Dual-objective job shop scheduling problem using genetic algorithm with local search

Abstract

The Job Shop Scheduling Problem (JSSP) is work allocation on workstations to produce items that have a goal to optimize one or a few objectives. This research aimed to develop a JSSP's model that deals with machines and job sequences by dual objective functions under the new gainful constraints which are involved with the types of skilled workers according to the case study of a steel mill. The proposed methodology of this research is applied with the Memetic algorithm (Genetic algorithm and Local Search technique) and the Pareto optimization. The results generated from the proposed idea can help the manager to decide on the assignment of the right worker to operate the right machine with the right job to achieve the objective values (minimum makespan and maximum average utilization of workers).