公开(公告)号：US20180357584A1

公开(公告)日：2018-12-13

申请号：US16006769

申请日：2018-06-12

Applicant: Hefei University of Technology

Inventor： Xinbao LIU ,  Jun PEI ,  Mei XUE ,  Shaojun LU ,  Hao CHENG ,  Min KONG ,  Zhiping ZHOU ,  Lu JIANG

IPC: G06Q10/06 ,  G06Q10/08

CPC classification number: G06Q10/06311 ,  G06Q10/083

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.

公开(公告)号：US20180356803A1

公开(公告)日：2018-12-13

申请号：US16006743

申请日：2018-06-12

Applicant: Hefei University of Technology

Inventor： Xinbao LIU ,  Jun PEI ,  Lu JIANG ,  Shaojun LU ,  Min KONG ,  Xiaofei QIAN ,  Zhiping ZHOU ,  Mei XUE

IPC: G05B19/418 ,  G06N3/12 ,  G06Q10/04 ,  G06Q50/04

CPC classification number: G05B19/41865 ,  G05B2219/32091 ,  G05B2219/39167 ,  G06N3/126 ,  G06Q10/04 ,  G06Q50/04 ,  Y02P90/30

Abstract: A method and system for batch scheduling uniform parallel machines with different capacities based on an improved genetic algorithm are provided. The method is to solve the batch scheduling problem of uniform parallel machines with different capacities. Jobs are distributed to machines by an improved genetic algorithm, and a corresponding batching strategy and a batch scheduling strategy are proposed according to the natural of the problem to obtain a fitness value of a corresponding individual; then, the quality of the solution is improved by a local search strategy; and, a crossover operation is performed on a population based on the fitness of the solution, and the population is continuously updated by repetitive iteration to eventually obtain an optimal solution.