Integrated cell formation and part scheduling: A new mathematical model along with two meta-heuristics and a case study for truck industry

Abstract

This paper proposes a new linearized mathematical model to solve the integrated cell formation and job scheduling problem. The model aims to minimize the exceptional elements, voids, and the make-span of the jobs. The results of test problems show that the proposed model is very effiective to obtain the best solutions for small-sized problems in reasonable computation times. However, due to the NP-hard nature of the considered problem, the best solutions couldn't be obtained in acceptable times for largesized test problems, whereas the real-life applications of the problem addressed here are often much larger in size. To meet the requirement of solving larger-sized problems, the Genetic Algorithm (GA), which is today considered one of the artificial intelligence and machine learning techniques, and the Marine Predators Algorithm (MPA) as a new and nature-inspired metaheuristic, are proposed. The success of the algorithms was investigated and compared. The test results reveal the fact that the MPA with optimized parameters has a high potential to solve real life problems. At last, an attempt is made to redesign an existing real-life production system with the proposed algorithms. Eventually, a considerable improvement is obtained in performance compared to the current situation of the system.