A multi-objective optimization problem for multi-state series-parallel systems: A two-stage flow-shop manufacturing system

• A redundancy-scheduling optimization problem for a multi-state series parallel system.
• A flow shop with multi-state machines and warm standby redundancy.
• Objectives are to optimize system purchasing cost, makespan and reliability.
• Different test problems are generated and evaluated by a unique genetic algorithm.
• It locates optimal/near optimal solution within a very reasonable time.

This research investigates a redundancy-scheduling optimization problem for a multi-state series parallel system. The system is a flow shop manufacturing system with multi-state machines. Each manufacturing machine may have different performance rates including perfect performance, decreased performance and complete failure. Moreover, warm standby redundancy is considered for the redundancy allocation problem. Three objectives are considered for the problem: (1) minimizing system purchasing cost, (2) minimizing makespan, and (3) maximizing system reliability. Universal generating function is employed to evaluate system performance and overall reliability of the system. Since the problem is in the NP-hard class of combinatorial problems, genetic algorithm (GA) is used to find optimal/near optimal solutions. Different test problems are generated to evaluate the effectiveness and efficiency of proposed approach and compared to simulated annealing optimization method. The results show the proposed approach is capable of finding optimal/near optimal solution within a very reasonable time.