Research On Sequencing Problems For Fabrication-Assembly Hybrid Production Systems

As the development of customization production, more and more mixed-model fabrication-assembly systems are adopted by different manufacturing enterprises, such as in car engine, automobile, air conditioner manufacturing industries, et al., to meet diversified demands of customers without holding large end product inventory. The efficency of these production systems can be improved by two ways. One way is to improve the hardware facilities, for example, by importing more efficient equipments. The shortcomings of this method are that it is expensive and it needs a long period to finish implementation. The other way is to optimize the production sequences. In contrast, the second method is more economic and realistic. In this paper, four typical sequencing problems in car engine mixed-model fabrication-assembly systems are fully addressed.The car engine mixed-model fabrication-assembly systems are analyzed to classify the the typical problems to be considered in this paper, including:scheduling problems in hybrid parts fabrication lines, sequencing problems in mixed-model assembly lines, integrated sequencing problems in mixed-model fabrication-assembly systems and lot sizing and sequencing problems in mixed-model fabrication-assembly systems. And then, the research work in each field is reviewed and the exsiting problems in each field are presented.Then, scheduling problems in hybrid part fabrication flow lines with limited intermediate buffers are considered. The optimization objective is minimizing the makespan. A method, which based on event driven and first available machine rule, is proposed to construct complete schedule and to determine the makespan from the production sequences for the first station. A hybrid algorithm based on genetic algorithm and simulated annealing is proposed to solve the optimization problem, which can balance the algorithm's exploration and exploitation abilities. In this algorithm, two heuristic approaches and a random generation method are adopted together to form the initial population, new selection, crossover and mutation operators are designed. The feasibility and superiority of the hybrid algorithm is shown by comparing with the methods presented in recently published literature for the same optimization problems. And then, the proposed algorithm is used to solve the real scheduling problems in cylinder body, cylinder cover, crank shaft and camshaft fabrication lines, respectively, in the Second Engine Company of Chery Automobile Co., Ltd. All the optimization results obtained by the hybrid algorithm are better than those obtained by the adopted scheduling method in this company.For sequencing problems in mixed-model assembly lines with limited intermediate buffers, two optimization objectives are considered simultaneously:minimizing the variation in parts consumption and minimizing the makespan. The mathematical models are presented. A multi-objective genetic algorithm are proposed to solve the models. In this algorithm, a new fitness value function is presented based on Pareto ranking and sharing function method to evaluate each individual, which can guarantee the individuals' diversity and uniformity in the non-dominated solution set. New initialization method, selection, crossover, mutation operators, and elitist strategy are presented. Using the real production data in the Second Engine Company of Chery Automobile Co., Ltd, the multi-objective genetic algorithm is applied to optimize the production sequences for the mixed-model assembly line. The optimization results are compared to the single objective optimization result obtained by the hybrid algorithm proposed in chapter 2 respectively. The comparison results show that multi-objective genetic algorithm proposed in this chapter is an efficient method for sequencing problems in mixed-model assembly lines.Next, integrated sequencing problems in mixed-model fabrication-assembly systems are investigated. The considered production systems are composed of one mixed-model assembly line with limited intermediate buffers and four parts fabrication lines with limited intermediate buffers and identical parallel machines. The optimization objectives include minimizing the variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication-assembly system. Based on the research efforts in previous chapters, considering the inventory constraints, the integrated optimization framework and mathematical models are proposed. A three-stage method to determine the production sequences for the first stations of all the fabrication lines from the production sequence for the assembly line is put forward. An adaptive multi-objective genetic algorithm is presented for solving the integrated optimization problem. In this algorithm, adaptive crossover probobility and mutation probobility are adopted to perform genetic operations, and new fitness value function is employed to guarantee the solutions' diversity and uniformity. A method based on desirability function is proposed for comparing the non-dominated solution sets obtained by multi-objective optimization algorithms. The optimization results of the adaptive multi-objective genetic algorithm is compared to a multi-objective simulated annealing algorithm by using the real production data in the Second Engine Company of Chery Automobile Co., Ltd. The comparision results indicate that the adaptive multi-objective genetic algorithm performs better than the multi-objective simulated annealing algorithm, satisfactory non-dominated solution sets can be obtained by the adaptive multi-objective genetic algorithm.In order to make full use of production capacity in each scheduling period and to avoid too frequent setups and mistake operations caused by complete mixed-model sequencing results, the lot sizing and sequencing integrated optimization problems in mixed-model fabrication-assembly systems are addressed. The lot sizing and sequencing problems consider consequent three shift production plans in the assembly line simultaneously. The optimization objective is minimizing the production cost during normal worktime, production cost during overtime period, and total holding cost during whole planning period. The optimization mathematical models are proposed. A hybrid algorithm based on genetic algorithm and tabu search is presented to solving the mathematical models. In this algorithm, new encoding method, crossover and mutation operators are designed, and adaptive crossover and mutation probobilities are adopted. The superiority of the hybrid algorithm is shown by comparing with an adaptive genetic algorithm using the same real computation data in the Second Engine Company of Chery Automobile Co., Ltd.For practical applications, the existing problems and the demand in the mixed-model production plan management in the Second Engine Company of Chery Automobile Co., Ltd are analyzed. A mixed-model production plan management software system is designed and developed. The sequencing methods proposed in this thesis are employed in the software system and applied to real production plan management.The research work is concluded and the future research efforts are proposed in the last chapter.