Genetic Algorithm For Job Shop Scheduling Research Articles

A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing

The job-shop scheduling problem with operators is a very interesting problem that generalizes the classic job-shop problem in such a way that an operation must be algorithm to solve this problem considering makespan minimization. The genetic algorithm uses permutations with repetition to encode chromosomes and a schedule generation scheme, termed OG&T, as decoding algorithm. This combination guaranties that at least one of the chromosomes represents and optimal schedule and, at the samhat machines and operators are idle while an operation is available to be processed. To improve the quality of the schedules for large instances, we use Lamarckian evolution and modify the OG&T algorithm to further reduce the idle time of the machines and operators, in this case at the risk of leaving all optimal schedules out of the search space. We conducted a large experimental study showing that these improvements allow the genetic algorithm to reach high quality solutions in very short time, and so it is quite competitive with the current state-of-the-art methods.

Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns

The job-shop scheduling problem (JSSP) is considered to be one of the most complex combinatorial optimisation problems. In our previous attempt, we hybridised a Genetic Algorithm (GA) with a local search technique to solve JSSPs. In this research, we propose an improved local search technique, Shifted Gap-Reduction (SGR), which improves the performance of GAs when solving relatively difficult test problems. We also modify the new algorithm for JSSPs with machine unavailability and breakdowns. We consider two scenarios of machine unavailability. First, where the unavailability information is available in advance (predictive) and, secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules.

A Genetic Algorithm for 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.

A New Adaptive Genetic Algorithm for Job-Shop Scheduling

In order to minimize makespan for job-shop scheduling problem (JSP), an improved adaptive genetic algorithm (IAGA) based on hormone modulation mechanism is proposed. This algorithm has characteristics with avoiding overcoming premature phenomenon and slow evolution. The proposed IAGA algorithm is applied to dynamic job-shop scheduling problem (DJSP) and the satisfied result is obtained. By employing the proposed IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently. Therefore it embodies good adaptation to the DJSP (rush order, machine malfunction, and so on).