PATENT CLAIM ANALYSIS

Application Number: 16006769
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-12
Patent Classification: ["705", "007130"]

Abstract:
The present invention discloses a method and system for collaborative scheduling of production and transportation in supply chains based on improved particle swarm optimization. The method includes the following steps: 1. setting algorithm parameters; 2. randomly generating an initial population; 3. correcting codes; 4. calculating fitness values and updating the speed and the position of particles; 5. performing tournament selection; 6. performing crossover mutation; 7. updating the population; and 8. determining whether a termination condition is satisfied; if so, outputting a globally optimal solution; if not, returning to the step 3. In the present invention, an approximately optimal solution can be obtained in view of the collaborative scheduling problem of production and transportation considering distributed storage, so that the cost is reduced for supply chains and the service level of supply chains is enhanced.

Claim (Index 3):
The method according to  claim 1 , wherein, the step of selecting particles by a \u201ctournament\u201d method, sequencing the particles in non-decreasing order of the fitness values, and selecting first 20% particles to perform a randomly pairwise crossover operation comprises:\n step 41: randomly selecting two individuals, comparing fitness values for the two individuals, and selecting the individual having a larger fitness value as a candidate crossover particle; selecting 20% particles from a population to perform a randomly pairwise crossover operation on the selected particles on the premise of ensuring the number of particles to be an even number, rounding off the number of particles if it is a decimal and adding 1 to the number of particles if it is an odd number, to generate corresponding filial particles to replace parent particles, wherein the specific crossover method is as follows: on the basis of dimensions, randomly generating 0 or 1 for each dimension; if it is 1, exchanging the value of the dimension; or otherwise, not exchanging the value; \n step 42: calculating the mutation probability of a particle m according to the following formula: \n P m = { P 1 - ( P 1 - P 2 ) \ue89e ( f avg - f ) f avg - f min , f \u2264 f avg P 1 , f > f avg where, f min  represents a fitness value for the globally optimal position, f avg  represents an average fitness value, f represents a fitness value for the current particle, P 1 =0.1 and P 2 =0.01; generating a random decimal within [0,1] for each particle and comparing the random decimal with P m ; if it is less than P m , performing mutation on the particle, or otherwise, not performing mutation, where the specific mutation method is as follows: on the basis of dimensions, randomly generating 0 or 1 for each dimension; if it is 1, performing mutation on the value of the dimension to change 1 to 0 or change 0 to 1, or otherwise, not performing mutation.

Metadata:
- Claim Count in Document: 17.0
- Percentile: 94.0
- Lexical Diversity: 1.60638
- Patent Class: 705.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15958932', '14434755', '13262458', '15953881', '15323407']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1036762357759635
- 35 USC 102 Novelty (BERT): 0.5244094011904541
- Combined Prediction Score: 0.1457495523174125
- Mean Citation Score: 188.733076
- Max Citation Score: 216.76024
- Similarity Product: 105.11073497616768

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