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.