Modeling and Application for Multiobjective Flow-shop Scheduling Using Hybrid Genetic Algorithms

Numerous real-world problems relating to flow-shop scheduling are characterized by combinatorially explosive alternatives as well as multiple conflicting objectives and are denoted as multiobjective combinatorial optimization problems. The problem of multiobjective optimization with setup times in flow shop is considered in this study. The objective function of the problem is minimization of the weighted sum of total completion time, makespan, maximum tardiness and maximum earliness. An integer programming model is developed for the problem which belongs to NP-hard class by using the hybrid genetic algorithm (HGA) to move from local optimal solution to near optimal solution for flow-shop scheduling problems. Small size problems and large size problems can be solved by the proposed integer programming model. Computational experiments are performed to illustrate the effectiveness and efficiency of the proposed HGA algorithm.