Fathi, M. (Masood)

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20162

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    A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines
    (Taylor & Francis, 2016) Fathi, M. (Masood); Rodríguez-Chacón, V. (Victoria); Fontes, D.B.M.M. (Dalila B.M.M.); Álvarez-Sánchez-Arjona, M.J. (María Jesús)
    The Assembly Line Part Feeding Problem (ALPFP) is a complex combinatorial optimisation problem concerned with the delivery of the required parts to the assembly workstations in the right quantities at the right time. Solving the ALPFP includes simultaneously solving two sub-problems, namely tour scheduling and tow-train loading. In this article, we first define the problem and formulate it as a multi-objective mixed-integer linear programming model. Then, we carry out a complexity analysis, proving the ALPFP to be NP-complete. A modified particle swarm optimisation (MPSO) algorithm incorporating mutation as part of the position updating scheme is subsequently proposed. The MPSO is capable of finding very good solutions with small time requirements. Computational results are reported, demonstrating the efficiency and effectiveness of the proposed MPSO.
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    A new heuristic-based bi-objective simulated annealing method for U-shaped assembly line balancing
    (Inderscience, 2016) Fathi, M. (Masood); Álvarez-Sánchez-Arjona, M.J. (María Jesús); Rodríguez-Chacón, V. (Victoria)
    The U-shaped assembly line has received a considerable attention and has been widely used in industry in recent years due to the pressures of the just-in-time (JIT) manufacturing philosophy. However, balancing the U-shaped line is more difficult than balancing the traditional straight line. This study aims at balancing the U-shaped assembly line by introducing a novel heuristic which is based on the simulated annealing (SA) algorithm. The objectives to be optimised in this study are the number of workstations and balance efficiency. The performance of the proposed algorithm is examined by solving a set of standard test problems and a real case study. The results attained by the proposed algorithm were compared against the best known solution in the literature and it was found that the proposed algorithm is able to find good solutions in a reasonably short computational time.