Research On The Solution Methods For Multi-objective Integrated Process Planning And Scheduling Problem

Integrated process planning and scheduling is very importmant in manufacturing system. With the development of market economy, manufacturing companies are confronted with more and more drastic competitive environments. In order to improve their competitiveness, managers need to find a trade-off scheduling plan by considering multi-objectives simultaneously. Therefore, this dissertation focuses on IPPS problem under multi-objective circumstance. IPPS is one of the most complicated NP-Complete combinational optimization problems. Moreover, multi-objective IPPS is concerned with optimizing multi-objectives simultaneously, which lead the problem much more complicated. Although a lot of researchers have done many works on IPPS, few of them pay attention on the optimization of multi-objectives simultaneously.The multi-objective IPPS solution strategy has been proposed. The optimization of multi-objective IPPS is conducted firstly, and then the decision process is carried out. During the optimization process of multi-objective IPPS, flexible process planning system provides near-optimal process plans for scheduling system, the non-dominated set of multi-objective IPPS is obtained by multi-objective optimization algorithm. In the decision process of multi-objective IPPS, the final scheduling scheme is selected from non-dominated set of multi-objective IPPS by decision criterions. According to this strategy, the dissertation has utilized the recently presented Honey Bees Mating Optimization (HBMO) algorithm to settle flexible process planning (FPP) problem, multi-objective IPPS problem and multi-objective uncertain IPPS problem. The multi-objective IPPS decision method has also been explored.To solve FPP problem, multi-dimensional encoding strategy has been proposed to deal with the various flexibilities in FPP. HBMO for FPP has been designed based on this encoding strategy, which has included the detailed operators of queen’s mating flight, generation of broods and improvement of broods by workers. Case studies have been carried out to test the performance of the proposed method. The results show that the proposed method is very effective and stable.Based on the aforementioned works, HBMO algorithm for multi-objective IPPS optimization method has been proposed. A novel multi-objective HBMO algorithm has been designed to settle scheduling system. The immune principle has been exploited to maintain the diversity of the population and prevent the premature condition. The fast non-dominated sorting approach has been utilized to maintain the queens’set and drones’ set. Different scale instances have been employed to test the performance of the proposed method. The results show that that the proposed algorithm has achieved good improvement and outperforms the other algorithms.Based on the above research results of multi-objective IPPS, the mathematic model of multi-objective uncertain IPPS problem has been established by fuzzy set theory. The measurements of the uncertainty of the problem have been considered in the model. Then, the HBMO algorithm for multi-objective uncertain IPPS problem has been designed based on the proposed model. Fuzzy operators have been utilized to evaluate fitness, judge the non-dominated relationship and decode the scheduling solution. Different instances have been designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain IPPS.The TOP SIS decision method under combination weight has been designed to settle multi-objective IPPS decision problem. Based on the characters of elements in decision matrix, the normalization operation of decision matrix has been designed. The different weights have been calculated by the characters of different objectives. The proposed method has been used to select a final scheme from the obtained non-dominated set of multi-objective IPPS problem under different preference from decision makers. The results have showed the proposed method is very effective.Based on the aforementioned research results, the method proposed by this dissertation has been conducted in the multi-objective IPPS problem from real-world production process. The result has verified the multi-objective IPPS solution method proposed by this dissertation is very effective.Finally, the research results and innovations of this dissertation are summarized and some further works to be developed in the future are presented.