather are identified. A method to extract relevant features from the large volume of weather data available to quantify the impact of convective weather on this routing structure over a given time range is presented. Finally, a method of estimating flow rate capacity along commonly used routes during convective
weather events is described. Results show that the flow rates drop exponentially
as a function of the values of the proposed feature and that convective weather on the final third of the route was found to have a greater impact on the flow rate restriction than other portions of the route.