Applying Genetic Algorithm to Optimize Production Scheduling Sequences

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Applying Genetic Algorithm to Optimize Production Scheduling Sequences
By Reinaldo Ragil Rompas

Production scheduling is an important aspect of the manufacturing industry that helps to increase efficiency and reduce costs. Production scheduling requires carefully planning the order in which products are processed on available machines. Key objectives include reducing turnaround time, completion time, and total production costs. Identifying the optimal scheduling sequence increases production efficiency, reduces machine downtime, and boosts a company’s profitability. This article discusses the use of Genetic Algorithms (GA) to determine the optimal production scheduling sequence. We will explain the working principles of GA, its benefits, and how GA can be used for effective production scheduling.

A genetic algorithm (GA) is a method for solving optimization problems that employs a natural selection process analogous to biological evolution. The algorithm iteratively modifies a collection of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to act as parents for the next generation’s offspring. Over time, the population “evolves” toward the best solution. Genetic algorithms are computer search techniques based on evolutionary biology concepts such as cross-over, mutation, and selection.

The complexity of the discrete manufacturing industry’s production workshop prevents traditional genetic algorithms (GA) from solving the production scheduling problem. The simulated annealing algorithm (SAA) is used to create a hybrid genetic algorithm capable of solving production scheduling problems. First, the crossover and mutation probabilities of genetic operations are altered, and the simulation operator anneals with elite replacement operations. Next, genetic operations are compared and replaced with mutation methods to obtain the best value for the current state. Finally, the proposed hybrid genetic algorithm is compared to other scheduling algorithms. The proposed method has been shown to be more efficient and effective in completing production scheduling.

The genetic algorithm for production scheduling begins with an arrangement of chromosomes (schedules) based on production sequence, such as (A, C, E, B, D). The fitness function determines how good a schedule (chromosome) is based on your goals, which are to minimize the total time required to complete all jobs or makespan. The GA utilizes three main operators to guide the population towards better solutions:

  • Crossover: This operator randomly switches segments between the two parental individuals, wishing to produce offspring who inherit both parents’ productive traits.
  • Mutation: Randomly shuffles an individual’s elements, which may introduce new variations into the population, allowing for exploring new areas of solution space and avoid local optima.
  • Selection: A competition selection method that chooses the best from a random subset of participants. This increases the likelihood that better solutions will be passed down to future generations.

Over time, the population develops toward better solutions, as evidenced by increased fitness. GA provides a powerful and adaptable approach to solving optimization problems. It can deal with complex scenarios, find optimal solutions, and adapt to changing conditions because it mimics evolution.