Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints

Abstract

Process planning and scheduling decisions are independently performed in a manufacturing system in traditional approaches. Integration of these two decisions provides significant benefits such as enhancing the productivity of manufacturing resources, lead time reduction, and decreasing total production costs. An approximately 10% improvement has been created in the small problems by this integration. In this research, a mathematical model is proposed to simultaneously make these decisions. Flexible job shop, as a prevalent configuration in production systems, is the supposed configuration in the study in which the feasible process plans are recognized based on the precedence relations between operations. Due to the NP-Hardness of the problem, a solving procedure based on the genetic algorithm is devised so that all the alternative process plans could be considered implicitly. Makespan, critical machine workload, and machines total workload are considered as objective function. These has been combined in a weighted-sum to form an objective function and solved in pareto space. A comparison of the exact solutions with the proposed algorithms (WGA & NSGA II) results confirms the efficiency and the effectiveness of the proposed algorithms in obtaining the final solutions.

Keywords

• Genetic algorithm
• NSGAII
• multi-objective optimization
• process planning and scheduling integration
• precedence constraints