A genetic algorithm for permutation flow shop scheduling under make to stock production system

• A real-world make-to-stock production system has been studied.
• We modified the existing model for this problem.
• A new genetic algorithm based Real-Time strategy has been proposed to solve the problem.
• Performance of the proposed approach has been tested against the traditional approaches.
• Numerical results illustrate that the proposed approach helps to reduce the production costs.

The permutation flow shop scheduling is a well-known combinatorial optimization problem that arises in many manufacturing systems. Over the last few decades, permutation flow shop problems have widely been studied and solved as a static problem. However, in many practical systems, permutation flow shop problems are not really static, but rather dynamic, where the challenge is to schedule n different products that must be produced on a permutation shop floor in a cyclical pattern. In this paper, we have considered a make-to-stock production system, where three related issues must be considered: the length of a production cycle, the batch size of each product, and the order of the products in each cycle. To deal with these tasks, we have proposed a genetic algorithm based lot scheduling approach with an objective of minimizing the sum of the setup and holding costs. The proposed algorithm has been tested using scenarios from a real-world sanitaryware production system, and the experimental results illustrates that the proposed algorithm can obtain better results in comparison to traditional reactive approaches.