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 4):
A system for collaborative scheduling of production and transportation in supply chains based on improved particle swarm optimization, comprising:\n a calculation module used for: step 1: obtaining the number of jobs transported by each warehouse; obtaining a sequence of jobs on a first-come-first-served basis; representing a code of the m th  particle by a vector X m ={x 1m , x 2m , . . . , x im , . . . , x nm }, elements of which are valued as 0 or 1; and when an element is valued as 1, putting elements valued as 0 in front of the element into a batch, where all vectors in the n th  dimension are valued as 1; step 2: initializing a particle swarm, determining a population size M of the particle swarm, the dimension D of particles, the number of iterations t=0, the maximum number of iterations t max , and the position x im  and the speed v im  of particles, and assigning 0 or 1 to each dimension of each particle according to an encoding rule; and selecting a random number rand between 0 and 1 and calculating the position and the speed of particles in the following way:\n v im t+1 =w*v im t +c 1 *r 1 *( pb i t \u2212x im t )+ c 2 *r 2 *( gb i t \u2212x im t ) \n where, w, c 1 , c 2  are constant coefficients; Sig \ue8a0 ( v im ) = 1 1 + exp \ue8a0 ( - v im ) x im = { 1 , Sig \ue8a0 ( v im ) \u2265 rand 0 , Sig \ue8a0 ( v im ) < rand ; step 3: correcting an initial solution according to a BU rule, assigning batches to corresponding machines for processing according to an MF batch assignment rule, calculating a fitness value for each particle by a fitness function, and updating an optimal position pb of individuals and a globally optimal position gb; step 4: 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 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; step 5: correcting the initial solution according to the BU rule, assigning batches to corresponding machines for processing according to the MF batch assignment rule, calculating a fitness value for each particle by the fitness function, and updating the optimal position pb of individuals and the globally optimal position gb; step 6: calculating the mutation probability of particles to perform mutation, calculating fitness values for particles according to the BU rule and the MF batch assignment rule, and updating the optimal position pb of individuals and the globally optimal position gb; step 7: calculating the speed and the position of all particles, updating the position x im  of particles, calculating a fitness value for each particle, and updating the optimal position pb of individuals and the globally optimal position gb, where t=t+1; step 8: determining whether t\u2264t max  is satisfied; if so, returning to the step 3; if not, ending the algorithm, and outputting the globally optimal solution gb; an output module used for outputting the optimal batching mode and batch processing sequences on each machine; the step of correcting an initial solution according to a BU rule by the calculation module comprises: step 1\u2032: setting i=1 and j=1 for a given individual X m ; step 2\u2032: assigning a job i to the j th  batch if x im =0, where i=i+1; or otherwise, assuming j=j+1, and assigning the job i to the j th  batch; step 3\u2032: determining whether i>1 is satisfied; if so, returning to the step 2\u2032; if not, obtaining all sets of batches; step 4\u2032: for any batch B j , if the capacity of B j  is greater than B, then: selecting a job J* arriving at a machine at the latest during transportation from a warehouse to a manufacturer and placing the job J* in a batch B j+1  if |B j |\u2212B+|B j+1 |\u2264B, where |B j | represents the number of jobs contained in the batch B j ; or otherwise, inserting a new batch in the position j+1, placing the job J* in the batch B j+1 , and removing the job J* from the batch B j ; where B represents an upper limit for the capacity of each batch; step 5\u2032: repeating the step 4\u2032 until all batches satisfy the requirement on the capacity.

Metadata:
- Claim Count in Document: 17.0
- Percentile: 94.0
- Lexical Diversity: 1.60638
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15958932', '14434755', '13262458', '15953881', '15323407']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.0938209722458507
- 35 USC 102 Novelty (BERT): 0.5138330365911126
- Combined Prediction Score: 0.1358221786803769
- Mean Citation Score: 188.733076
- Max Citation Score: 216.76024
- Similarity Product: 120.0263529185057

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