公开(公告)号：US20180357610A1

公开(公告)日：2018-12-13

申请号：US16006694

申请日：2018-06-12

Applicant: Hefei University of Technology

Inventor： Xinbao LIU ,  Jun PEI ,  Min KONG ,  Zhanhui WEI ,  Shaojun LU ,  Qingru SONG ,  Xiaofei QIAN

IPC: G06Q10/10 ,  G06Q10/08

CPC classification number: G06Q10/1093 ,  G06F7/58 ,  G06Q10/083

Abstract: A method and system for collaborative scheduling of production and transportation based on shuffled frog leaping and path relinking algorithms. The method includes the following steps: 1. setting algorithm parameters; 2. generating an initial population; 3. calculating fitness values; 4. grouping the population; 5. performing local search on all groups and updating individuals in groups; 6. performing global search on all groups and updating individuals in groups; 7. gathering all groups to obtain a new population; 8. performing a greedy path relinking algorithm on elegant solutions in the population to obtain an updated population; and, 9. determining termination conditions of algorithms; if so, ending; or otherwise, returning to the step 3.

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.