Overview of Dynamic Facility Layout Planning as a Sustainability Strategy

[EN] The facility layout design problem is significantly relevant within the business operations strategies framework and has emerged as an alternate strategy towards supply chain sustainability. However, its wide coverage in the scientific literature has focused mainly on the static planning approach and disregarded the dynamic approach, which is very useful in real-world applications. In this context, the present article offers a literature review of the dynamic facility layout problem (DFLP). First, a taxonomy of the reviewed papers is proposed based on the problem formulation current trends (related to the problem type, planning phase, planning approach, number of facilities, number of floors, number of departments, space consideration, department shape, department dimensions, department area, and materials handling configuration); the mathematical modeling approach (regarding the type of model, type of objective function, type of constraints, nature of market demand, type of data, and distance metric), and the considered solution approach. Then, the extent to which recent research into DFLP has contributed to supply chain sustainability by addressing its three performance dimensions (economic, environmental, social) is described. Finally, some future research guidelines are provided.This research was funded by the Spanish Ministry of Science, Innovation and Universities Project CADS4.0, grant number RTI2018-101344-B-I00; and the Valencian Community ERDF Programme 2014-2020, grant number IDIFEDER/2018/025.Pérez-Gosende, P.; Mula, J.; Díaz-Madroñero Boluda, FM. (2020). Overview of Dynamic Facility Layout Planning as a Sustainability Strategy. Sustainability. 12(19):1-16. https://doi.org/10.3390/su12198277S1161219Ghassemi Tari, F., & Neghabi, H. (2015). A new linear adjacency approach for facility layout problem with unequal area departments. Journal of Manufacturing Systems, 37, 93-103. doi:10.1016/j.jmsy.2015.09.003Kheirkhah, A., Navidi, H., & Messi Bidgoli, M. (2015). Dynamic Facility Layout Problem: A New Bilevel Formulation and Some Metaheuristic Solution Methods. IEEE Transactions on Engineering Management, 62(3), 396-410. doi:10.1109/tem.2015.2437195Altuntas, S., & Selim, H. (2012). Facility layout using weighted association rule-based data mining algorithms: Evaluation with simulation. Expert Systems with Applications, 39(1), 3-13. doi:10.1016/j.eswa.2011.06.045Ku, M.-Y., Hu, M. H., & Wang, M.-J. (2011). Simulated annealing based parallel genetic algorithm for facility layout problem. International Journal of Production Research, 49(6), 1801-1812. doi:10.1080/00207541003645789Navidi, H., Bashiri, M., & Messi Bidgoli, M. (2012). A heuristic approach on the facility layout problem based on game theory. International Journal of Production Research, 50(6), 1512-1527. doi:10.1080/00207543.2010.550638Hosseini-Nasab, H., Fereidouni, S., Fatemi Ghomi, S. M. T., & Fakhrzad, M. B. (2017). Classification of facility layout problems: a review study. The International Journal of Advanced Manufacturing Technology, 94(1-4), 957-977. doi:10.1007/s00170-017-0895-8Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387. doi:10.1108/09600030810882816Carter, C. R., & Washispack, S. (2018). Mapping the Path Forward for Sustainable Supply Chain Management: A Review of Reviews. Journal of Business Logistics, 39(4), 242-247. doi:10.1111/jbl.12196Roy, V., Schoenherr, T., & Charan, P. (2018). The thematic landscape of literature in sustainable supply chain management (SSCM). International Journal of Operations & Production Management, 38(4), 1091-1124. doi:10.1108/ijopm-05-2017-0260Barbosa-Póvoa, A. P., da Silva, C., & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 399-431. doi:10.1016/j.ejor.2017.10.036Tonelli, F., Evans, S., & Taticchi, P. (2013). Industrial sustainability: challenges, perspectives, actions. International Journal of Business Innovation and Research, 7(2), 143. doi:10.1504/ijbir.2013.052576Sánchez-Flores, R. B., Cruz-Sotelo, S. E., Ojeda-Benitez, S., & Ramírez-Barreto, M. E. (2020). Sustainable Supply Chain Management—A Literature Review on Emerging Economies. Sustainability, 12(17), 6972. doi:10.3390/su12176972Ford, S., & Despeisse, M. (2016). Additive manufacturing and sustainability: an exploratory study of the advantages and challenges. Journal of Cleaner Production, 137, 1573-1587. doi:10.1016/j.jclepro.2016.04.150Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. doi:10.1016/j.psep.2018.05.009Khuntia, J., Saldanha, T. J. V., Mithas, S., & Sambamurthy, V. (2018). Information Technology and Sustainability: Evidence from an Emerging Economy. Production and Operations Management, 27(4), 756-773. doi:10.1111/poms.12822Roy, S., Das, M., Ali, S. M., Raihan, A. S., Paul, S. K., & Kabir, G. (2020). Evaluating strategies for environmental sustainability in a supply chain of an emerging economy. Journal of Cleaner Production, 262, 121389. doi:10.1016/j.jclepro.2020.121389Morais, D. O. C., & Silvestre, B. S. (2018). Advancing social sustainability in supply chain management: Lessons from multiple case studies in an emerging economy. Journal of Cleaner Production, 199, 222-235. doi:10.1016/j.jclepro.2018.07.097Stindt, D. (2017). A generic planning approach for sustainable supply chain management - How to integrate concepts and methods to address the issues of sustainability? Journal of Cleaner Production, 153, 146-163. doi:10.1016/j.jclepro.2017.03.126MOSLEMIPOUR, G., LEE, T. S., & LOONG, Y. T. (2017). Performance Analysis of Intelligent Robust Facility Layout Design. Chinese Journal of Mechanical Engineering, 30(2), 407-418. doi:10.1007/s10033-017-0073-9Emami, S., & S. Nookabadi, A. (2013). Managing a new multi-objective model for the dynamic facility layout problem. The International Journal of Advanced Manufacturing Technology, 68(9-12), 2215-2228. doi:10.1007/s00170-013-4820-5Al Hawarneh, A., Bendak, S., & Ghanim, F. (2019). Dynamic facilities planning model for large scale construction projects. Automation in Construction, 98, 72-89. doi:10.1016/j.autcon.2018.11.021Pournaderi, N., Ghezavati, V. R., & Mozafari, M. (2019). Developing a mathematical model for the dynamic facility layout problem considering material handling system and optimizing it using cloud theory-based simulated annealing algorithm. SN Applied Sciences, 1(8). doi:10.1007/s42452-019-0865-xTuranoğlu, B., & Akkaya, G. (2018). A new hybrid heuristic algorithm based on bacterial foraging optimization for the dynamic facility layout problem. Expert Systems with Applications, 98, 93-104. doi:10.1016/j.eswa.2018.01.011Moslemipour, G., Lee, T. S., & Rilling, D. (2011). A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems. The International Journal of Advanced Manufacturing Technology, 60(1-4), 11-27. doi:10.1007/s00170-011-3614-xTebaldi, L., Bigliardi, B., & Bottani, E. (2018). Sustainable Supply Chain and Innovation: A Review of the Recent Literature. Sustainability, 10(11), 3946. doi:10.3390/su10113946Tseng, M.-L., Islam, M. S., Karia, N., Fauzi, F. A., & Afrin, S. (2019). A literature review on green supply chain management: Trends and future challenges. Resources, Conservation and Recycling, 141, 145-162. doi:10.1016/j.resconrec.2018.10.009Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869. doi:10.1016/j.jclepro.2019.119869Boar, A., Bastida, R., & Marimon, F. (2020). A Systematic Literature Review. Relationships between the Sharing Economy, Sustainability and Sustainable Development Goals. Sustainability, 12(17), 6744. doi:10.3390/su12176744Novais, L., Maqueira, J. M., & Ortiz-Bas, Á. (2019). A systematic literature review of cloud computing use in supply chain integration. Computers & Industrial Engineering, 129, 296-314. doi:10.1016/j.cie.2019.01.056Masi, D., Day, S., & Godsell, J. (2017). Supply Chain Configurations in the Circular Economy: A Systematic Literature Review. Sustainability, 9(9), 1602. doi:10.3390/su9091602Zavala-Alcívar, A., Verdecho, M.-J., & Alfaro-Saiz, J.-J. (2020). A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain. Sustainability, 12(16), 6300. doi:10.3390/su12166300Li, K., Rollins, J., & Yan, E. (2017). Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1-20. doi:10.1007/s11192-017-2622-5Kulturel-Konak, S., & Konak, A. (2014). A large-scale hybrid simulated annealing algorithm for cyclic facility layout problems. Engineering Optimization, 47(7), 963-978. doi:10.1080/0305215x.2014.933825Madhusudanan Pillai, V., Hunagund, I. B., & Krishnan, K. K. (2011). Design of robust layout for Dynamic Plant Layout Problems. Computers & Industrial Engineering, 61(3), 813-823. doi:10.1016/j.cie.2011.05.014Peng, Y., Zeng, T., Fan, L., Han, Y., & Xia, B. (2018). An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem. Discrete Dynamics in Nature and Society, 2018, 1-8. doi:10.1155/2018/1529058McKendall, A. R., & Hakobyan, A. (2010). Heuristics for the dynamic facility layout problem with unequal-area departments. European Journal of Operational Research, 201(1), 171-182. doi:10.1016/j.ejor.2009.02.028Yang, C.-L., Chuang, S.-P., & Hsu, T.-S. (2010). A genetic algorithm for dynamic facility planning in job shop manufacturing. The International Journal of Advanced Manufacturing Technology, 52(1-4), 303-309. doi:10.1007/s00170-010-2733-0Abedzadeh, M., Mazinani, M., Moradinasab, N., & Roghanian, E. (2012). Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem. The International Journal of Advanced Manufacturing Technology, 65(1-4), 197-211. doi:10.1007/s00170-012-4160-xGuan, X., Dai, X., Qiu, B., & Li, J. (2012). A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system. Computers & Industrial Engineering, 63(1), 98-108. doi:10.1016/j.cie.2012.01.016Jolai, F., Tavakkoli-Moghaddam, R., & Taghipour, M. (2012). A multi-objective particle swarm optimisation algorithm for unequal sized dynamic facility layout problem with pickup/drop-off locations. International Journal of Production Research, 50(15), 4279-4293. doi:10.1080/00207543.2011.613863Kia, R., Baboli, A., Javadian, N., Tavakkoli-Moghaddam, R., Kazemi, M., & Khorrami, J. (2012). Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Computers & Operations Research, 39(11), 2642-2658. doi:10.1016/j.cor.2012.01.012McKendall, A. R., & Liu, W.-H. (2012). New Tabu search heuristics for the dynamic facility layout problem. International Journal of Production Research, 50(3), 867-878. doi:10.1080/00207543.2010.545446Hosseini-Nasab, H., & Emami, L. (2013). A hybrid particle swarm optimisation for dynamic facility layout problem. International Journal of Production Research, 51(14), 4325-4335. doi:10.1080/00207543.2013.774486Kaveh, M., Dalfard, V. M., & Amiri, S. (2013). A new intelligent algorithm for dynamic facility layout problem in state of fuzzy constraints. Neural Computing and Applications, 24(5), 1179-1190. doi:10.1007/s00521-013-1339-5KIA, R., JAVADIAN, N., PAYDAR, M. M., & SAIDI-MEHRABAD, M. (2013). A SIMULATED ANNEALING FOR INTRA-CELL LAYOUT DESIGN OF DYNAMIC CELLULAR MANUFACTURING SYSTEMS WITH ROUTE SELECTION, PURCHASING MACHINES AND CELL RECONFIGURATION. Asia-Pacific Journal of Operational Research, 30(04), 1350004. doi:10.1142/s0217595913500048Mazinani, M., Abedzadeh, M., & Mohebali, N. (2012). Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm. The International Journal of Advanced Manufacturing Technology, 65(5-8), 929-943. doi:10.1007/s00170-012-4229-6Samarghandi, H., Taabayan, P., & Behroozi, M. (2013). Metaheuristics for fuzzy dynamic facility layout problem with unequal area constraints and closeness ratings. The International Journal of Advanced Manufacturing Technology, 67(9-12), 2701-2715. doi:10.1007/s00170-012-4685-zYu-Hsin Chen, G. (2013). A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems. International Journal of Production Economics, 142(2), 362-371. doi:10.1016/j.ijpe.2012.12.012Bozorgi, N., Abedzadeh, M., & Zeinali, M. (2014). Tabu search heuristic for efficiency of dynamic facility layout problem. The International Journal of Advanced Manufacturing Technology, 77(1-4), 689-703. doi:10.1007/s00170-014-6460-9CHEN, G. Y.-H., & LO, J.-C. (2014). DYNAMIC FACILITY LAYOUT WITH MULTI-OBJECTIVES. Asia-Pacific Journal of Operational Research, 31(04), 1450027. doi:10.1142/s0217595914500274Hosseini, S., Khaled, A. A., & Vadlamani, S. (2014). Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem. Neural Computing and Applications, 25(7-8), 1871-1885. doi:10.1007/s00521-014-1678-xKia, R., Khaksar-Haghani, F., Javadian, N., & Tavakkoli-Moghaddam, R. (2014). Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm. Journal of Manufacturing Systems, 33(1), 218-232. doi:10.1016/j.jmsy.2013.12.005Nematian, J. (2014). A robust single row facility layout problem with fuzzy random variables. The International Journal of Advanced Manufacturing Technology, 72(1-4), 255-267. doi:10.1007/s00170-013-5564-yPourvaziri, H., & Naderi, B. (2014). A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Applied Soft Computing, 24, 457-469. doi:10.1016/j.asoc.2014.06.051Derakhshan Asl, A., & Wong, K. Y. (2015). Solving unequal-area static and dynamic facility layout problems using modified particle swarm optimization. Journal of Intelligent Manufacturing, 28(6), 1317-1336. doi:10.1007/s10845-015-1053-5Li, L., Li, C., Ma, H., & Tang, Y. (2015). An Optimization Method for the Remanufacturing Dynamic Facility Layout Problem with Uncertainties. Discrete Dynamics in Nature and Society, 2015, 1-11. doi:10.1155/2015/685408Ulutas, B., & Islier, A. A. (2015). Dynamic facility layout problem in footwear industry. Journal of Manufacturing Systems, 36, 55-61. doi:10.1016/j.jmsy.2015.03.004Zarea Fazlelahi, F., Pournader, M., Gharakhani, M., & Sadjadi, S. J. (2016). A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230(12), 2264-2274. doi:10.1177/0954405415615728Hosseini, S. S., & Seifbarghy, M. (2016). A novel meta-heuristic algorithm for multi-objective dynamic facility layout problem. RAIRO - Operations Research, 50(4-5), 869-890. doi:10.1051/ro/2016057Pourvaziri, H., & Pierreval, H. (2017). Dynamic facility layout problem based on open queuing network theory. European Journal of Operational Research, 259(2), 538-553. doi:10.1016/j.ejor.2016.11.011Tayal, A., & Singh, S. P. (2016). Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem. Annals of Operations Research, 270(1-2), 489-514. doi:10.1007/s10479-016-2237-xKumar, R., & Singh, S. P. (2017). A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems. Engineering Optimization, 49(11), 1848-1867. doi:10.1080/0305215x.2016.1274205Liu, J., Wang, D., He, K., & Xue, Y. (2017). Combining Wang–Landau sampling algorithm and heuristics for solving the unequal-area dynamic facility layout problem. European Journal of Operational Research, 262(3), 1052-1063. doi:10.1016/j.ejor.2017.04.002Vitayasak, S., Pongcharoen, P., & Hicks, C. (2017). A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm. International Journal of Production Economics, 190, 146-157. doi:10.1016/j.ijpe.2016.03.019Xiao, Y., Xie, Y., Kulturel-Konak, S., & Konak, A. (2017). A problem evolution algorithm with linear programming for the dynamic facility layout problem—A general layout formulation. Computers & Operations Research, 88, 187-207. doi:10.1016/j.cor.2017.06.025Li, J., Tan, X., & Li, J. (2018). Research on Dynamic Facility Layout Problem of Manufacturing Unit Considering Human Factors. Mathematical Problems in Engineering, 2018, 1-13. doi:10.1155/2018/6040561Vitayasak, S., & Pongcharoen, P. (2018). Performance improvement of Teaching-Learning-Based Optimisation for robust machine layout design. Expert Systems with Applications, 98, 129-152. doi:10.1016/j.eswa.2018.01.005Wei, X., Yuan, S., & Ye, Y. (2019). Optimizing facility layout planning for reconfigurable manufacturing system based on chaos genetic algorithm. Production & Manufacturing Research, 7(1), 109-124. doi:10.1080/21693277.2019.1602486Kulturel-Konak, S. (2007). Approaches to uncertainties in facility layout problems: Perspectives at the beginning of the 21st Century. Journal of Intelligent Manufacturing, 18(2), 273-284. doi:10.1007/s10845-007-0020-1Sharma, P., & Singhal, S. (2016). Implementation of fuzzy TOPSIS methodology in selection of procedural approach for facility layout planning. The International Journal of Advanced Manufacturing Technology, 88(5-8), 1485-1493. doi:10.1007/s00170-016-8878-8Bukchin, Y., & Tzur, M. (2014). A new MILP approach for the facility process-layout design problem with rectangular and L/T shape departments. International Journal of Production Research, 52(24), 7339-7359. doi:10.1080/00207543.2014.930534Meller, R. D., Kirkizoglu, Z., & Chen, W. (2010). A new optimization model to support a bottom-up approach to facility design. Computers & Operations Research, 37(1), 42-49. doi:10.1016/j.cor.2009.03.018Feng, J., & Che, A. (2018). Novel integer linear programming models for the facility layout problem with fixed-size rectangular departments. Computers & Operations Research, 95, 163-171. doi:10.1016/j.cor.2018.03.013Allahyari, M. Z., & Azab, A. (2018). Mathematical modeling and multi-start search simulated annealing for unequal-area facility layout problem. Expert Systems with Applications, 91, 46-62. doi:10.1016/j.eswa.2017.07.049Ahmadi, A., Pishvaee, M. S., & Akbari Jokar, M. R. (2017). A survey on multi-floor facility layout problems. Computers & Industrial Engineering, 107, 158-170. doi:10.1016/j.cie.2017.03.015Drira, A., Pierreval, H., & Hajri-Gabouj, S. (2007). Facility layout problems: A survey. Annual Reviews in Control, 31(2), 255-267. doi:10.1016/j.arcontrol.2007.04.001Grobelny, J., & Michalski, R. (2017). A novel version of simulated annealing based on linguistic patterns for solving facility layout problems. Knowledge-Based Systems, 124, 55-69. doi:10.1016/j.knosys.2017.03.001Hathhorn, J., Sisikoglu, E., & Sir, M. Y. (2013). A multi-objective mixed-integer programming model for a multi-floor facility layout. International Journal of Production Research, 51(14), 4223-4239. doi:10.1080/00207543.2012.75348

The single row layout problem with clearances

The single row layout problem (SRLP) is a specially structured instance of the classical facility layout problem, especially used in flexible manufacturing systems. The SRLP consists of finding the most efficient arrangement of a given number of machines along one side of the material handling path with the purpose of minimising the total weighted sum of distances among all machine pairs. To reflect real manufacturing situations, a minimum space (so-called clearances) between machines may be required by observing technological constraints, safety considerations and regulations. This thesis intends to outline the different concepts of clearances used in literature and analyse their effects on modelling and solution approaches for the SRLP. In particular the special characteristics of sequence-dependent, asymmetric clearances are discussed and finally extended to large size clearances (machine-spanning clearances). For this, adjusted and novel model formulations and solution approaches are presented. Furthermore, a comprehensive survey of articles published in this research area since 2000 is provided which identify recent developments and emerging trends in SRLP

A simple heuristic for linear sequencing of machines in layout design

This paper presents a simple heuristic to determine a common linear machine sequence for multiple products with different operation sequences and a limited number of duplicate machine types available for the job. The heuristic is based on minimisation of the total flow distance travelled by a product on the linear machine sequence. It is assumed that the flows of products are allowed only in the forward direction, either in-sequence or by-pass. It is also assumed that backtrack movements are not allowed. The effectiveness of the proposed heuristic is demonstrated through the solutions of two typical layout design problems taken from the literature. Subsequently, a number of additional problems are solved and their results are compared with the results applying existing methods. The results indicate that the proposed method can be an effective tool in solving layout design problems

Heuristics and Metaheuristics Approaches for Facility Layout Problems: A Survey

Facility Layout Problem (FLP) is a NP-hard problem concerned with the arrangement of facilities as to minimize the distance travelled between all pairs of facilities. Many exact and approximate approaches have been proposed with an extensive applicability to deal with this problem. This paper studies the fundamentals of some well-known heuristics and metaheuristics used in solving the FLPs. It is hoped that this paper will trigger researchers for in-depth studies in FLPs looking into more specific interest such as equal or unequal FLPs

Non-classical computing: feasible versus infeasible

Physics sets certain limits on what is and is not computable. These limits are very far from having been reached by current technologies. Whilst proposals for hypercomputation are almost certainly infeasible, there are a number of non classical approaches that do hold considerable promise. There are a range of possible architectures that could be implemented on silicon that are distinctly different from the von Neumann model. Beyond this, quantum simulators, which are the quantum equivalent of analogue computers, may be constructable in the near future

Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost

A novel multi-objective Interactive Coral Reefs Optimization algorithm for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictions and constraints. Nevertheless, the subjective opinion of the designer (Decision Maker, DM) has never been considered along with the quantitative criteria and restrictions. This work proposes a novel approach for the UA-FLP based on an Interactive Coral Reefs Optimization (ICRO) algorithm, which combines the simultaneous consideration of both quantitative and qualitative (DM opinion) features. The algorithm implementation is explained in detail, including the way of jointly considering quantitative and qualitative aspects in the fitness function of the problem. The experimental part of the paper illustrates the effect of including qualitative aspects in UA-FLP problems, considering three different hard UA-FLP instances. Empirical results show that the proposed approach is able to incorporate the DM preferences in the obtained layouts, without affecting much to the quantitative part of the solutions