The integrated production-distribution scheduling in parallel machine environment by using improved genetic algorithms
Hamed Kazemi, Mohammad Mahdavi Mazdeh, Mohammad Rostami, Mahdi Heydari
Abstract
This study investigates the integrated production-distribution scheduling in a workshop which utilizes the non-identical parallel machines in production line. The objective is to schedule the jobs on machines and forming them into the batches so as to minimize the sum of tardiness and delivery costs. First, a mixed integer linear programming model is proposed which can solve the small-size instances in a reasonable time. To deal with large-size instances, a genetic algorithm (GA) and the improved genetic algorithm (IGA) equipped with optimal properties mechanism is developed. Areal life instance in bedroom furniture manufacturing is also presented that indicates the algorithm can reach the optimal solution for this problem. In order to evaluate the performance of the algorithms, 150 small size instances and more than 800 large size instances are generated which is conducted from benchmarks. The results indicate that IGA outperforms GA with respect to relative percentage deviations.