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 4):
The method according to  claim 1 , wherein, in the step  8 , randomly generating a second type of neighborhood individuals X_ 2  of MEK sl (L)  specifically comprises:\n step  81 : randomly selecting two positions of the individual MEK sl (L)  for exchange; and \n step  82 : repeating the step  81  for several times to obtain the individual X_ 2 .

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): 0.3653423364471225
- 35 USC 102 Novelty (BERT): 0.4924743656964288
- Combined Prediction Score: 0.3780555393720531
- Mean Citation Score: 143.822686
- Max Citation Score: 150.9968
- Similarity Product: 91.90207362823487

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