Representative value of cross-fault in the northeastern margin of the Qinghai-Tibet block and case analysis of the 2016 Menyuan Ms6.4 earthquake

The equation for determining cross-fault representative value is calculated based on hanging wall and foot wall reference level surfaces. The cross-fault data reliability are analyzed base on the stability of reference datum and observation points, thereby facili- tating plotting of the representative value curves after removing interference. The spatial and temporal characteristics of fault deformation abnormalities before the 2016 Menyuan Ms6.4 earthquake, as well as the fault-movement characteristics reflected by representa- tive value, are summarized. The results show that many site trends had changed 1-3 years before the Menyuan Ms6.4 earthquake in the Qilian Fault, reflecting certain background abnormalities. The short-term abnormalities centrally had appeared in the 6 months to 1 year period before the earthquake near and in the neighborhood of the source region, demonstrating a significantly increased number of short-term abnormalities. Many sites near and in the neighborhood of the source region had strengthened inverse activities or had changed from positive to inverse activities in the most recent 2-3 years, which reflect stress-field enhancements or adjustment features.

Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.

A Multi-Objective Hybrid Algorithm for the Casting Scheduling Problem with Unrelated Batch Processing Machine

The casting production process typically involves single jobs and small batches,with multiple constraints in the molding and smelting operations.To address the discrete optimization challenge of casting production scheduling,this paper presents a multi-objective batch scheduling model for molding and smelting operations on unrelated batch processing machines with incompatible job families and non-identical job sizes.The model aims to minimise the makespan,number of batches,and average vacancy rate of sandboxes.Based on the genetic algorithm,virus optimization algorithm,and two local search strategies,a hybrid algorithm(GA-VOA-BMS)has been designed to solve the model.The GA-VOA-BMS applies a novel Batch First Fit(BFF)heuristic for incompatible job families to improve the quality of the initial population,adopting the batch moving strategy and batch merging strategy to further enhance the quality of the solution and accelerate the convergence of the algorithm.The proposed algorithm was then compared with multi-objective swarm optimization algorithms,namely NSGA-ll,SPEA-l,and PESA-ll,to evaluate its effectiveness.The results of the performance comparison indicate that the proposed algorithm outperforms the others in terms of both qualityand stability.