Slot Allocation Research Articles

Allocation of Treatment Slots in Elective Mental Health Care-Are Waiting Lists the Ethically Most Appropriate Option?

Waiting lists are a standard approach to managing excess demand in elective health care. While waiting times are an important policy issue, the ethical validity of the first come, first served (FCFS) principle as such is rarely questioned. Presenting a psychiatric day hospital where all eligible patients have roughly equal claims as a case study, we criticize the reflex use of FCFS for allocation of elective psychiatric care, consider conditions under which this may not be the optimal strategy, and discuss alternatives. We conclude that in our example prioritizing more recent referrals (last come, first served [LCFS]) makes more sense, clinically and ethically. Where several referrals arrive (near-)simultaneously under LCFS, we propose that a higher level of scrutiny be applied to detect possible good reasons for prioritizing one of them. We believe that our observations can be applied to other health care settings that share relevant characteristics with our case.

Dynamic Flexible Allocation of Slots in Container Line Transport

Due to the imbalance between supply and demand, liner container transportation often faces the problem of low slot utilization, which will occur in the shipping process, such as dry container demand exceeding the available dry slots and reefer slots not being fully utilized. This makes it important and challenging to maintain a balance between the actual demand and the limited number of slots allocated for liner container transport. Therefore, this study proposes a flexible allocation method: expanding the types of containers that can be loaded in the same slot. This method is suitable for handling each dynamic arrival container booking request by shipping enterprises, making decisions to accept or reject, and flexibly allocating shipping slots. In order to maximize the total revenue generated by accepting container booking requests during the entire booking acceptance cycle, we establish a dynamic programming model for the flexible allocation of slots. For model solving, we use the Q-learning reinforcement learning algorithm. Compared with traditional heuristic algorithms, this algorithm can improve solving efficiency and facilitate decision-making at the operational level of shipping enterprises. In terms of model performance, examples of different scales are used for comparison and training; the results are compared with the model without flexible allocation, and it is proved that the model proposed in this paper can obtain higher returns than the model without flexible allocation. The results show that the model and Q-learning algorithm can help enterprises solve the problem of the flexible allocation of shipping slots, and thus, this research has practical significance.

Progress and challenges in airport slot allocation

Most of Level 3 airports worldwide face severe congestion and flight delays. Optimal allocating airport slots is one of the most effective ways in airport demand management. Here we systematically review the progress of research in airport slots allocation, as well as the main techniques in setting airport declared capacity and allocating slots.

Flight Arrival Scheduling via Large Language Model

The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabilities and generalization potential of large language models (LLMs). Our method conceptualizes the dynamic scheduling problem for training flight as a language modeling problem, a perspective not previously explored. Specifically, we represent the allocator’s inputs and outputs as language tokens, utilizing LLMs to generate conflict-free results based on a language description of requested landing information and assigned training flight information. Additionally, we employ a reset strategy to create a small dataset for scenario-specific samples, enabling LLMs to quickly learn allocation schemes from the dataset. We demonstrated the capability of LLMs in addressing time conflicts by evaluating metrics such as answer accuracy, conflict rate, and total delay time (without the wrong answer). These findings underscore the feasibility of employing LLMs in the field of air traffic control.

A Novel Medium Access Policy Based on Reinforcement Learning in Energy-Harvesting Underwater Sensor Networks.

Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency.

Analyzing India's National Civil Aviation Policy and Global Trends in Contemporary Civil Aviation Policies

The expansion of the Indian economy is expected to extend to remote regions, and while market-driven processes are ideal for regional air connectivity, there is recognition of the valuable role of public policy in this endeavor. The UDAN Scheme follows a market-oriented approach where operators assess demand, propose routes, and navigate variables like traffic rights, slot allocation, regulatory restrictions, and infrastructure limitations. Global airline industry deregulation has introduced healthy competition, enhancing efficiency and service quality, leading to increased passenger traffic. Studies have highlighted the constructive impact of improved air connectivity on the economic development of regions. Efficient airports are vital economic catalysts, but research suggests that some regional airports operate inefficiently, missing opportunities for profitability. Government subsidies and policies for enhancing regional and remote air connectivity are common practices worldwide, with schemes like EAS in the United State and PSO in Europe bridging the accessibility gap. Research is crucial to understand policy effects on air connectivity and identify factors that improve outcomes. Route dispersal guidelines, such as those in India, can create financial redistribution between route categories, affecting airlines' profitability and serving state capitals over remote regions. Policymakers must find effective ways to enhance air transport to underserved areas where market dynamics might not naturally establish viable transportation links. In summary, this study emphasizes the importance of government-supported policies like UDAN and the role of market-oriented approaches in promoting regional air connectivity. It underlines the need for research to assess policy impacts and identifies the challenges and opportunities in enhancing air connectivity to remote and underserved regions.

On-demand airport slot management: tree-structured capacity profile and coadapted fire-break setting and slot allocation

We propose an on-demand airport slot management (ODASM) approach to guide slot fire-break capacity setup and airport slot allocation. The ODASM consists of a tree-structured capacity profile and a coadapted fire-break setting and slot allocation model. The tree-structured capacity profile that is constructed using a decision tree aims to capture various fire-break capacity settings and their corresponding delays. It provides diversified fire-break schemes and delay references in the airport slot allocation process. The coadapted fire-break setting and slot allocation model are able to generate good coadaptation. That is, the fire-breaks are set to adapt to the preferences of airlines' requests to minimise the total displacements, and the airlines' requests are modified to adapt to the fire-breaks to satisfy a predetermined delay level. The approach has been tested at Shanghai Pudong Airport, and the results suggest that the ODASM can generate a moderate level of rescheduling while resulting in the expected delay that does not exceed a given acceptable delay level. It has been demonstrated that the ODASM can offer substantial advantages in contrast to a classic slot allocation method where the capacity are set as the maximum of the declared capacity. Moreover, due to the refined relationship between fire-break configuration and delay, the computation process of slot optimisation only takes a few minutes, which is completely viable for the application. This approach is compatible with the current slot guidelines while showing significant potential to improve the collaboration among stakeholders in slot management.

A powerful adversary model and corresponding OTP time slot allocation scheme in RIS-assisted physical layer key generation

Physical layer key generation (PLKG) is a technique of information-theoretic security to tackle the problem of key distribution between resource-constrained legitimate users and is a promising candidate for the one time pad (OTP) technique. However, in quasi-static, the key rate is greatly limited due to low entropy. Reconfigurable intelligent surface (RIS) is introduced to adaptively reconfigure the radio environment. However, how to allocate time slots in the OTP to counter the increasingly powerful adversary model is an urgent problem to be solved. In this paper, we propose a very powerful adversary model and give an attack strategy called eavesdropping channel search, which allows Eve to use its search and eavesdropping capabilities to maximize the probability of successful attacks. Meanwhile, we propose a time slot allocation algorithm in the OTP to ensure the security of the key. Simulations validate that our proposed attack strategy is more powerful than any existing adversary model and our proposed time slot allocation algorithm does not have any security loss.

Toward regret-free slot allocation in billboard advertisement

Toward regret-free slot allocation in billboard advertisement

Single point of entry to the European precision cancer medicine trial network PRIME-ROSE.

e23024 Background: Over the last decades, there has been a surge in the development and approval of targeted drugs and immunotherapies for treating cancer patients, as seen by the 757 % increase in approvals by the FDA for new cancer treatments since 2000 (1). This has significantly impacted cancer care and contributed to improving overall survival in various cancer subgroups. However, access to these new treatments is constrained by the market access strategy of the patent-owning company and available knowledge of treatment effects. Only a few treatments have received pan-cancer approval from EMA or FDA, and most drugs receive market authorization per indication. As a result, there is a widening access gap between patients with different cancer types (2). Methods: Exploring the effect of biomarker-driven treatments in new cancer subtypes requires the ability to find patients with rare biomarkers. PRIME-ROSE is a European precision medicine network comprising 11 ongoing or soon-to-start national DRUP-like clinical trials testing registered drugs outside their current label ( www.prime-rose.eu ). Patients with relevant tumor biomarkers are identified and treated with matched drugs available in each trial’s drug portfolio. The ambition is to swiftly and systematically evaluate the effectiveness of approved precision cancer medicines in new indications through pragmatic trial designs and with RWE control cohorts, ensuring expansion into all relevant patient groups to maximize societal benefit. This is particularly relevant for rare cancers, which are enriched in precision medicine trials (3). Results: In PRIME-ROSE, the trials now share and aggregate data to build evidence faster and more effectively impact patient care by addressing key challenges in precision cancer medicine implementation (increasing the recruitment area to 71 million inhabitants). This will significantly reduce the time for filling treatment cohorts and contribute to closing the indication/drug-specific knowledge gap. In fact, several pharmaceutical companies have already shown their interest in and commitment to participating in PRIME-ROSE, as it offers the unique advantage of entering the trials in the network simultaneously (single point of entry) and with a floating allocation of treatment slots between trials, increasing efficiency in finding patients with specific biomarkers to fill treatment cohorts. Conclusions: A unified entry point to the PRIME-ROSE network is feasible and can facilitate building the knowledge base faster for label expansion and/or country-specific approvals/ reimbursement. National multi-stakeholder ecosystems that include pragmatic, RWE-controlled DRUP-like clinical trials may advance precision medicine implementation.

A State-Interactive MAC Layer TDMA Protocol Based on Smart Antennas

Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay.

Learning to allocate: a delay and temperature-aware slot allocation framework for WBAN with TDMA-MAC

Learning to allocate: a delay and temperature-aware slot allocation framework for WBAN with TDMA-MAC

E017 An early inflammatory arthritis questionnaire to enable enhanced triage and a scoping exercise to assess clinical utility

Abstract Background/Aims The quality of early inflammatory arthritis (EIA) referrals from primary care can vary even with a standardised referral form leading to large numbers of non-EIA patients being seen. Poor utilisation of EIA slots places pressure on an EIA service’s ability to see suspected EIA patients within 3 weeks of referral. To overcome this challenge, a questionnaire was devised to enable enhanced EIA triage and a scoping exercise was carried out to assess clinical utility. Methods 50 patients attending the EIA clinic completed the questionnaire (Table 1) either self-administered or via telephone prior to their appointment. Early Inflammatory Arthritis Screening Questionnaire (Table 1) Positive answers were scored 1 with a maximum score of 11. Following their appointment, the patient diagnosis was collected from patient records. Results Of the 50 patients, 14 (28%) had a confirmed EIA diagnosis and the other 36 (72%) patients had a range of alternative diagnoses. In the confirmed EIA group, the scores ranged from 5-8/11 with an average mean score of 7.0 and median of 7. Further analysis showed patients with a score over 5/11 including symptom duration of 12 months and under (Q1) identified all the confirmed EIA patients. Applying the above criteria to our 50 study patients resulted in 27/50 patients qualifying for an EIA appointment slot and 14/27 (52%) would have been confirmed with EIA. After applying these cut off criteria the sensitivity of the questionnaire was 100% and the specificity was 64%. The non-EIA group scores ranged from 0 - 8/11 with an average mean score of 4.9 and median score of 5. Conclusion In our service triage of EIA referrals on a standardised form resulted in poor utilisation of EIA slots. Triage using the questionnaire improved the EIA slot allocation and increased EIA conversion rate from 28% to 50%. This questionnaire appears to be a promising tool to improve utilisation of EIA appointments. Telephone assessment could be delivered by any member of the multidisciplinary team. Disclosure J. Burton: None. R. Sathananthan: None. S. Levy: None. N. Horwood: None. T. Ahmed: None. R. Suresh: None.

A Novel Auction-Based Truck Appointment System for Marine Terminals

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.

Retraction Note: A novel dynamic en-route and slot allocation method based on receding horizon control

Retraction Note: A novel dynamic en-route and slot allocation method based on receding horizon control

Warehouse management optimization using a sorting-based slotting approach

Purpose: Slotting is one of the main operations in warehouse management. It is based on the efficient allocation of slots for stock-keeping units (SKUs). Order picking and slotting represent a high percentage of total logistics costs; therefore, improving these activities leads to significant savings in the overall performance. This paper aims to develop an allocation model integrating SKUs physical variables, warehousing design, and operation (dimensions, layout, material handling equipment), and heterogeneous product demand.Design/methodology/approach: The modeling methodology considers two phases. First, an integer linear programming model for warehousing spaces assignment for SKUs considering priority and required orders is developed. Then, the total operation times using different strategies are calculated.Findings: The effectiveness of the model was verified through simulation using historical data. The results showed that the best performance in the total time of the slotting operation is achieved by using the ABC as a criterion for the classification of the SKUs and by sequentially assigning the row, level, column, and the section.Practical implications: This approach can be adapted to different industrial sectors and serves as a basis for more robust models regarding the number of constraints or the incorporation of additional warehouse operating parameters.Originality/value: The most important contribution of this work is the development of a flexible and adaptable methodology to changes in the operation to improve the efficiency of storage management through slotting. Future work includes other objective functions of sustainable operations and uncertainty treatment techniques.

TMPAD: Time-Slot-Based Medium Access Control Protocol to Meet Adaptive Data Requirements for Trusted Nodes in Fog-Enabled Smart Cities

The popularity of fog-enabled smart cities is increasing due to the advantages provided by modern communication and information technologies, which contribute to an improved quality of life. Wireless networks make them more vulnerable when the network is under malicious attacks that cause a collision in the medium. Furthermore, diverse applications of smart cities demand a contention-free medium access control (MAC) protocol to meet adaptive data requirements. In this work, a time-slot-based medium access control protocol to meet adaptive data requirements (TMPAD) for IoT nodes in fog-enabled smart cities is proposed. TMPAD proposes a trust mechanism to differentiate malicious and legitimate data requests. In addition, it accommodates more legitimate data-requesting nodes to transfer their data during a session by applying the technique for order performance by similarity to ideal solution (TOPSIS) and 0/1 knapsack algorithm. The performance of TMPAD is compared with well-known techniques such as first come first serve (FCFS), shortest job first (SJF), and longest job first (LJF) in different prospective scenarios. The results show that TMPAD scrutinizes more data-requesting nodes in slot allocation, allowing more data transmission in a session, with better mean trust value, as compared to other algorithms.

Energy efficient optimal parent selection with Chameleon Swarm algorithm and non-overlapping backoff algorithm based dynamic slot allocation in wireless body area network

Energy efficient optimal parent selection with Chameleon Swarm algorithm and non-overlapping backoff algorithm based dynamic slot allocation in wireless body area network

Deterministic Scheduling Algorithm Based on Proportional Conflict and Deadline in Industrial Wireless Networks

<p>Deterministic scheduling technology is of great significance for the real-time and deterministic transmission of industrial wireless network data. In view of the fact that the industrial wireless network data stream itself has priority classification attribute, this paper, based on multi-channel time-division multiple access (TDMA) technology, analyzes link conflict delay and channel contention delay caused by high priority data stream to low priority data stream, and performs scheduling preprocessing on the network, so as to eliminate the network with unreasonable parameters, and feedback to the network manager. For preprocessed networks, the scheduling algorithm prioritizes the allocation of time slots and channel resources for links of high priority data streams, while for data streams belonging to the same priority class, a scheduling scheme based on Maximum Proportional Conflict and Deadline First (MPC-D) is proposed. Under the premise of meeting schedulability conditions, time slots and channel resources are allocated sequentially according to the proportional conflict deadline values of each link, from largest to smallest. The experimental results show that the proposed scheduling algorithm can achieve a high network scheduling success rate.</p> <p> </p>

Proactive Hybrid-Granularity Slot Allocation for Flexible Ethernet

Proactive Hybrid-Granularity Slot Allocation for Flexible Ethernet