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.

Abstract: The present invention relates to a production batch scheduling method and system based on improved variable neighborhood search and differential evolution algorithms as well as a storage medium. The method is executed by a computer, and includes: setting algorithm parameters; building a neighborhood structure; initializing a population; determining an initial solution; calculating a fitness value; locally searching; selecting a male parent; performing individual recessive variation; regenerating the population; updating the initial solution; updating a neighborhood structure of algorithmic search; and judging whether a terminal condition of algorithm execution is met, outputting a global optimal solution of the algorithmic search if so, otherwise, returning to Step 6. The present invention can obtain an approximate optimal solution in regard to a coordinated batch scheduling problem of production and transportation in the case of a manufacturer single machine based on a differential workpiece.

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.