Abstract
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.
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A obtained best permutations
A obtained best permutations
A set of 20 instances of sizes in the range of [60, 80] is studied in Sect. 4.1, whose best objective values obtained by applying the proposed hybridized pGA are shown in Table 2. The corresponding orderings of the facilities are shown in Table 7.
Similarly, the obtained best orderings of facilities for the 20 instances each of size 60, which are studied in Sect. 4.2 along with their best objective values in Table 3, are shown in Tables 8 and 9.
Finally, the obtained best orderings of facilities for the 20 large-size instances of sizes in the range of [110, 300], which are studied in Sect. 4.3 along with their best objective values in Table 5, are shown in Tables 10, 11 and 12.
As in Tables 2, 3 and 5, the scenarios (a) and (b) in Tables 7, 8, 9, 10, 11, 12 indicate the bCAP scenarios with the best material handling cost and best corridor length, respectively.
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Kalita, Z., Datta, D. & Palubeckis, G. Bi-objective corridor allocation problem using a permutation-based genetic algorithm hybridized with a local search technique. Soft Comput 23, 961–986 (2019). https://doi.org/10.1007/s00500-017-2807-0
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DOI: https://doi.org/10.1007/s00500-017-2807-0