Design of Mixed-Model Assembly Lines Integrating New Energy Vehicles

The automotive industry is undergoing a transformational period where more and more new energy vehicles (NEVs) are being produced and delivered to the market. Accordingly, some new challenges arise during the manufacturing process for car companies. Since the mixed-model assembly line has been widely used, how to integrate the NEVs into the existing assembly system that was designed for the production of gasoline cars is a key issue. A practical approach assigning a specific workforce to handle NEV assembly work is applied at the BMW assembly shop. This work studies this new production pattern and focuses on the design of the assembly system under this pattern. This work aims to develop a method for minimizing the production cost of NEV assembly. Thus, an exact algorithm for hierarchically solving the assembly line balancing problem and vehicle model sequencing problem is proposed. Mixed integer programming mathematical models that describe these two problems are formulated for the first time. Three new benchmark problems and one industry case that include the NEV models are created to evaluate the effectiveness of the proposed method. Results of numerical tests demonstrate that the developed algorithm can quickly generate reconfiguration solutions of the assembly line for various model mix scenarios and production rates. High flexibility of the manufacturing system can be obtained using the proposed approach.

A linear physical programming model for assembly line balancing problem

This paper deals with the mixed-model assembly line balancing problem. This type of line is applied to more than one similar model of a product in an intermixed order. Despite their widespread use, these lines have received little attention in the literature. Metaheuristics, heuristics and mathematical programming techniques are developed to solve these types of assembly line balancing problems. However, linear physical programming method has never used. In this paper, a linear physical programming model is proposed for balancing a mixed-model assembly line. The performance of the proposed model is applied to a numerical example to analyze the usage of the methodology. Five objectives are considered in the model and the outperformance of the methodology is demonstrated by comparing it to a different approach. According to the results, it has been seen that the proposed linear physical programming model is practical and useful approach for mixed-model assembly line balancing problems.

A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

In manufacturing systems, mixed model assembly lines are used to produce different products to deal with the problem of customers’ demands variety, and minimizing the cycle time in such assembly line is a critical problem. This paper addresses the mixed model assembly line balancing problem type 2 that consists in finding the optimal cycle time for a given number of workstations. A hybrid Greedy randomized adaptive search procedure-Genetic algorithm is proposed to find the optimal assignment of tasks among workstations that minimize the cycle. A Ranked Positional Weight heuristic is used in the construction phase of the proposed GRASP, and in the local search phase, a neighborhood search procedure is used to ameliorate the constructed solutions in the construction phase. The GRASP is executed many times in order to seed the initial population of the proposed genetic algorithm, and the results of the executions are compared with the final solutions obtained by the hybrid GRASP-GA. In order to test the proposed approaches, a numerical example is used.

Real-Time Scheduling of Mixed Model Assembly Line with Large Variety and Low Volume Based on Event-Triggered Simulated Annealing (ETSA)

In the contemporary industrial production, multiple resource constraints and uncertainty factors exist widely in the actual job shop. It is particularly important to make a reasonable scheduling scheme in workshop manufacturing. Traditional scheduling research focused on the one-time global optimization of production scheduling before the actual production. The dynamic scheduling problem of the workshop is getting more and more attention. This paper proposed a simulated annealing algorithm to solve the real-time scheduling problem of large variety and low-volume mixed model assembly line. This algorithm obtains three groups of optimal solutions and the optimal scheduling scheme of multiple products, with the shortest product completion time and the lowest cost. Finally, the feasibility and efficiency of the model are proved by the Matlab simulation.