Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations

• We propose a hybrid genetic algorithm (GA) and tabu search (TS) called HGATS for the FCMRP system.
• We evaluate the performance of HGATS in flexible flow shop with assembly operations based on industrial scale problems.
• HGATS outperforms GA, TS and the existing FCMRP algorithm done in 2014.
• HGATS improves the total cost obtained from the existing algorithm about 31.34% on average.
• HGATS requires a practical computational time when applied to real industrial cases.

The finite capacity material requirement planning system (FCMRP) for industrial scale flexible flow shops is known to be strongly NP-hard. Due to very long computational time, the exact method can be inappropriate for this problem. In this paper, a new hybrid improvement algorithm for the FCMRP system in a flexible flow shop with assembly operations is proposed. The proposed algorithm is a hybrid of genetic algorithm (GA) and tabu search (TS) called HGATS. There are six primary steps in HGATS. In step 1, a production schedule is generated by variable lead-time MRP (VMRP). In step 2, dispatching and random rules are applied to generate initial sequences of orders. From step 3 to step 5, the sequences of orders are iteratively improved by characteristics of TS and GA. Finally, the start times of operations are optimally determined by linear programming. The results show that HGATS outperforms GA, TS and the existing algorithm. Furthermore, HGATS requires a practical computational time when applied to real industrial cases.