PATENT CLAIM ANALYSIS

Application Number: 16127350
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-03
Patent Classification: ["705", "007130"]

Abstract:
The present invention discloses a coordinated production and transportation scheduling method and system based on an improved tabu search algorithm, and a storage medium. The method includes batching jobs, initializing algorithm parameters, generating an initial solution, generating a neighborhood solution set, performing mutation, crossover and selection on individuals, determining a candidate solution set; calculating a fitness value of an individual, updating the candidate solution set; updating a tabu list, and determining whether an algorithm termination condition is satisfied; if yes, outputting the global optimal solution; otherwise, returning to the step 4. The present invention is mainly aimed at the coordinated production and transportation batch scheduling problem with multiple manufacturers. The whole profit of an enterprise in the production and transportation phases can be maximized, and high quality services can be provided for customers of the enterprise with improved core competitiveness of the enterprise.

Claim (Index 1):
A coordinated production and transportation scheduling method based on an improved tabu search algorithm, the method being executed by a computer and comprising:\n S1, sorting all jobs in a job set J={J 1 , . . . , J i , . . . , J n } in non-increasing order by basic processing time, thereby obtaining a sorted job set J\u2032={J\u2032 1 , . . . , J\u2032 i , . . . , J\u2032 n }, wherein J i  represents the ith job in the job set, and J\u2032 i  represents the ith job in the sorted job set; S2, picking out all batches that are capable of holding a first unassigned job from existing batches according to the job set J\u2032, obtaining the batch with the smallest free space from the chosen batches, placing the first unassigned job of J\u2032 into the finally chosen batch, and deleting the job from a job list; S3, repeating the step S2 until all jobs of the job set J\u2032 are assigned to corresponding batches, thereby forming a batch set denoted as B={b 1 , . . . , b d , . . . , b l }, wherein b d  represents the dth batch, and l represents the number of batches; S4, initializing input parameters of the algorithm, the input parameters including number of jobs n, job size s, basic processing time of a job p, number of processing machines m, time needed for a job to arrive at a processing machine r, maximum number of jobs that each batch is capable of holding C and processing speed v, as well as time needed for transporting jobs from each processing machine T; S5, setting execution parameters of the algorithm, the execution parameters including maximum iterations I max , current iteration I=1, crossover probability CR, and initial solution of the algorithm X s ={x 1 , . . . , x d , . . . , x l }, wherein x d  represents that the dth batch is assigned to machine x d , with the global optimal solution X best =X s ; S6, generating a neighborhood solution set N(X s ) according to the initial solution X s , updating individuals in N(X s ), and determining a candidate solution set List(X s ); S7, determining whether List(X s ) is a null set; if yes, performing step S 12 ; otherwise, picking out the optimal individual Y from List(X s ); S8, determining whether the individual Y is better than the global optimal solution X best ; if yes, assigning Y to X best  and performing step S11; otherwise, performing step S9; S9, determining whether the individual Y obtained at step S7 conforms to a tabu rule in a tabu list TSList; if yes, performing step S10; otherwise, performing step S11; S10, updating the candidate solution set List(X s ), moving the individual Y out of the set List(X s ), and returning to the step S7; S11, determining whether a new individual Y is obtained; and if the individual Y is updated, assigning Y to X s ; S12, updating the tabu list TSList according to the initial solution X s ; and S13, assigning I+1 to I, determining whether I\u2264I max  is true, and if yes, returning to the step S6; otherwise, terminating the execution of the algorithm, and outputting a fitness value of the optimal solution X best , a batching scheme of jobs in the job set and an assignment scheme of job batches to each processing machine; wherein setting the initial solution of the algorithm X s ={x 1 , . . . , x d , . . . , x l } in the step S5 comprises: step S51, sorting all batches in the batch set B={b 1 , . . . , b d , . . . , b l } in non-increasing order by processing length, thereby forming a sorted batch set denoted as B\u2032={b\u2032 1 , . . . , b\u2032 d , . . . , b\u2032 i }; step S52, sorting machines in non-increasing order by processing speed, thereby obtaining a sorted machine set M={M 1 , . . . , M k , . . . , M m }, with M k  representing the processing machine which has a processing speed in the kth place and which is numbered as M k ; step S53, defining variables d=1 and k=1; step S54, assigning M k  to x d ; step S55, assigning k+1 to k, and determining whether k\u2264m is true; if yes, performing step S56; otherwise, letting k=1, and performing step S56; and step S56, assigning d+1 to d, and determining whether d\u2264l is true; if yes, returning to the step S54; otherwise, taking X\u2212{x 1 , . . . , x d , . . . , x l } as the initial solution X s .

Metadata:
- Claim Count in Document: 17.0
- Percentile: 97.0
- Lexical Diversity: 1.85227
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['16127337', '15958932', '12558879', '11866484', '11254501']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1089993869492468
- 35 USC 102 Novelty (BERT): 0.5253095059812287
- Combined Prediction Score: 0.150630398852445
- Mean Citation Score: 228.83944599999995
- Max Citation Score: 338.37384
- Similarity Product: 200.35329673770903

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

Dataset: test