Simultaneous balancing, sequencing, and workstation planning for a mixed model manual assembly line using hybrid genetic algorithm. (May 2018)

• Record Type: Journal Article
• Authors: Defersha, Fantahun M.; Mohebalizadehgashti, Fatemeh
• Is Part Of: Computers & industrial engineering. Volume 119(2018)
• Page Start: 370
• Page End: 387
• Publication Date: 2018-05
• Subjects: Balancing -- Sequencing -- Mixed integer linear programming model -- Mixed model assembly line -- Hybrid genetic algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.04.014
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Develop a mathematical model for integrated assembly line balancing and sequencing. Determine length and number of station of the assembly line for a continuous conveyor. Develop a linear programming embedded algorithm to solve the proposed model for continuous conveyor. Illustrate the computation performance of the developed algorithm. Abstract: Balancing and sequencing are two important challenging problems in designing mixed-model assembly lines. A large number of studies have addressed these two problems both independently and simultaneously. However, several important aspects such as assignment of common tasks between models to different workstations, and minimizing the number and length of workstations are not addressed in an integrated manner. In this paper, we proposed a mixed integer linear programming mathematical model by considering the above aspects simultaneously for a continuously moving conveyor. The objective function of the model is to minimize the length and number of workstations, costs of workstations and task duplications. Since the proposed model cannot be efficiently solved using commercially available packages, a multi-phased linear programming embedded genetic algorithm is developed. In the proposed algorithm, binary variables are determined using genetic search whereas continuous variables corresponding to the binary variables are determined by solving linear programming sub-problem using simplex algorithm. Several numerical examples with different sizes are presented to illustrate features of the proposed model and computational efficiency of the proposed hybrid genetic algorithm. A comparative study of genetic algorithm and simulated annealing is also conducted.