Bi-objective corridor allocation problem using a permutation-based genetic algorithm hybridized with a local search technique

As a more practical form of the double-row layout problem, the bi-objective corridor allocation problem(bCAP) was introduced, in which a given number of facilities are to be placed on opposite sides of a central corridor so as to minimize both the overall flow cost among the facilities and the length of the corridor. Further, the bCAP seeks the placement of the facilities starting from the same level along the corridor without allowing any gap between two facilities of a row. In the initial proposal, the bCAP was solved as an unconstrained optimization problem using a permutation-based genetic algorithm(pGA). It is observed that the pGA alone is not sufficient to reach to the potential solutions of the complicated bCAP. In this work, incorporating a promising local search technique in the pGA, the hybridized pGA is found outperforming the simple pGA as well as a simulated annealing and tabu search-based approach in a number of instances of sizes 60 and above, in terms of both the best objective values and statistical analysis. Moreover, the hybridized pGA could explore multiple optimal solutions for some of such instances.