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Research on Intelligent Algorithm and Its Application to Production Scheduling Problem under Uncertainty

Author: YanShaoBin
Tutor: JiaoBin
School: East China University of Science and Technology
Course: Control Science and Engineering
Keywords: Production scheduling Flow-Shop Job-Shop Collaborative particle swarm optimization The niche mirror sharing mechanism Simulated annealing algorithm Theory of quantum behavior
CLC: TP273
Type: Master's thesis
Year: 2011
Downloads: 189
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Abstract


Production scheduling problem is an important part of the production plan, the core technology of the modern manufacturing management, whose main task under conditions of limited corporate resources to develop a production scheduling program, so that the needs of the target to achieve economic performance optimal. Therefore, reasonable scheduling scheme not only can improve enterprise management level, and can bring significant economic benefits for the enterprise. In addition, the production scheduling problem is usually multi-constrained, multi-objective, random uncertainty optimization problem., Has been proved to belong to the NP-hard problem. The one hand, proposed two improved cooperative particle swarm optimization, including collaborative the Quantum Particle swarm optimization (SACQPSO) based of synergy sharing mechanism of niche mirror particle swarm optimization (NCPSO) and analog annealing the former algorithm niche mirror sharing mechanism adjustment, increased particle retention excellent ability and improve the performance of the convergence of the algorithm, the latter strategy for simulated annealing algorithm is introduced and adaptive mutation strategy to strengthen the global search ability of the algorithm, and by the theory of quantum behavior to group individual fitness change particles update algorithm is more simple and effective. Algorithm on the other hand made system of discrete manufacturing production scheduling problem under certain conditions and under conditions of uncertainty, and the above two cases, the scheduling problem analysis, modeling, and finally the use of improved its optimization. This paper, research results are summarized as follows: (1) describe the production scheduling problems, including the characteristics of the production scheduling problem under certain conditions, the research progress, trends and research methods, as well as the production scheduling problem under uncertainty classification modeling approach Research Progress and scheduling strategy. (2) of the swarm intelligence of particle swarm optimization (PSO) and the theory of co-evolution. Expounded the principle of the standard PSO, and then gives the particle swarm algorithm flowchart, and analysis related to the nature of the particle swarm algorithm, and cooperative coevolutionary algorithm to achieve. At the same time, described the niche mirror shared principles and operation of the mechanism as well as the principle of the simulated annealing algorithm, an important role of the theory of quantum behavior of the particles update and adaptive mutation strategy algorithm. (3) the proposed two improved cooperative particle swarm algorithm is applied to solve the flow shop scheduling problem (FSSP) under certain conditions, to determine the conditions under job shop scheduling problem (JSSP) and standard test function optimization. Collaborative niche mirror particle swarm optimization (NCPSO) solving the FSSP and function optimization results show that the efficiency of NCPSO algorithm. Simulated annealing for solving job - shop schedule problem of collaborative quantum particle swarm optimization (SACQPSO), the simulation results show the feasibility and effectiveness of the new algorithm. (4) Research Flow Shop problem in the workpiece processing time under conditions of uncertainty, and introduce the rough set theory and basic operations, the establishment of a rough time FSSP scheduling model and use it to obtain a clear rough FSSP model, the last niche mirror Cooperative Particle Swarm Optimization (NCPSO), the simulation results show that the scheduling model and the validity of the new algorithm. Longer be studied under conditions of uncertainty Flow Shop earliness / tardiness scheduling problem, fuzzy set theory and basic arithmetic earliness / tardiness units under conditions of uncertainty punish different earliness / tardiness scheduling mathematical model basis on, using fuzzy set theory to establish a fuzzy FSSP scheduling model, clear Flow Shop earliness / tardiness scheduling problem model, collaborative quantum particle swarm optimization (SACQPSO) Finally, using simulated annealing simulation to this problem, the results show that the scheduling model and a new algorithm is reasonable and effective.

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