MARCOS approach based upon cubic Fermatean fuzzy set and its application in evaluation and selecting cold chain logistics distribution center

Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients

A hybrid hierarchical fault diagnosis method under the condition of incomplete decision information system

Multi-attribute decision-making (MADM) is a part of management decision-making and an important branch of the modern decision theory and method. MADM focuses on the decision problem of discrete and finite decision schemes. Uncertain MADM is an extension and development of classical multi-attribute decision making theory. When the attribute value of MADM is shown by neutrosophic number, that is, the attribute value is complex data and needs three values to express, it is called the MADM problem in which the attribute values are neutrosophic numbers. However, in practical MADM problems, to minimize errors in individual decision making, we need to consider the ideas of many people and synthesize their opinions. Therefore, it is of great significance to study the method of attribute information aggregation. In this paper, we proposed a new theory—non-dual multi-granulation neutrosophic rough set (MS)—to aggregate multiple attribute information and solve a multi-attribute group decision-making (MGDM) problem where the attribute values are neutrosophic numbers. First, we defined two kinds of non-dual MS models, intersection-type MS and union-type MS. Additionally, their properties are studied. Then the relationships between MS, non-dual MS, neutrosophic rough set (NRS) based on neutrosophic intersection (union) relationship, and NRS based on neutrosophic transitive closure relation of union relationship are outlined, and a figure is given to show them directly. Finally, the definition of non-dual MS on two universes is given and we use it to solve a MGDM problem with a neutrosophic number as the attribute value.

https://www.mdpi.com/2073-8994/11/7/910/pdf

Non-Dual Multi-Granulation Neutrosophic Rough Set with Applications

Determining the appropriate number and position of waypoints on a great circle route (GCR) helps to shorten the sailing distance, reduce the number of course changes, and well-approximate the GCR through a small number of rhumb line (RL) legs. In this study, a genetic algorithm-based method (i.e., the GA method) is proposed to optimize the positions of waypoints on the GCR when the number of waypoints is given. Furthermore, a fuzzy logic-based evaluation method for the number of waypoints (i.e., the FL method) is proposed to judge whether to add a new waypoint or stop the process by using the non-fixed values while considering both the number of waypoints and the remaining benefit of the GCR. According to the example demonstration results, the two methods proposed in this study can well-determine the number and position of waypoints and provide effective support for ocean route planning.

/pdf/optimization-of-waypoints-on-the-great-circle-route-based-on-27asez5h.pdf

Optimization of Waypoints on the Great Circle Route Based on Genetic Algorithm and Fuzzy Logic

With the continuous progress of the chemical industry, warehouse design needs to be diversified on account of the increasing complex and multitudinous perilous chemicals. In this situation, this study projects the conception of the interconnected warehouse. By taking the storage points as the quantity and the path as the variable, this study establishes a quadratic allocation model on the operations of this novel kind of warehouse. Then, an improved neural network algorithm is proposed to ascertain the optimal solution. The innovation of this study is that it releases the space resources of the classic dangerous goods warehouse and improves the operational efficiency of the dangerous goods warehouse under the premise of ensuring safety. Finally, the proposed model and algorithm is tested and verified with a data of Shanghai Lingang dangerous Material Warehouse. The empirical research demonstrates that the interconnected warehouse has ideal performance for lifting the handling efficiency on the basis of ensuring safety.

Design of Interconnected Warehouse and Routing Optimization by BP Genetic Neural Network Algorithm

In the classical simulated annealing algorithm (SAA), the iteration feasible solution is mainly based on a certain random probability. In the process of iteration, there is a lack of comparison between individuals and the whole population of feasible solutions, and the indicators to measure the change of state of individuals are too absolute to achieve the overall control of the algorithm. To deal with uncertain information that individuals encounter in iteration, this study introduces the idea of neutrosophic decision-making and establishes a kind of neutrosophic fuzzy set (NFS) to describe the time-varying iterative state of individuals according to the change of individual state, the change of population state, and the number of iteration. The biggest feature of this study is to propose a neutrosophic simulated annealing algorithm that combines the idea of NFS with simulated annealing. The biggest contribution of this study is to propose a novel entropy measurement method using the NFS and combine it with the simulated annealing algorithm to handle the optimization process. Finally, the effectiveness of the novel algorithm is verified by an example of warehouse optimization.

/pdf/neutrosophic-simulated-annealing-algorithm-and-its-fk882qg0.pdf

Neutrosophic Simulated Annealing Algorithm and Its Application in Operation Optimization in Dangerous Goods Warehouse

With the continuous progress of the chemical industry, warehouse design needs to be diversifed on account of the increasing complex and multitudinous perilous chemicals. In this situation, this study projects the conception of the interconnected warehouse. By taking the storage points as the quantity and the path as the variable, this study establishes a quadratic allocation model on the operations of this novel kind of warehouse. Ten, an improved neural network algorithm is proposed to ascertain the optimal solution. Te innovation of this study is that it releases the space resources of the classic dangerous goods warehouse and improves the operational efciency of the dangerous goods warehouse under the premise of ensuring safety. Finally, the proposed model and algorithm is tested and verifed with a data of Shanghai Lingang dangerous Material Warehouse. Te empirical research demonstrates that the interconnected warehouse has ideal performance for lifting the handling efciency on the basis of ensuring safety.

Research Design of Interconnected Warehouse and Routing Optimization by BP Genetic Neural Network Algorithm

In order to better construct the R - tree index structure and improve the efficiency of R - tree construction and retrieval, the R - tree construction algorithm is optimized from the following three aspects: the selection and determination of the initial center point, the weighted distance between data combined with the actual situation, and the division of redundant data by combining the theories of nearest neighbor, information entropy and probability statistics. The feasibility of the proposed algorithm is verified by comparing the objective function values, iteration times and time consumption under different cluster numbers. Furthermore, experimental results show that the index structure constructed by this method has smaller overlapping area and more efficient query efficiency.

R - Tree Index Construction of Dynamic K-means Algorithm

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