Bi-objective dependent location quadratic assignment problem: Formulation and solution using a modified artificial bee colony algorithm. (July 2018)

• Record Type: Journal Article
• Authors: Samanta, Suman; Philip, Deepu; Chakraborty, Shankar
• Is Part Of: Computers & industrial engineering. Volume 121(2018)
• Page Start: 8
• Page End: 26
• Publication Date: 2018-07
• Subjects: Combinatorial optimization -- Multi-objective QAP -- Interwoven systems -- Artificial bee colony algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.05.018
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A new dependent location bi-objective facility layout problem is identified. The problem is formulated as a bi-objective quadratic assignment problem. An artificial bee colony based algorithm is proposed to solve the problem. Control parameter selection procedure for the algorithm is discussed. Performance of the proposed algorithm is compared with other algorithms. Abstract: Combinatorial optimization problems arise from various real life situations and the quadratic assignment problem (QAP) to model a facility layout problem or a plant location problem is such an example. While examining the facility layout of a semi-automated bus body manufacturing unit, a bi-objective facility layout optimization problem is identified in which the solution space of the second objective function depends and changes upon the feasible solutions of the first objective function. In this paper, the said problem is first defined in the form of a bi-objective quadratic dependent location assignment problem (bi-d-QAP), a heuristic solution approach is then provided, and finally, a modified artificial bee colony algorithm is proposed while combining both the genetic and neighborhood search algorithms to solve the considered bi-d-QAP. The data obtained from the above-mentioned manufacturing unit are utilized to show how the proposed algorithm performs better in comparison to some of the popular state-of-the-art optimization algorithms.