Research On Multi-objective Aggregate Production Planning Problem And Algorithm Of Multi-product

Manufacturing industry accounts for a large proportion in the national economy and is an important basis for the development of the national economy.So,manufacturing industry is very important to the development of national economy.With the globalization of economy,the internationalization of market and the increasing of labor cost,the traditional manufacturing industry has encountered the development bottleneck and been facing increasingly fierce competition.Enterprises should to constantly improve their management level,optimize their operation process,and reduce production costs,so as to improve their competitiveness.Aggressive industrial market competition makes enterprises have to face the changing market environment.The diversity of products and fierce competition make the stability of manufacturing industry and supply chain more important than ever before.So,enterprises should be to arrange the production plan reasonably to improve the enterprise's adaptability and reduce the production cost.This thesis develops some deterministic,stochastic and fuzzy multi-objective optimization model for aggregate production planning(APP)problem of multi-product,multi-stage to minimize total production costs and instability in the work force,considering the relationship of raw materials,inventory cost and product demand.Some algorithms are designed to solve the models combined with the local search algorithm based on the minimum cost flow.Furthermore,some experiments are employed in order to validate the performance of the proposed evaluation of design algorithms.The main contents of this thesis include the following aspects.(1)Taking into account the relationship of raw materials,inventory cost and product demand,a deterministic multi-objective optimization model is developed for an aggregate production planning problem of multi-product to minimize total production costs and instability in the work force.Based on the analysis of the feasible range of the planned production and the number of workers in each period,a genetic algorithm(LS-GA)is designed to solve the model combined with the local search algorithm which is designed based on the augmented cycle algorithm.Then the information entropy strategy,NSGA-II strategy and multi population strategy are compared and analyzed with examples.In order to improve the efficiency of solving large-scale multi-objective APP problems,based on the global search ability of particle swarm optimization(PSO)algorithm and the local search ability of LS-GA,a hybrid genetic algorithm-particle swarm optimization based on stages(HGA-PSO1)and multi population strategy(HGA-PSO2).Finally,the performance of the proposed evaluation of multi-objective algorithm is selected to compare and analyze each algorithm.(2)Considering the situation of early or delayed delivery,the impact of early delivery and delayed delivery are analyzed,the method to determine the loss threshold is put forward,the maximum allowable shortage of customers with different tardiness is calculated and the cost of delayed delivery and loss of sales is determined.Considering the production cost,raw material cost,inventory cost,staff cost,backorder cost and lost sales cost,an early/delay multi-objective optimization model is developed for an aggregate APP problem of multi-product to minimize total production costs and instability in the work force.Based on the analysis of the feasible range of the planned production and the number of workers in each period,the LS-GA,HGA-PSO1 and HGA-PSO2 algorithms are designed to solve the model combined.Finally,some test experiments are employed in order to validate the performance of the proposed evaluation of three algorithms.(3)Taking into account the uncertainty of product demand and production capacity caused by external demand and internal capacity,a multi-objective stochastic APP programming model to minimize the stochastic expected opportunity cost and instability in the work force is constructed.Under a certain confidence level,the chance constraint is transformed,and the feasible range of the planned production and the number of workers in each period is determined.The stochastic HGA-PSO1(SHGA-PSO1)and stochastic HGA-PSO2(SHGA-PSO2)algorithms are designed based on LS-GA.The sensitivity of different confidence levels is analyzed by some experiments.(4)Considering that the demand and production capacity of each period are fuzzy variables,under a certain confidence level,a multi-objective fuzzy APP programming model is proposed to minimize the expected opportunity cost and instability in the work force.Under a certain confidence level,the chance constraint is transformed to crisp.Moreover,according to the analysis of the feasible range of the planned production and the number of workers,the fuzzy HGA-PSO1(FHGA-PSO1)and fuzzy HGA-PSO2(FHGA-PSO2)algorithms are designed based on LS-GA.The sensitivity of different confidence levels is analyzed by some experiments.