Study For Production Sequencing Of Mixed-Model Assembly Lines In Just-In-Time Production Mode

In order to increasing sales and thereby revenues, car-manufacturing enterprises are under constant pressure to produce an increased number of variations or models on a basic end product. Therefore, just-in-time (JIT) production system originated in the Toyota Automobile Corporation has been applied recently in Car-manufacturing enterprises of China.JIT production mode utilizes mixed-model assembly lines to produce, which can combine demands of market with process of manufacture and improves competition of products. Therefore, advantage of JIT production mode can be better developed by studying on mixed-model assembly line. Thanks to difference of producing process and operation time to different product on mixed-model assembly line, production sequencing problem of mixed-model assembly line must be considered, in order to guaranteeing regular work of mixed-model assembly line. Production sequencing problem is focus of study in abroad. In our country, time when car-manufacturing enterprises adopt JIT production mode is late and only large-scale car-manufacturing enterprises have adopted JIT production mode. Wherefore study of production sequencing for mixed-model assembly line is in initial stage.There are three objectives according to classification of Toyota Automobile Corporation in sequencing for mixed-model assembly lines. The first objective is that smooth the workload (assembly time) on workstation of mixed-model assembly line. The second objective is that level (keep constant) the usage rate of every part used on the mixed-model assembly line. The third objective is that minimize stop time of the conveyor. Because the second objective is of a widely research value to car-manufacturing corporations, connecting with production mode of car-manufacturing corporations in China, this paper presents the mathematical model of production sequencing problem focused on the second objective. It is known that a production sequencing problem in mixed-model assembly line falls into NP-hard class of combinatorial optimization problem and thus a large-sized problem may be computationally intractable. As the size of the problem increases, the time of attaining feasible solutions increases in an exponential fashion, making attainment of optimal solutions impractical. In order to solving nondeterministic polynomial problem(NP), people present many approximation methods. But these methods lost general utility because of concentrating on characteristics of special problems, or lost practical utility because of optimal solution quality strongly depending on selection of initial feasible solution. This paper presents an application of solving out large-sized combinatorial optimization problem, especially NP-hard class of combinatorial optimization problem of an approximation method—simulated annealing algorithm to resolve production sequencing problem of mixed-model assembly lines. Simulated annealing algorithm can overcome those shortcomings of the approximation methods as stated above. Simulated annealing algorithm is motivated by an analogy to the thermodynamics of annealing in solids. The solid is first heated to a temperature that permits many molecules to move freely respectively to each other, then it is cooled carefully, slowly, until the material freezes into a crystal, which is completely ordered, and thus the system is at the state of minimum energy. In combinatorial optimization, simulated annealing techniques use an analogous cooling operation for transforming a poor, unordered solution into an ordered, desirable solution, so as to optimize the objective function. Search of simulated annealing algorithm starts from an initial feasible solution. Each solution has a specific cost value. A small change in one or a combination of some variables can generate a new solution with a different cost value. In simulated annealing, the candidate solution is generated randomly. If the cost value of the candidate solution is lower than that of the current solution, a move to the candidate solution is made. However,...