Search Results (3)

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

The engagement of emerging market participants in electricity markets exerts dual influences on price formation mechanisms and operational dynamics. To quantify the impacts on locational marginal prices and stakeholders’ economic interests when EV aggregators (EVAs), cloud energy storage operators (CESSOs), and load aggregators (LAs) collectively participate in market competition, this study develops a bi-level game-theoretic framework for market equilibrium analysis. The proposed architecture comprises two interdependent layers: The upper-layer Stackelberg game coordinates strategic interactions among EVA, LA, and CESSO to mitigate bidding uncertainties through cooperative mechanisms. The lower-layer non-cooperative Nash game models competition patterns to determine market equilibria under multi-agent participation. A hybrid solution methodology integrating nonlinear complementarity formulations with genetic algorithm-based optimization was developed. Extensive numerical case studies validate the methodological efficacy, demonstrating improvements in solution optimality and computational efficiency compared to conventional approaches. Full article

Fifth generation (5G) mobile networks can accomplish enhanced communication capabilities and desired to connect things in addition to people. By means of optimally splitting the spectrum to integrate more efficient segments, mobile operators can deliver better Quality of Services (QoS) for Internet of Things (IoT), even the nowadays so-called metaverse need broadband mobile communication. Drawing on the Theory of Quality Value Transformation, we developed a 5G ecosystem as a sustainable organic coalition constituted of planners, providers, and users. Most importantly, we put forward the altruism as the ethics drive for the organic cooperative evolution to sustain the inclusive sharing economy to solve the problem of the Theory of Games and Economic Behavior. On the top of the collaboration framework for the coalition game for 5G, we adopted Pareto Optimality as the target situation for the optimization via cooperative evolution and further apply ISO 25000 to define the metrics for the value of 5G corresponding to Pareto Frontier. Based on the collaboration framework as above, we conducted a survey to gather the features and costs for the 5G spectrum in relation to IoT and the financial status of the mobile operators as the constraint for the optimization. Taking Simultaneous Multi-Round Auction (SMRA) as the standard rule for spectrum auction, we developed a novel optimization program of two hybrid metaheuristics with the combination of Simulated Annealing (SA), Genetic Algorithm (GA), and Random Optimization (RO) for the multiple objectives of quality, usability, and costs. The results of the simulation show that the coalition game for 5G spectrum auction is a dynamic group decision in which the government authority and mobile operators can achieve a synergy to maximize the profits, quality score, and usability, and minimize the costs. Last but not least, the hybrid metaheuristic with SA and RO is more efficient and effective than that with GA and BO, from the perspective of inclusive sharing economy. It is the first study of its kind as we know. Full article