A disjunctive graph model and framework for constructing new train schedules

The work presented in this paper proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature.

An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

An integrated tabu search algorithm for the lot streaming problem in job shops

A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling

In this paper, we analyze the characteristics of the job shop scheduling problem. A new genetic algorithm for solving the agile job shop scheduling is presented to solve the job shop scheduling problem. Initial population is generated randomly. Two-row chromosome structure is adopted based on working procedure and machine distribution. The relevant crossover and mutation operation is also designed. It jumped from the local optimal solution, and the search area of solution is improved. Finally, the algorithm is tested on instances of 8 working procedure and 5 machines. The result shows that the genetic algorithm has been successfully applied to the job shop scheduling problems efficiency.

/pdf/a-genetic-algorithm-for-job-shop-scheduling-35ykuoz16b.pdf

Peter Šuhel

Papers

A repairing technique for the local search of the job-shop problem