PATENT CLAIM ANALYSIS

Application Number: 15958932
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-12
Patent Classification: ["700", "101000"]

Abstract:
A method and a system for scheduling parallel machines based on hybrid shuffled frog leaping algorithm and variable neighborhood search algorithm are provided to solve collaborative production and processing of jobs on a plurality of unrelated batch processing machines. The jobs are distributed to machines based on the normal processing time and deterioration situation of the jobs on different machines and are arranged. An effective multi-machine heuristic rule is designed according to the structural properties of an optimal solution for the single-machine problem, and the improved rule is applied to the improved shuffled frog leaping algorithm to solve this problem. The improvement strategy for the traditional shuffled frog leaping algorithm is to improve the local search procedure of the traditional frog leaping algorithm by introducing the variable neighborhood search algorithm. The convergence rate and optimization capacity of the original algorithm are thus improved.

Claim (Index 3):
The method according to  claim 1 , wherein, in the step  7 , randomly generating a first type of neighborhood individuals X_ 1  for one MEK sl (L)  specifically comprises:\n step  71 : randomly generating a vector A={A 1 , . . . , A j , . . . , A n } of a length n, all elements of which is valued from (0, 1), wherein there is a one-to-one correspondence between the elements of the vector A and elements of the individual MEK sl (L) ; \n step  72 : reordering the position of the individual MEK sl (L)  according to a non-descending order of A j ; and \n step  73 : denoting an individual generated by reordering as X_ 1 .

Metadata:
- Claim Count in Document: 11.0
- Percentile: 91.0
- Lexical Diversity: 2.01351
- Patent Class: 700.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15577127', '10603666', '10257913', '14434755', '12851498']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3641353256165655
- 35 USC 102 Novelty (BERT): 0.4973458997483855
- Combined Prediction Score: 0.3774563830297475
- Mean Citation Score: 143.822686
- Max Citation Score: 150.9968
- Similarity Product: 87.61568094139099

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test