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 1):
A method for collaborative manufacturing of a plurality of unrelated parallel-batching processing machines based on hybrid shuffled frog leaping algorithm and variable neighborhood search algorithm, comprising:\n step  1 : inputting a normal processing time for each job on each machine, initializing algorithm parameters including a population size N, the number of divided groups S, the current number of iterations L, the maximum number of iterations of RVNS algorithm U max  and the maximum number of iterations L max , and setting L=1; step  2 : initializing positions of all frogs in a population which is denoted by P (L) ={P 1 (L) , . . . , P i (L) , . . . , P N (L) }, where P i (L) ={P i1 (L) , . . . , P ij (L) , . . . , P in (L) } represents the i th  individual in the L th  generation of population P (L)  P ij (L)  represents a position of the individual P i (L)  in the j th  dimension, the j th  position corresponds to the job J j , i=1, 2, . . . N, j=1, 2, . . . n, and n represents the number of jobs; step  3 : calculating a fitness value of each individual in the population P (L) ; step  4 : dividing the population into S groups in average according to the fitness value, with a set of groups denoted by MEK (L) ={MEK 1 (L) , . . . , MEK s (L) , . . . , MEK S (L) }, where MEK s (L) ={MEK s1 (L) , . . . , MEK sl (L) , . . . , MEK s(N/S) (L) )} represents the s th  group in the set, MEK sl (L)  represents the l th  individual in a group MEK s (L) , s=1, 2, . . . S, 1=1, 2, . . . (N/S), and setting s=1; step  5 : calculating the best and the worst individuals in the group MEK s (L) , which are denoted by MEK sbest (L) , and MEK sworst (L) , respectively, randomly selecting one individual MEK sl (L)  from the group MEK s (L)  as an initial solution of the RVNS algorithm, and setting u=1; step  6 : determining whether u\u2264U max  is satisfied, if so, going to a step  9 ; step  7 : randomly generating a first type of neighborhood individuals X_ 1  for one MEK sl (L) , determining whether X_ 1  is better than MEK sl (L) , if so, assigning X_ 1  to MEK sl (L) , assigning u+1 to u, and going to the step  5 ; if not, going to a step  8 ; step  8 : randomly generating a second type of neighborhood individuals X_ 2  for one MEK sl (L) , determining whether X_ 2  is better than MEK sl (L) , if so, assigning X_ 2  to MEK sl (L) , assigning u+1 to u, and going to the step  5 ; if not, going to a step  9 ; step  9 : randomly generating a third type of neighborhood individuals X_ 3  for one MEK sl (L) , determining whether X_ 3  is better than MEK sl (L) , if so, assigning X_ 3  to MEK sl (L) , assigning u+1 to u, and going to the step  5 ; if not, going to a step  10 ; step  10 : determining whether MEK sl (L)  is better than MEK sbest (L) , if so, assigning MEK sl (L)  to MEK sbest (L) ; if not, assigning MEK sl (L)  to MEK sworst (L) ; step  11 : determining whether s is not greater than S, if so, assigning s+1 to s, and going to the step  5 ; and step  12 : determining whether L is not greater than L max , if so, assigning L+1 to L, and going to the step  3 ; if not, ending the algorithm, outputting an execution machine for each job as well as batches and batch processing sequences on each machine.

Metadata:
- Claim Count in Document: 11.0
- Percentile: 91.0
- Lexical Diversity: 2.01351
- Patent Class: 700.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15577127', '10603666', '10257913', '14434755', '12851498']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3373667815169973
- 35 USC 102 Novelty (BERT): 0.4938398253724227
- Combined Prediction Score: 0.3530140859025399
- Mean Citation Score: 143.822686
- Max Citation Score: 150.9968
- Similarity Product: 88.67889100999832

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