Addressing Unequal Area Facility Layout Problems with the Coral Reef Optimization algorithm with Substrate Layers. (August 2020)

• Record Type: Journal Article
• Authors: Garcia-Hernandez, L.; Garcia-Hernandez, J.A.; Salas-Morera, L.; Carmona-Muñoz, C.; Alghamdi, N.S.; de Oliveira, J. Valente; Salcedo-Sanz, S.
• Is Part Of: Engineering applications of artificial intelligence. Volume 93(2020)
• Publication Date: 2020-08
• Subjects: Unequal Area Facility Layout Problem -- Coral Reefs Optimization -- Facility Layout -- Meta-heuristics -- Bio-inspired algorithms; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103697
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The Unequal Area Facility Layout Problem (UA-FLP) is a relevant task in industrial manufacturing, in which the disposition of a number of facilities (or departments ) in a manufacturing system must be obtained, under several optimization criteria and different constraints. The UA-FLP is a hard optimization problem, in which traditional optimization techniques do not obtain good results. Thus, it has been successfully tackled with different heuristics and meta-heuristics in the last years. In this work we address the UA-FLP with a multi-method ensemble approach, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL). It is a novel multi-method evolutionary algorithm that encourages the evolution of several searching procedures at the same time over a single population. The CRO-SL has been previously applied to very difficult optimization problems, obtaining excellent performance. In this case, we adapt the CRO-SL to the UA-FLP, by means of increasing the diversity generation within the algorithm, which is helpful to improve the exploration of the searching space, avoiding to fall into local minima. Specifically, we propose to include several reproduction mechanisms (adapted to the UA-FLP) within each substrate of the algorithm, which will highly increase the diversity generation in the CRO-SL. An exhaustive experimental study of the CRO-SL performance in a large number of UA-FLP instances is carried out, including a comparison with the state-of-the-art algorithms for this problem. We will show the ability of the CRO-SL to reach or surpass the best-known solutions in most of the tested UA-FLP cases.