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The deployment of industrial robots in time‐critical applications demands ultra‐low latency and high reliability in communication systems. This study presents a novel delay optimisation framework for industrial robot control systems using 6G network slicing technologies. A Gale–Shapley (GS)‐based elastic switching model is proposed to dynamically match robot controllers to optimised network slices and base stations under latency‐sensitive conditions. To enhance resource adaptability, a long short‐term memory (LSTM)‐based encoder‐decoder structure is developed for predictive resource allocation across slices. The proposed integrated matching mechanism achieves a success rate of 91.16% for slice access and a base station access rate of 90.83%, outperforming conventional integrated and two‐stage schemes. The LSTM‐based resource allocation achieves a mean absolute error of 0.04 and a violation rate below 10%, with over 92% utilisation of both node and link resources. Experimental simulations demonstrate a consistent end‐to‐end latency below 7 ms and a throughput of 18.4 Mbit/s, validating the proposed models' effectiveness in ensuring robust, real‐time communication for industrial robot operations. This research contributes a scalable solution for dynamic 6G network resource management, providing a foundation for advanced industrial automation and intelligent manufacturing. A novel elastic switching model based on the Gale–Shapley (GS) algorithm and a resource allocation model grounded in an LSTM encoder‐decoder structure, tailored for 6G network slicing scenarios. Through extensive simulations, our model demonstrates a 91.16% slice access success rate, average latency below 5 ms, and resource utilisation exceeding 92%, outperforming conventional integrated matching and static allocation methods.

Artificial intelligence (AI) developments enable human capability to deliver the same outcome at a lower cost. This research performs a high-level matching between AI solutions and challenges within the port area by developing a novel academic approach. This way, the matching is carried out more structured than when one (manager, developer, challenge owner, etc.) fulfils it based on their opinion without following any structured approach. Therefore, the study defines first a comprehensive typology of port stakeholders' challenges, which can be solved via AI solutions. This typology presents challenges, including their main issues, widespread impact, and potential solutions. A state-of-the-art review of AI solutions that can address these challenges is carried out in parallel. Secondly, this review clearly distinguishes between AI solutions based on their technology and functionality. Thirdly, this research selects an appropriate AI solution for addressing each identified challenge in the port operation by upgrading the Gale-Shapley algorithm. Finally, it shows that the most critical presented AI solutions in this study use various machine learning (ML) techniques. Besides, concerning the AI solution's reusability feature and the result of high-level matching, this research shows that the implementation phase effort can be drastically reduced by using the recently developed matching algorithm.

Electric Vehicles (EVs) can be considered as a flexible source of energy which can receive some benefit in terms of incentives for selling their energy. For efficient and economic trading amongst the EV owners, various researchers have proposed a variety of preference based matching algorithms like College Admission Framework (CAF), Max-weight, Merge and Split, Gale-Shapley Algorithm and Brute-Force Algorithm etc, where buyer and seller EVs can exchange energy and receive better payoffs. Unlike the above mentioned algorithms, in this paper the participating entities do not submit the preferences menu (which contains preferred choices of sellers (of a buyer) and of buyers (for a seller)) to a central authority.) However, this paper proposes an assignment energy trading game where no central authority is needed, the matching algorithm is hosted on cloud which matches charging and discharging of EVs based on their aspiration level and bids. The contribution of the work is to deduce the bids and aspiration level of charging and discharging EVs which is not considered in any of the existing work. Another contribution of the work is the behavioral assignment game that eludes the need of integer linear programming problem and deduces the convergence of game by adjustments of aspiration levels. Futhermore, the entire algorithm is cloud hosted with no middleman hence trading EVs identities are concealed from each other making the system unbiased. The proposed game helps both the buyer and the seller side of EVs to achieve their best bids as well as by reducing grid dependency it boosts the profit margin of the charging stations (CS).

We consider a two-sided market under incomplete preference lists with ties, where the goal is to find a maximum size stable matching. The problem is APX-hard, and a 3/2-approximation was given by McDermid [1]. This algorithm has a non-linear running time, and, more importantly needs global knowledge of all preference lists. We present a very natural, economically reasonable, local, linear time algorithm with the same ratio, using some ideas of Paluch [2]. In this algorithm every person make decisions using only their own list, and some information asked from members of these lists (as in the case of the famous algorithm of Gale and Shapley). Some consequences to the Hospitals/Residents problem are also discussed.

In response to growing environmental concerns and regulatory pressures, reducing carbon emissions in maritime transport has become a priority. Shipping companies face the challenge of balancing profitability objectives with the imperative to minimize their environmental footprint. This study addresses the tramp ship routing and scheduling problem by incorporating the carbon intensity indicator (CII) into the optimization framework. A bi-objective optimization model is developed, with two objective functions aimed at maximizing fleet profit and improving CII ratings. The Gale–Shapley algorithm is employed to achieve stable vessel–cargo matching, and the genetic algorithm is adopted for iterative optimization. This computational study, based on real historical data, verifies the effectiveness of the proposed model and algorithm. The results demonstrate notable improvements in fleet efficiency and environmental performance, increasing profitability by 4.38% while maintaining favorable CII ratings. The findings provide valuable theoretical guidance for shipping companies navigating increasingly stringent CII regulations.

The rapid advancement of unmanned aerial vehicle (UAV) technology has led to its widespread adoption in military reconnaissance, disaster monitoring, environmental inspection, and related fields. However, a single UAV often faces limitations when executing large-scale and complex missions. UAV swarm technology, which employs multi-agent collaboration, can significantly improve task execution efficiency and overall system performance, representing an area of considerable research importance. Current studies on task allocation and path planning for UAV swarms exhibit certain shortcomings, particularly the high computational complexity and insufficient real-time performance of existing path planning methods when applied to highly dynamic, multi-objective, and large-scale complex scenarios. To address the above challenge, this paper proposes a Gale-Shapley-based Genetic Algorithm (GSGA) for UAV swarm task allocation and path planning. First, a multi-UAV data inspection system model is formulated based on an energy consumption model, analyzing the influence of factors including geographical fairness, data utility, and energy consumption. The proposed GSGA integrates the Gale-Shapley stable matching algorithm for one-to-one task assignment between UAVs and sub-regions with a genetic algorithm optimized for intra-region path planning. Dynamic programming is further employed to refine the flight paths. The results show that the GSGA strategy can effectively improve the balance of task allocation, optimize path length and inspection quality. The proposed method demonstrated robust performance in complex scenarios characterized by numerous task targets and intricate regional partitions, consistently enabling UAVs to complete inspection tasks with high collaborative efficiency.

We study changes in educational homogamy in the US and four European countries over the decade covering the Great Recession. The marital preferences identified point to the widening of the social gap between different educational groups since these preferences have increased the inclination of the individuals to match with others of similar educational traits in all five countries. We obtain this finding with an aggregate measure characterizing revealed preferences of individuals in relationship. We apply a novel approach for validating our finding: we compare our aggregate measure with dating data informative about the reservation points not only of those people who will be in a couple, but also those who will remain single. Finally, we challenge a commonly held view: we argue that marital preferences should not be blamed for the documented increase of the social gap since preferences are not exogenous, but are shaped by changes in the employment prospects of the potential partners.

Since the pioneering work of Gale and Shapley, the stable marriage problem has received wide treatment by researchers due to its elegance and applicability. The original problem has been generalized and well studied from different angles, and many algorithms have been proposed for the solution of many variants of the traditional formulation. This short note tries to shed some more light on the original algorithm by providing a more direct proof of its correctness.

We study strategic issues in the Gale-Shapley stable marriage model. In the first part of the paper, we derive the optimal cheating strategy and show that it is not always possible for a woman to recover her women-optimal stable partner from the men-optimal stable matching mechanism when she can only cheat by permuting her preferences. In fact, we show, using simulation, that the chances that a woman can benefit from cheating are slim. In the second part of the paper, we consider a two-sided matching market found in Singapore. We study the matching mechanism used by the Ministry of Education (MOE) in the placement of primary six students in secondary schools, and discuss why the current method has limited success in accommodating the preferences of the students, and the specific needs of the schools (in terms of the \"mix\" of admitted students). Using insights from the first part of the paper, we show that stable matching mechanisms are more appropriate in this matching market and explain why the strategic behavior of the students need not be a major concern.

The radio resource allocation is one of the most important issues to achieve effective wireless communication. In Long Term Evolution (LTE) network, single carrier frequency division multiple access (SC-FDMA) is applied as the transmission technology for uplink traffic. Most researches focus on maximizing the system throughput of SC-FDMA under changeable channel condition. However, users may require different quality of services (QoS) for different applications. This paper studies radio resource allocation for QoS users in localized SC-FDMA system. The proposed scheme divides allocation process into matching algorithm and radio resource assignment algorithm. The Gale–Shapley algorithm is applied to find the optimal matching between resource blocks (RB) and user equipment (UE) by considering channel conditions and the desired QoS. Then the resource assignment algorithm heuristically allocates bandwidth to UE by referring the matched RB under the constraint of carrier continuity. This paper modified the Recursive Maximum Expansion (RME) algorithm to effectively assign radio resource for UEs with different bandwidth demands. The performance of our proposed scheme is compared with the modified RME scheme through exhaustive simulations. The video streaming, VoIP, and FTP traffic types were adopted for simulations. Our simulation results show that the proposed scheme achieves better QoS satisfaction and system throughput than the RME-modified scheme.