Slot Assignment Research Articles

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless-powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAV-IRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.

Cognitive radio network architecture for GEO and LEO satellites shared downlink spectrum

The fixed spectrum assignment policy in the space sector and large constellations of Low Earth Orbit (LEO) satellites left little or no spectrum available for future LEO satellite communications services. Cognitive Radio (CR) technology enables spectrum sharing between primary and secondary users without limiting the transmission power, and thus is of great interest to commercial and defense entities. A number of Radio Environment Map (REM) techniques have been making Cognitive Radio Networks (CRNs) practical by constructing a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. In this paper, we investigate spectrum sharing for a network comprised of a Geostationary Orbit (GEO) and a LEO satellite with a multibeam antenna array. A CRN architecture of GEO and LEO satellites shared downlink spectrum is proposed and details are provided covering its architecture, REM structure and channel utilization data aggregation, as well as a frequency slot assignment mechanism.

Patients with Private Insurance Endure Significantly Longer Delays in Time from Intent to CAR-T to Apheresis (Brain-2-Vein) in Multiple Treated Diffuse Large B-Cell Lymphoma: Analysis By Cellular Therapy Intent Quotient (CTIQ)

Introduction: Since FDA approval in 2017, chimeric antigen receptor T-cell (CAR-T) therapy has become an integral part of the treatment algorithm for diffuse large B-cell lymphoma (DLBCL). Despite CD19-modified CAR-T effectiveness in multiple prospective clinical trials, with the transition to a commercial product at an authorized treatment center (ATC) it became apparent that multiple steps additional steps were needed before apheresis for a commercial CAR-T cell product. The workflow for an ATC namely during the B2V time appeared to be significantly different than the clinical trial experience. There remains a dearth of data that evaluates the impact of the B2V time. Intent to pursue CAR-T therapy in the commercial environment has two time periods: Brain-to-Vain (B2V) and Vein-to-Vein (V2V). The B2V time encapsulates referral and evaluation for CAR-T, purchase authorization, when needed negotiation of a single case agreement (SCA), and slot assignment for collection of a cellular product. The time between apheresis and cellular product infusion is known as the V2V period, which is product specific and established from prospective clinical trials. We identified that patients with private insurance or Medicare with managed plans at our institution often require a SCA. A SCA is a contract between the insurance company and the ATC that allows the patient to receive therapy using their in-network benefits. We hypothesized there would be a discrepancy in the B2V time between patients who required a SCA and those who did not. We further hypothesized that patients who did not require a SCA would experience significantly improved treatment outcomes, including a higher percentage achieving the best response of complete response (CR) with a superior prolonged overall survival (OS). Methods: To test these hypotheses, we performed a single-center retrospective study of consecutively evaluated patients with DLBCL who had at least two prior lines of therapy and intended to receive CAR-T therapy with a single CAR-T product at the University of Nebraska from 2018-2022. Patient and disease characteristics were collected. Intent to CAR-T (iCAR-T) was defined as the date of consultation at the ATC to date of apheresis of the product. Cellular therapy intent quotient (CTIQ) was defined as the number of infused with a CAR-T product versus those who intended to go to CAR-T. Those patients who intended to go to CAR-T but did not proceed to apheresis or product infusion were included in the calculation of CTIQ and OS. Results: Of 64 patients who intended to receive CAR-T therapy, 42 required a SCA (66%). Most (39/42) of the patients requiring a SCA had private insurance. All (22/22) of the patients in the non-SCA cohort had public insurance. The was no difference between cohorts based on gender, disease characteristics, distance traveled to ATC, and prior lines of therapy. As expected, the non-SCA cohort was significantly older than the SCA cohort, with mean ages of 69 and 55 respectively (p=0.0001). The CTIQ for the entire group was 94% (60/64). Of the 42 patients who required a SCA before apheresis, 39 (CTIQ– 92.8%) underwent apheresis. Of the 22 patients who did not require a SCA, 21 (CTIQ–95.5%) underwent apheresis. Of the patients who were apheresed, the B2V time was significantly longer for the SCA (42 days; 15-92) compared to the non-SCA cohorts (24 days; range 7-78)(p=0.0005). The average V2V times for the SCA and non-SCA groups were comparable at 29 and 31 days, respectively. All patients who underwent apheresis had their CAR-T cells infused. No significant difference in best response of CR or OS was observed. The best response of CR for the SCA and non-SCA groups was 46% and 57%, respectively (p=0.417). The median OS post-iCAR-T for the SCA group was 19 months, and the median OS for the non-SCA group was 45.2 months (p=0.3327). Conclusion: Patients who require a SCA experienced a significantly longer time to CAR-T apheresis compared to those not requiring a SCA. The longer B2V time acknowledges a period that is often unaccounted for in other trials. Our assessment was limited due to small cohort size and implementation of bridging therapies (results to be presented). Further investigation comparing the CTIQ and B2V time to outcomes with commercial products is warranted to ensure patients are receiving equitable access regardless of insurance type.

An enhanced MSU‐TSCH scheduling algorithms for industrial wireless sensor networks

SummaryThe reliability and latency requirements of wireless sensor network‐based smart grid communications are met in large part by MAC protocols. Developed by IEEE, time slotted channel hopping (TSCH), a method that is effective, dependable, and predictable. However, the TSCH standard does not enable mobility and does not offer any solution for scheduling, especially in IEEE 802.15.4 TSCH, the most recent generation of extremely dependable and low‐power MAC protocols. Recently, MSU‐TSCH a time‐frequency communication schedule was proposed to provide mobility to TSCH. Despite its distinctive qualities, MSU‐TSCH has the drawback of computing the TSCH schedule at each node independently of its traffic load, which can significantly increase the communication delay. Due to this limitation, MSU‐TSCH is not suitable for some delay‐sensitive smart grid applications. In this article, we provide an improved MSU‐TSCH‐based TSCH protocol, called Mobility‐TSCH, that dynamically adjusts time slot assignments based on traffic volume and latency requirement. Moreover, the protocol Mobility‐TSCH adapts to topology changes and supports mobility. Additionally, it optimizes time slot allocation by prioritizing nodes closest to the sink. The performance and evaluation analysis of Mobility‐TSCH compared to the original MSU‐TSCH, reveal that the communication delay is greatly reduced, decreases the average end‐to‐end latency, the high packet delivery ratio (PDR), Increase the performance of both metrics, parent connectivity ratio (PCR) and convergence time (CT), while maintaining a minimal overhead signaling.

Energy Efficient Resource Allocation for UAV-Served Energy Harvesting-Supported Cognitive Industrial M2M Networks

In this letter, the resource allocation problem for Energy Harvesting-supported Cognitive Industrial Machine-to-Machine (EH-CI-M2M) network underlaying Unmanned Aerial Vehicles (UAVs) communication is investigated with the objective of maximizing the average energy efficiency by jointly considering the EH time slot assignment, transmit power control and bandwidth allocation under the constraints of Quality of Service (QoS) and the available energy status of the EH-CMNet devices. Nevertheless, the optimization problem is difficult to be tackled directly since it is non-convex and NP-hard. We first transform the primitive objective problem into a convex form equivalently by non-linear fractional programming and variable relaxation approach. Based on Dinkelbach and Lagrangian theory, an iterative algorithm is proposed to solve the optimization problem. Extensive simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of energy efficiency in different network settings.

Distributed and Efficient Slot Assignment–Alignment Protocol for Resource-Constrained Wireless IoT Devices

Distributed and Efficient Slot Assignment–Alignment Protocol for Resource-Constrained Wireless IoT Devices

Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things

Concurrent communication constitutes one of the challenging issues associated with IoT networks, as it is highly likely that multiple devices may start communication simultaneously. This issue has become more complex as devices belonging to the IoT networks increasingly become mobile. To resolve this issue, various mechanisms have been reported in the literature. However, none of these approaches has considered the neighborhood information of a server module to resolve this issue. In this paper, a neighborhood-based smart slot allocation scheme for the IoT is presented where member devices are mobile. In this scheme, every CH or server module is bound to maintain two different types of slots, i.e., dedicated and reserved. Dedicated slots are assigned to every device on a First-Come-First-Serve (FCFS) basis, whereas reserved slots are assigned to the migrated devices. Additionally, as long as a device Ci is located inside the server module’s coverage area, it is required to use these dedicated slots. Simulation results verified that the proposed neighborhood-based slot allocation scheme performed better than existing approaches and considerably improved various performance metrics, such as 20% in lifetime, 27.8% in slot allocation, and 30.50% in slot waiting time.

Broadcast Scheduling Protocols in Multi-Hop Mobile Ad hoc Networks

When packets are sending in multi-hop mobile unintended networks numerous problems occur like flooding, rebroadcast, broadcast latency, power conservation and collision. If multiple transmission of packets simultaneously in MANETs that using the slot assignments approach, when additional channels are transmitting at the same time as the first slot allocations, interference may occur at the nodes. Because of the multi-hops data transfer, the network performance is hampered by the constrained bandwidth and therefore the self-initiated topological alterations. Therefore, a broadcast algorithm is important within the mobile ad hoc network for collision control and reliable communication. This paper proposes two new broadcasting protocols: modify SRBS and DSB algorithms. The planned algorithms outperform context of efficiency, reliability, traffic overload and reachability in highly mobile networks is an enhanced performance within the different environments. Evaluation of simulation results with other well-known exiting protocols as DFCN and PEGSP algorithms shows that the proposed protocol performance is best within the wireless network and channel bandwidths are well utilized within the network.

Nash equilibrium as a tool for the Car Sequencing Problem 4.0

This paper introduces a new concept to solve car sequencing problem called the Car Sequencing Problem 4.0, focuses the paint shop. The problem of effective car sequencing in the paint shop is caused by the specifics of the production process itself and the structure of the production line. Sequencing of cars as required by the painting process is justified economically. The main goal is to minimize the number of costly changeovers of the painting guns because of color changes and to synchronize those with periodic cleanings, forced by technological requirements. For this purpose, a buffer located in the paint shop is applied. In this paper a game theoretic framework is presented to analyze the problem. Three games are introduced: Buffer Slot Assignment Game–Buffer-OutShuttle Game called the BSAG-BOSG, In–Out Shuttle Game and its modification called modified In–Out Shuttle Game. Based on the simulations performed the efficiency of the algorithms is verified using several datasets.

Recovery of disrupted airline operations using k-maximum matching in graphs

When an aircraft is approaching an airport, it gets a short time interval called a slot by Air Traffic Control that it can use to land. If the landing of the aircraft is delayed for some reason, it loses its slot and Airline Operation Controllers have to assign it a new one. However, landing slots are a scarce resource of the airports and, in order to avoid that an aircraft waits too long, Airline Operation Controllers have to regularly modify the assignment of slots to the aircraft. Due to the system implemented to exchange slots, controllers can modify the slot-assignment using only two kinds of operations: either assign a slot that was free to aircraft A, or give the slot of another aircraft B to A and assign a free slot to B. The problem can then be modelled as follows.Let k≥1 be an odd integer, let G be a graph and M be a matching in G, i.e., a set of pairwise disjoint edges. A k-maximum matching is a largest possible matching that can be obtained from M by using only augmenting paths of length at most k. This problem has already been studied in the context of wireless networks, mainly because it provides a simple approximation for the maximum matching problem. This paper provides a polynomial-time algorithm when k≤3. We then prove that the problem is NP-hard in planar bipartite graphs with maximum degree at most 3 for any odd integer k≥5.

Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

The rapid advancement of Internet of Things (IoT) technology has brought convenience to people’s lives; however further development of IoT faces serious challenges, such as limited energy and shortage of network spectrum resources. To address the above challenges, this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation (SIATS) algorithm. First, an adaptive time slot consisting of periods for sensing, information transmission, and energy harvesting is designed to ensure that the minimum energy harvesting requirement is met while the maximum uplink and downlink throughputs are obtained. Second, the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods, and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized. Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4% compared with that of the fixed time slot allocation (FTS) algorithm. In the case of a large noise variance, the SIATS algorithm has good noise immunity, and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7% compared with that obtained using the FTS algorithm.

Crosstalk-Avoid Virtual Optical Network Embedding Over Elastic Optical Networks With Heterogeneous Multi-Core Fibers

With the advent of the era of the 5th generation fixed networks (F5G), some bandwidth-hungry services such as 4K/8K video, telemedicine, and virtual reality (VR) gaming are emerging, which will bring more challenges to the optical network. Typically, both flexible bandwidth allocation and dynamic services deployment will encounter a bottleneck when huge volumes of access data are flooded. To break it, virtual optical network embedding (VONE) over the space division multiplexing (SDM) based elastic optical network (EON) using multi-core fiber (MCF) is adopted. However, the problem of inter-core crosstalk (IC-XT) between two adjacent cores in an MCF link has not been solved. In this paper, we first design a heterogeneous MCF (HMCF) structure. The IC-XT can be significantly reduced when virtual networks are embedded over EONs with HMCF. Then some integer linear programming (ILP) models are established to formulate the process of VONE over EONs with HMCF. Next, to avoid the high time complexity of ILP, we propose a novel VONE algorithm supporting joint IC-XT-avoid cores and frequency slots assignment in EONs with HMCF. Finally, simulation results demonstrate that the proposed algorithm can effectively improve 72% of the available frequency ratio (AFR) and reduce 35% of the fragmentation ratio (FR) compared with conventional VONE over MCF-based networks without any IC-XT reducing strategy, which squeezes more bandwidth resources.

Incorporating slot valuation in making airport slot scheduling decisions

Airport Slot Allocation (ASA) seeks to manage scarce airport resources by matching airline requests to airport slots. The ASA process is carried out by coordinators, who prioritise airline requests through the consideration of policy rules that aim to serve airlines and the travelling public, improve airport capacity utilisation, connectivity, and competition. Even though the ASA involves both airlines and coordinators, existing studies model the ASA without considering the interactions between airlines’ preferences and the rules and priorities that are considered by the coordinators. This paper introduces an ASA problem variant in which the assignment of airport slots is modelled by considering the interrelation between airlines’ preferences and coordinators’ prioritisation, expressed through time-dependent valuation functions. Through the development of a Mixed Integer Programming Model (MIP) and a preference-based algorithm that integrate the developed valuation functions, we propose request-to-slot assignments that are Pareto optimal per se, ergo guaranteeing that airlines and coordinators have no incentive to reject or alter the proposed schedules. Computational results demonstrate the ability of the proposed MIP and algorithm in supporting coordinators on decisions relating to which requests will be displaced or rejected so as to achieve improved capacity utilisation.

Joint Resource and Trajectory Optimization for Energy Efficiency Maximization in UAV-Based Networks

The explosive growth of unmanned aerial vehicles (UAVs)-based networks has accelerated in recent years. One of the crucial tasks of a UAV-based network is managing and allocating resources, including time, power, fly trajectory, and energy resources. We investigate a UAV-based network that gathers information from smart devices, sensor devices, and IoT devices (IDs) with respect to energy efficiency (EE) maximization. The EE of users served by the UAV over the time slots of a cycle is maximized through three categories: UAV trajectory optimization, power allocation, and time slot assignment. However, these are non-convex problems that are very difficult to solve. To solve the problem efficiently, we divide it step by step and convert the non-convex optimization problem into an equivalent convex optimization problem, optimizing each equivalent problem over each variable while other variables are fixed. Firstly, we perform a UAV trajectory optimization with a different number of ground users. Secondly, the Dinkelbach method is used to construct a non-convex fractional power allocation problem. In addition, we develop an algorithm to distribute time slots to all users, which continually raises the EE value. Eventually, a scheme is provided to sequentially update the method to each equivalent problem. The numerical results provide evidence that by solving the proposed sum-rate maximization problem, the performance of ground users has significantly improved with the support of the UAV-based network.

Joint Resource Allocation on Slot, Space and Power Towards Concurrent Transmissions in UAV Ad Hoc Networks

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

Fair Assignment for Reserved Nucleic Acid Testing

Corona Virus Disease 2019 (COVID-19) is now treating the health of millions of people worldwide. The Chinese government now applies nucleic acid testing as a tool to detect patients from healthy people to control the spread of COVID-19. However, people may come to the nucleic acid testing stations simultaneously, leading to long queues and wasting time. In this paper, we proposed the reserved nucleic acid testing method, which could be easily implemented via Web applications associated with nucleic acid testing. Its key idea is to assign people to different pre-scheduled time slots so that the number of people arriving at a certain time slot can be controlled under the capacity, and thus congestion can be relieved. The key question is how to assign people in a fair manner. We propose a concise model to formalize and analyze the minimum total envy and pairwise fairness assignment problem for a variety of reservation-based applications, including nuclear acid testing. Its objective is to maximize the sum of each person’s utility under the capacity constraints of time slots. The decision variables are the time slot assignment of each person. We show that the envy-freeness solution is usually unavailable. However, we can minimize the total envy through appropriate arrangements and realize pairwise fairness with equal-chance shuffling.

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

PurposeData traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordinationDesign/methodology/approachFair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE.FindingsA multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2.Research limitations/implicationsHowever, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture.Practical implicationsThe individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level.Originality/valueThis study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.

On Airport Time Slot Auctions: A Market Design Complying with the IATA Scheduling Guidelines

The growth in air traffic (before the Covid-19 pandemic) made airport time slots an increasingly scarce resource (and it is believed that this growth will continue after recovery). It is widely acknowledged that the grandfathering schemes used nowadays lead to inefficient allocations and that auctions would be a means to allocate valuable airport time slots efficiently. It has, however, also been pointed out that the design of such slot auctions is challenging due to the various constraints that need to be considered. The present paper proposes a market design for the sales of airport time slots at EU airports that complies with the Worldwide Scheduling Guidelines of the International Air Transport Association (IATA), most notably the reference value systems at level 3 airports. These guidelines need to be considered but lead to significant additional complexity in the market design. Capacity constraints are defined for overlapping time windows, which render the maximum welfare flight scheduling problem NP-hard. Auction formats with good incentive properties such as the Vickrey-Clarke-Groves mechanism or core-selecting auctions require an exact solution to the allocation problem. Given its hardness, it is far from obvious that the allocation problem can be solved to optimality sufficiently fast for practically relevant sizes of real-world problems. We introduce a mathematical model formulation for the maximum welfare flight scheduling problem that complies with all specified IATA constraints and evaluate it on near real-world data sets of flight requests for a full season of a major international airport. We show that the allocation can be computed within minutes and that all the payment computations for the winners can be done in less than two hours on average for realistic problem sizes. The consideration of values of airlines within the proposed auction mechanism leads to significant welfare gains of more than 35% as compared with benchmarks resulting from different standard objectives. These include the maximization of the number of movements, the minimization of the number of movements for which deviations from requested times occur, and the minimization of the total deviation of scheduled from requested times. Whereas the results indicate that auctions can be solved quickly for realistic problem sizes and promise significant welfare gains under the standard independent private values assumptions, the implementation of auctions in the field leads to additional serious challenges. For example, the regulator might have to impose allocation constraints to mitigate the market power of incumbent airlines. In addition, the valuation of slots and the interdependencies of the slot assignment with those at other coordinated airports need careful attention. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grants BI-1057/I-9 and 405008493]. M. Bichler gratefully acknowledges funding by the German National Science Foundation [DFG BI 1057/9-1].

Wide-Sense Nonblocking Converting-Converting Networks with Multirate Connections.

In this paper, we consider a two-stage converting-converting (CC) switching network. This structure can be used, for instance, in switches of elastic optical networks (EONs) or in time-division switches. We propose a new routing algorithm based on fixed slot assignment in interstage links. This algorithm, called Fixed Input–interstage Slot Assignment (FISA), reduces the switching network complexity compared to the rearrangeable (RNB) switching networks of the same structure. We derive the wide-sense nonblocking (WNB) conditions for the switching network controlled by this algorithm. The obtained WNB conditions are the same as those of the RNB, but the switching network does not need troublesome and time-consuming rearrangements. When implementing the proposed switching network structure, we can also reduce the number of tunable full-range spectrum converters and replace part of them with fixed spectrum converters, or even use space switches in the first stage. This is especially important when this architecture is applied in EONs.

Passenger-Centric Slot Allocation at Schedule-Coordinated Airports

Schedule coordination is the primary form of demand management used at busy airports. At its core, slot allocation involves a highly complex combinatorial problem. In response, optimization models have been developed to minimize the displacement of flight schedules from airline requests, subject to physical and administrative constraints. Existing approaches, however, may not result in the best itineraries for passengers. This paper proposes an original passenger-centric approach to airport slot allocation to maximize available itineraries and minimize connecting times. Because of the uncertainty regarding passenger demand, the proposed approach combines predictive analytics to forecast passenger flows in flight networks from historical data and prescriptive analytics to optimize airport slot assignments in view of flight-centric and passenger-centric considerations. The problem is formulated as a mixed-integer nonconvex optimization model. To solve it, we propose an approximation scheme that alternates between flight-scheduling and passenger-accommodation modules and embed it into a large-scale neighborhood search algorithm. Using real-world data from the Singapore Changi and Lisbon Airports, we show that the proposed model and algorithm return solutions in acceptable computational times. Results suggest that slot-allocation outcomes can be made much more consistent with passenger flows at a relatively small cost in terms of flight displacement. Ultimately, this paper provides a new paradigm that can create more attractive flight schedules by bringing together airport-level considerations, airline-level considerations, and, for the first time, passenger-level considerations. Funding: Financial support from the Civil Aviation Authority of Singapore [Project on Airfield Management and Economics] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1165 .