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Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Published online by Cambridge University Press:  21 September 2020

Yann-Seing Law-Kam Cio Open the ORCID record for Yann-Seing Law-Kam Cio [Opens in a new window] ,
Yuanchao Ma ,
Aurelian Vadean ,
Giovanni Beltrame  and
Sofiane Achiche
Yann-Seing Law-Kam Cio*
Affiliation:
Department of Mechanical Engineering, Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Québec, CanadaH3C 3A7
Yuanchao Ma
Affiliation:
Department of Mechanical Engineering, Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Québec, CanadaH3C 3A7
Aurelian Vadean
Affiliation:
Department of Mechanical Engineering, Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Québec, CanadaH3C 3A7
Giovanni Beltrame
Affiliation:
Department of Computer Engineering and Software Engineering, Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Québec, CanadaH3C 3A7
Sofiane Achiche
Affiliation:
Department of Mechanical Engineering, Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Québec, CanadaH3C 3A7
*
Author for correspondence: Yann-Seing Law-Kam Cio, E-mail: [email protected]
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Abstract

The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.

Keywords

Design automationdesign synthesisevolutionary computinggenetic algorithmproduct development
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 1 , February 2021 , pp. 49 - 64
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Abas, MA and Dahlui, M (2015) Development of greenhouse autonomous control system for Home Agriculture project.2015 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), 12–17. IEEE.Google Scholar
Abas, MA, Salman, M, Ridwan, M and Adzhar, M (2016) Autonomous irrigation control through rectifying methods for tropical greenhouse electronic system. International Journal of Simulation: Systems, Science and Technology 17(32), 30.1–30.7.Google Scholar
Ahmadi, A, Pishvaee, MS and Akbari Jokar, MR (2017) A survey on multi-floor facility layout problems. Computers & Industrial Engineering 107, 158–170.CrossRefGoogle Scholar
Alibaba (2019) mini 6V DC Dosing Pump Peristaltic Dosing Head For Aquarium Lab Analytical Water with Water Pipe Peristaltic Pump. Available at https://www.alibaba.com/product-detail/mini-6V-DC-Dosing-pump-Peristaltic_60829374698.html?spm=a2700.7724857.normalList.199.1dca42bfUVp4XT.Google Scholar
Amazon (2019) Dicrey Micro Water Pump Mini Submersible Pump Fountain Pump Aquarium Water Pump Small DC Motor Water Pump 3V 4.5V 100L/H. Available at https://www.amazon.com/Dikley-Micro-Submersible-Fountain-Aquarium/dp/B0791FSR2S.Google Scholar
Azaza, M, Tanougast, C, Fabrizio, E and Mami, A (2016) Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring. ISA Transactions 61, 297–307.CrossRefGoogle ScholarPubMed
Banggood, (2019) 12V DC Dosing Pump Peristaltic Pump For Aquarium Lab Analytical Water. https://www.banggood.com/12V-DC-Dosing-Pump-Peristaltic-Pump-For-Aquarium-Lab-Analytical-Water-p-931333.html?cur_warehouse=CN.Google Scholar
Browning, TR (2016) Design structure matrix extensions and innovations: a survey and new opportunities. IEEE Transactions on Engineering Management 63, 27–52.CrossRefGoogle Scholar
Castañeda-Miranda, A and Castaño, VM (2017) Smart frost control in greenhouses by neural networks models. Computers and Electronics in Agriculture 137, 102–114.CrossRefGoogle Scholar
Cheng, M-Y, Gupta, A, Ong, Y-S and Ni, Z-W (2017) Coevolutionary multitasking for concurrent global optimization: with case studies in complex engineering design. Engineering Applications of Artificial Intelligence 64, 13–24.CrossRefGoogle Scholar
Chouinard, U, Achiche, S, Leblond-Ménard, C and Baron, L (2017) Assessment of dependencies in mechatronics conceptual design of a quadcopter drone using linguistic fuzzy variables. DS 87-4 Proceedings of the 21st International Conference on Engineering Design (ICED 17), Vol. 4: Design Methods and Tools, Vancouver, Canada, 21–25 August 2017.Google Scholar
Chouinard, U, Achiche, S and Baron, L (2019) Integrating negative dependencies assessment during mechatronics conceptual design using fuzzy logic and quantitative graph theory. Mechatronics 59, 140–153.CrossRefGoogle Scholar
Drira, A, Pierreval, H and Hajri-Gabouj, S (2007) Facility layout problems: a survey. Annual Reviews in Control 31, 255–267.CrossRefGoogle Scholar
Eben-Chaime, M, Bechar, A and Baron, A (2011) Economical evaluation of greenhouse layout design. International Journal of Production Economics 134, 246–254.CrossRefGoogle Scholar
Eiben, AE and Smit, SK (2012) Evolutionary algorithm parameters and methods to tune them. In Hamadi, Y, Monfroy, E and Saubion, F (eds), Autonomous Search. Berlin, Heidelberg: Springer, pp. 15–36.Google Scholar
Elferchichi, A, Gharsallah, O, Nouiri, I, Lebdi, F and Lamaddalena, N (2009) The genetic algorithm approach for identifying the optimal operation of a multi-reservoirs on-demand irrigation system. Biosystems Engineering 102, 334–344.CrossRefGoogle Scholar
Ferentinos, KP and Albright, LD (2005) Optimal design of plant lighting system by genetic algorithms. Engineering Applications of Artificial Intelligence 18, 473–484.CrossRefGoogle Scholar
Ferentinos, KP and Tsiligiridis, TA (2007) Adaptive design optimization of wireless sensor networks using genetic algorithms. Computer Networks 51, 1031–1051.CrossRefGoogle Scholar
Ferentinos, KP, Tsiligiridis, TA and Arvanitis, KG (2005) Energy optimization of wireless sensor networks for environmental measurements.Proceedings of the International Conference on Computational Intelligence for Measurment Systems and Applicatons (CIMSA) 51, 1031–1051.Google Scholar
Ferentinos, KP, Katsoulas, N, Tzounis, A, Bartzanas, T and Kittas, C (2017) Wireless sensor networks for greenhouse climate and plant condition assessment. Biosystems Engineering 153, 70–81.CrossRefGoogle Scholar
Fleming, PJ, Purshouse, RC and Lygoe, RJ (2005) Many-Objective Optimization: An Engineering Design Perspective. Berlin, Heidelberg: Springer.Google Scholar
Fu, Y, Liu, H, Shao, L, Wang, M, Berkovich, YA, Erokhin, A and Liu, H (2013) A high-performance ground-based prototype of horn-type sequential vegetable production facility for life support system in space. Advances in Space Research 52, 97–104.CrossRefGoogle Scholar
Giroux, R, Berinstain, A, Braham, S, Graham, T, Bamsey, M, Boyd, K, Silver, M, Lussier-Desbiens, A, Lee, P, Boucher, M, Cowing, K and Dixon, M (2006) Greenhouses in extreme environments: the arthur clarke Mars greenhouse design and operation overview. Advances in Space Research 38, 1248–1259.CrossRefGoogle Scholar
Goumopoulos, C (2012) An autonomous wireless sensor/actuator network for precision irrigation in greenhouses. In Mukhopadhyay, SC (ed), Smart Sensing Technology for Agriculture and Environmental Monitoring. Berlin, Heidelberg: Springer, pp. 1–20.Google Scholar
Goumopoulos, C, O'Flynn, B and Kameas, A (2014) Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support. Computers and Electronics in Agriculture 105, 20–33.CrossRefGoogle Scholar
Guzmán-Cruz, R, Castañeda-Miranda, R, García-Escalante, JJ, López-Cruz, IL, Lara-Herrera, A and de la Rosa, JI (2009) Calibration of a greenhouse climate model using evolutionary algorithms. Biosystems Engineering 104, 135–142.CrossRefGoogle Scholar
Haeuplik-Meusburger, S, Paterson, C, Schubert, D and Zabel, P (2014) Greenhouses and their humanizing synergies. Acta Astronautica 96, 138–150.CrossRefGoogle Scholar
Hahn, F (2011) Fuzzy controller decreases tomato cracking in greenhouses. Computers and Electronics in Agriculture 77, 21–27.CrossRefGoogle Scholar
Häuplik-Meusburger, S, Peldszus, R and Holzgethan, V (2011) Greenhouse design integration benefits for extended spaceflight. Acta Astronautica 68, 85–90.CrossRefGoogle Scholar
Herrero, JM, Blasco, X, Martínez, M, Ramos, C and Sanchis, J (2007) Non-linear robust identification of a greenhouse model using multi-objective evolutionary algorithms. Biosystems Engineering 98, 335–346.CrossRefGoogle Scholar
Hisao, I, Noritaka, T and Yusuke, N (2008) Evolutionary many-objective optimization: A short review. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2419–2426.CrossRefGoogle Scholar
Huang, W, Zhang, Y and Li, L (2019) Survey on multi-objective evolutionary algorithms. Journal of Physics: Conference Series 1288, 012057.Google Scholar
Kaim, A, Cord, AF and Volk, M (2018) A review of multi-criteria optimization techniques for agricultural land use allocation. Environmental Modelling & Software 105, 79–93.CrossRefGoogle Scholar
Kalyanmoy, D (2001) Multi Objective Optimization Using Evolutionary Algorithms. New York, NY: John Wiley and Sons.Google Scholar
Király, A and Abonyi, J (2015) Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using google maps API. Engineering Applications of Artificial Intelligence 38, 122–130.CrossRefGoogle Scholar
Koenig, R and Schneider, S (2012) Hierarchical structuring of layout problems in an interactive evolutionary layout system. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26, 129–142.CrossRefGoogle Scholar
Komasilovs, V, Stalidzans, E, Osadcuks, V and Mednis, M (2013) Specification development of robotic system for pesticide spraying in greenhouse. 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 453–457.CrossRefGoogle Scholar
Krishna, KL, Madhuri, J and Anuradha, K (2016) A ZigBee based energy efficient environmental monitoring alerting and controlling system. 2016 International Conference on Information Communication and Embedded Systems (ICICES), IEEE, pp. 1–7.Google Scholar
Li, B, Li, J, Tang, K and Yao, X (2015) Many-objective evolutionary algorithms: a survey. ACM Computing Surveys (CSUR) 48, 1–35.CrossRefGoogle Scholar
Ma, H, Simon, D, Fei, M and Chen, Z (2013) On the equivalences and differences of evolutionary algorithms. Engineering Applications of Artificial Intelligence 26, 2397–2407.CrossRefGoogle Scholar
Marler, RT and Arora, JS (2004) Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26, 369–395.CrossRefGoogle Scholar
Matos, J, Gonçalves, PJ and Torres, PM (2015) An automatic mechanical system for hydroponics fodder production. Romanian Review Precision Mechanics, Optics & Mecatronics 47, 63–71.Google Scholar
Mohebbi, A, Baron, L, Achiche, S and Birglen, L (2014) Trends in concurrent, multi-criteria and optimal design of mechatronic systems: A review. Proceedings of the 2014 International Conference on Innovative Design and Manufacturing (ICIDM), IEEE, pp. 88–93.Google Scholar
Montero, E, Riff, M-C and Rojas-Morales, N (2018) Tuners review: how crucial are set-up values to find effective parameter values? Engineering Applications of Artificial Intelligence 76, 108–118.CrossRefGoogle Scholar
Mørkeberg Torry-Smith, J, Qamar, A, Achiche, S, Wikander, J, Henrik Mortensen, N and During, C (2012) Challenges in designing mechatronic systems. Journal of Mechanical Design 135, 011005-1–011005-11.Google Scholar
Moslemipour, G, Lee, TS and Rilling, D (2012) A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems. The International Journal of Advanced Manufacturing Technology 60, 11–27.CrossRefGoogle Scholar
Nadal, A, Llorach-Massana, P, Cuerva, E, López-Capel, E, Montero, JI, Josa, A, Rieradevall, J and Royapoor, M (2017) Building-integrated rooftop greenhouses: an energy and environmental assessment in the Mediterranean context. Applied Energy 187, 338–351.CrossRefGoogle Scholar
Ooi, CS, Lim, MH and Leong, MS (2019) Self-tune linear adaptive-genetic algorithm for feature selection. IEEE Access 7, 138211–138232.CrossRefGoogle Scholar
Pala, M, Mizenko, L, Mach, M and Reed, T (2014) Aeroponic greenhouse as an autonomous system using intelligent space for agriculture robotics. Robot Intelligence Technology and Applications 2, 83–93.Google Scholar
Panerati, J and Beltrame, G (2014) A comparative evaluation of multi-objective exploration algorithms for high-level design. ACM Transactions on Design Automation of Electronic Systems 19, 1–22.CrossRefGoogle Scholar
Paraforos, DS and Griepentrog, HW (2013) Multivariable greenhouse climate control using dynamic decoupling controllers. IFAC Proceedings Volumes 46, 305–310.CrossRefGoogle Scholar
Pimmler, TU and Eppinger, SD (1994) Integration analysis of product decompositions. Proceedings of the ASME Design Theory and Methodology Conference 68, 343–351.Google Scholar
Poulet, L and Doule, O (2014) Greenhouse Automation, Illumination and Expansion Study for Mars Desert Research Station. 65th International Astronautical Congress.Google Scholar
Rabago, F, de Santago, E and Moncada, J (2013) Solar automated greenhouse. Advanced Materials Research 740, 198–202.Google Scholar
Ribas, PC, Yamamoto, L, Polli, HL, Arruda, LV and Neves, F Jr (2013) A micro-genetic algorithm for multi-objective scheduling of a real world pipeline network. Engineering Applications of Artificial Intelligence 26, 302–313.CrossRefGoogle Scholar
Romantchik, E, Ríos, E, Sánchez, E, López, I and Sánchez, JR (2017) Determination of energy to be supplied by photovoltaic systems for fan-pad systems in cooling process of greenhouses. Applied Thermal Engineering 114, 1161–1168.CrossRefGoogle Scholar
Sabri, N, Aljunid, S, Ahmad, B, Yahya, A, Kamaruddin, R and Salim, M (2011) Wireless sensor actor network based on fuzzy inference system for greenhouse climate control. Journal of Applied Sciences 11, 3104–3116.CrossRefGoogle Scholar
Saldanha, WH, Soares, GL, Machado-Coelho, TM, dos Santos, ED and Ekel, PI (2017) Choosing the best evolutionary algorithm to optimize the multiobjective shell-and-tube heat exchanger design problem using PROMETHEE. Applied Thermal Engineering 127, 1049–1061.CrossRefGoogle Scholar
Saleh, HA and Chelouah, R (2004) The design of the global navigation satellite system surveying networks using genetic algorithms. Engineering Applications of Artificial Intelligence 17, 111–122.CrossRefGoogle Scholar
Schubert, D, Quantius, D, Hauslage, J, Glasgow, L, Schroder, F and Dorn, M (2011) Advanced Greenhouse Modules for use within Planetary Habitats. 41st International Conference on Environmental Systems, p. 5166.Google Scholar
Seeed The IoT Hardware Enabler (2019) 6V Mini Water Pump. Available at https://www.seeedstudio.com/6V-Mini-Water-Pump-p-1945.html.Google Scholar
Simon, D (2013) Evolutionary Optimization Algorithms. Hoboken, New Jersey: John Wiley & Sons.Google Scholar
Systems, T. F. (2019) Miniature Peristaltic Pump. Available at https://www.takasago-fluidics.com/products/products_pump/peristaltic/.Google Scholar
Taura, T and Nagasaka, I (1999) Adaptive-growth-type 3D representation for configuration design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, 171–184.CrossRefGoogle Scholar
Torry-Smith, JM, Achiche, S, Mortensen, NH, Qamar, A, Wikander, J and During, C (2011) Mechatronic Design – Still a Considerable Challenge. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 54860, 33–44.Google Scholar
Torry-Smith, JM, Mortensen, NH and Achiche, S (2014) A proposal for a classification of product-related dependencies in development of mechatronic products. Research in Engineering Design 25, 53–74.CrossRefGoogle Scholar
Ushada, M and Murase, H (2009) Design of customisable greening material using swarm modelling. Biosystems Engineering 104, 169–183.CrossRefGoogle Scholar
Utamima, A, Reiners, T and Ansaripoor, AH (2019) Optimisation of agricultural routing planning in field logistics with evolutionary hybrid neighbourhood search. Biosystems Engineering 184, 166–180.CrossRefGoogle Scholar
Vera, M, Osorio-Comparán, R, Rienzo, A, Duarte-Mermoud, M and Lefranc, G (2017) Variables control of a modular greenhouse. 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), IEEE, pp. 1–8.Google Scholar
Wang, H, Olhofer, M and Jin, Y (2017) A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges. Complex & Intelligent Systems 3, 233–245.CrossRefGoogle Scholar
Wolpert, DH and Macready, WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82.CrossRefGoogle Scholar
Xu, D and Li, H (2008) Intelligent greenhouse control-system based on agent. 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA) 1, 390–394.CrossRefGoogle Scholar
Yano, S, Kasahara, H, Masuda, D, Tanigaki, F, Shimazu, T, Suzuki, H, Karahara, I, Soga, K, Hoson, T and Tayama, I (2013) Improvements in and actual performance of the plant experiment unit onboard kibo, the Japanese experiment module on the international space station. Advances in Space Research 51, 780–788.CrossRefGoogle Scholar
Youssef, H, Sait, SM and Adiche, H (2001) Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Engineering Applications of Artificial Intelligence 14, 167–181.Google Scholar
Yu, B, Yang, Z and Cheng, C (2007) Optimizing the distribution of shopping centers with parallel genetic algorithm. Engineering Applications of Artificial Intelligence 20, 215–223.CrossRefGoogle Scholar
Yu, G, Jin, Y and Olhofer, M (2019) References or preferences – rethinking many-objective evolutionary optimization. 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 2410–2417.CrossRefGoogle ScholarPubMed
Zhang, Q and Zhang, W (2007) Network partition for switched industrial ethernet using genetic algorithm. Engineering Applications of Artificial Intelligence 20, 79–88.CrossRefGoogle Scholar
Zhao, X, Hsu, C-Y, Chang, P-C and Li, L (2016) A genetic algorithm for the multi-objective optimization of mixed-model assembly line based on the mental workload. Engineering Applications of Artificial Intelligence 47, 140–146.CrossRefGoogle Scholar