Application of Interval Theory and Genetic Algorithm for Uncertain Integrated Process Planning and Scheduling

Abstract

Process planning and scheduling are two important parts in intelligent manufacturing system and have great impacts on production efficiency. Integrate them can highly increase the production feasibility and optimality. Researchers have done a lot work on integration of process planning and scheduling (IPPS). But former researchers rarely focused on uncertain environment. In reality many factors can cause the uncertainty of production process time. This paper pioneers in choosing a better solution in uncertain manufacturing environment based on interval theory. The uncertain process time is modeled as interval number. And then, the completion time is also an interval number. Genetic Algorithm (GA) is used to solve this model. The feasibility and effectiveness of the solution have been taken into consideration. The experimental results obtained by increasing the scale of the problem illustrate the proposed method is stable and effective.

Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem

“…Wen et al 43 designed an improved genetic algorithm to settle multi-objective uncertain IPPS model by considering fuzzy processing time. Wang et al 44 used interval numbers to describe the uncertainty of processing time in IPPS problem.…”