NoSQL database can provide massive, high concurrency, and scalable services for storing different types of data. HBase, a type of NoSQL database, in which columns are grouped into column families, is very suitable for storing semi-structured or unstructured spatial vector data. However, since there are few rules and constraints to be followed for NoSQL database, design of storage schema for spatial data based on NoSQL is difficult. In this paper, based on our early work, an improved Z curve storage schema is proposed for spatial vector data. According to our new schema, row key of a geometric object is the Z curve code of the spatial grids intersected with the geometric object. Moreover, geometric objects with the same row key are stored in a column family. Our proposed method has two features. First, geometric objects adjacent in location are adjacent in physical storage. Second, redundant exists in storage for improving query accuracy. In our experiments, we compare the improved Z curve storage schema with a Quadtree storage schema, a R-tree storage schema and the previous Z curve storage schema. Query response time, memory usage, and query accuracy of spatial query on point and range are used to verify the validity of our proposed method. Experimental results show that the two storage schemas based on Z curve achieves higher query efficiency than the two storage schemas based on tree - the Quadtree storage schema and the R-tree storage schema. More importantly, the query results of the improved Z curve schema are completely correct, while the query results of the previous Z curve schema are not.

In the last 30 years, a flexible job shop scheduling problem (FJSP) has been extensively explored. Production efficiency is a widely utilized objective. With the rise in environmental awareness, green objectives (e.g., energy consumption) have received a lot of attention. Nevertheless, energy consumption has received little attention. Furthermore, controllable processing times (CPT) should be considered in the field of scheduling, because they are closer to some real production. Therefore, this work investigates a FJSP with CPT (i.e., FJSP-CPT) where asymmetrical conditions and symmetrical constraints increase the difficulty of problem solving. The objectives of FJSP-CPT are to minimize simultaneously the maximum completion time (i.e., makespan) and total energy consumption (TEC). First of all, a mathematical model of this multi-objective FJSP-CPT was formulated. To optimize this problem, a novel multi-objective cellular memetic optimization algorithm (MOCMOA) was presented. The proposed MOMOA combined the advantages of cellular structure for global exploration and variable neighborhood search (VNS) for local exploitation. At last, MOCMOA was compared against other multi-objective optimization approaches by performing experiments. Numerical experiments reveal that the presented MOCMOA is superior to its competitors in 15 instances regarding three commonly used performance metrics.

Satellite image classification is essential for many socio-economic and environmental applications of geographic information systems, including urban and...