Abstract
Car Sequencing on two-sided assembly line is an important problem in an automotive industry. Researchers and practitioners have attempted several approaches to solve this problem aiming at maximum production efficiency. The problem is considered as an “NP-Hard problem”. In this paper, three objective functions are considered including 1) minimizing utility work, 2) reducing the number of violation and 3) decreasing the number of color changes. The expansion of Combinatorial Optimization with Coincidence (COIN-E) algorithm is developed from its original version (i.e. COIN). Several well-known algorithms are compared in solving this problem including Non-dominated Sorting Genetic Algorithms (NSGA-II), Discrete Particle Swarm Optimization (DPSO), Biogeography-based Optimization (BBO) and (COIN). The experimental results indicate that COIN-E is efficient and it obtains the values of convergence = 91.85%, spread = 51.08% and ratio = 57.48%, which are significantly superior to NSGA-II, DPSO, BBO and COIN.
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