PATENT CLAIM ANALYSIS

Application Number: 16127124
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-03
Patent Classification: ["705", "007130"]

Abstract:
The present invention disclose a parallel machine batch scheduling method and system based on an improved artificial bee colony algorithm in a deterioration situation. With this method, a near-optimal solution for the parallel machine batch scheduling problem with deteriorating jobs and maintenance consideration can be obtained. The model of the present invention is derived from an actual production process with considerations of machine maintenance and batching as well as additional processing and maintenance time for jobs and machines over time in actual production. According to the present invention, the settlement of this problem is conducive to providing reliable decision support for the production and maintenance of an enterprise in complex real production conditions, thus reducing enterprise operation costs, increasing enterprise productivity, and promoting building of a modern smart factory of the enterprise.

Claim (Index 6):
The system according to  claim 5 , wherein in the step S3 implemented through loading and execution of the at least one instruction by the at least one processing unit, the process of calculating the fitness value of each nectar source in the solution set comprises:\n step S31, successively assigning the jth job to machine x q j  according to a code rule first, and on each machine, sorting the jobs in a non-decreasing order by the general processing time; step S32, on each machine, arranging first n i - ( \u2308 n i c \u2309 - 1 ) \ue89e c jobs into a first batch and deleting such jobs from a job list; then arranging first c jobs in the remaining list into a batch and deleting such jobs from the job list, and so on, until all jobs in the job list are arranged completely, wherein n i  represents the number of jobs on the ith machine, while c represents the number of jobs that a machine is able to process simultaneously, and \u2308 x y \u2309 represents the smallest integer that is not less than x y ; step S33, setting parameter e=1, indicating that maintenance is started after the completion of processing of the first job; step S34, with regard to each machine, calculating w hi = { ( d i + 1 ) \ue89e ( \u03b2 + 1 ) e - h \ue89e h = 1 , 2 , 3 \ue89e \u2026 , e ( \u03b2 + 1 ) e - h \ue89e h = 1 , 2 , 3 , \u2026 , B i , i = 1 , \u2026 , m , wherein B i  represents the total number of batches on the ith machine; step S35, sorting all batches on each machine in a non-decreasing order by processing time, arranging the first batch in a position with the maximum value of w hi , i.e., arranging the first job to be processed as the hth one, and deleting this batch and w hi  from the list; and repeating this operation until each batch corresponds to the processing order one to one; and step S36, calculating completion time C max j (e) on each machine; let e=e+1, determining whether e\u2264B i  is true; if yes, returning to the step S34; otherwise, confirming on the machine that maintenance is started after the completion of processing of the (L*)th job, wherein L*=Arg max L=1,2, . . . ,B i , {C max i (L)}, i.e., arranging the position with the shortest processing time among all positions to be the position of maintenance, and terminating the algorithm.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 97.0
- Lexical Diversity: 1.7625
- Patent Class: 705.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['12805363', '15958932', '12334561', '13046346', '10918336']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.0888322951832741
- 35 USC 102 Novelty (BERT): 0.4878251807750476
- Combined Prediction Score: 0.1287315837424515
- Mean Citation Score: 138.43728000000002
- Max Citation Score: 167.02881000000005
- Similarity Product: 115.47273668705463

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

Dataset: test