Multi-objective flow shop scheduling problem with stochastic parameters: fuzzy goal programming approach

Flow shop scheduling problem with stochastic parameters is dealt with in this paper. A multi‐objective mixed integer linear programming model is proposed in this concern which can generate non‐permutation schedules. To provide a more realistic model, process time and release time are considered stochastic variables with normal distribution. The objective functions are minimising three performance measures including maximum completion time (Makespan), total flow time and total tardiness. Chance constrained programming (CCP) approach and fuzzy goal programming (FGP) are applied to deal with the stochastic parameters and multi‐objective function. Due to the complexity of the problem, we have implemented an adapted genetic algorithm to solve large‐sized problem. According to the computational experiments, the GA can reach good‐quality solutions in reasonable computational time, and can be used to solve large scale problems effectively.