IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i11p7640-7672d42525.html
   My bibliography  Save this article

Metaheuristic Algorithms Applied to Bioenergy Supply Chain Problems: Theory, Review, Challenges, and Future

Author

Listed:
  • Krystel K. Castillo-Villar

    (Department of Mechanical Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA)

Abstract

Bioenergy is a new source of energy that accounts for a substantial portion of the renewable energy production in many countries. The production of bioenergy is expected to increase due to its unique advantages, such as no harmful emissions and abundance. Supply-related problems are the main obstacles precluding the increase of use of biomass (which is bulky and has low energy density) to produce bioenergy. To overcome this challenge, large-scale optimization models are needed to be solved to enable decision makers to plan, design, and manage bioenergy supply chains. Therefore, the use of effective optimization approaches is of great importance. The traditional mathematical methods (such as linear, integer, and mixed-integer programming) frequently fail to find optimal solutions for non-convex and/or large-scale models whereas metaheuristics are efficient approaches for finding near-optimal solutions that use less computational resources. This paper presents a comprehensive review by studying and analyzing the application of metaheuristics to solve bioenergy supply chain models as well as the exclusive challenges of the mathematical problems applied in the bioenergy supply chain field. The reviewed metaheuristics include: (1) population approaches, such as ant colony optimization (ACO), the genetic algorithm (GA), particle swarm optimization (PSO), and bee colony algorithm (BCA); and (2) trajectory approaches, such as the tabu search (TS) and simulated annealing (SA). Based on the outcomes of this literature review, the integrated design and planning of bioenergy supply chains problem has been solved primarily by implementing the GA. The production process optimization was addressed primarily by using both the GA and PSO. The supply chain network design problem was treated by utilizing the GA and ACO. The truck and task scheduling problem was solved using the SA and the TS, where the trajectory-based methods proved to outperform the population-based methods.

Suggested Citation

  • Krystel K. Castillo-Villar, 2014. "Metaheuristic Algorithms Applied to Bioenergy Supply Chain Problems: Theory, Review, Challenges, and Future," Energies, MDPI, vol. 7(11), pages 1-33, November.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7640-7672:d:42525
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/11/7640/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/7/11/7640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michel Gendreau & Alain Hertz & Gilbert Laporte, 1994. "A Tabu Search Heuristic for the Vehicle Routing Problem," Management Science, INFORMS, vol. 40(10), pages 1276-1290, October.
    2. Mula, Josefa & Peidro, David & Díaz-Madroñero, Manuel & Vicens, Eduardo, 2010. "Mathematical programming models for supply chain production and transport planning," European Journal of Operational Research, Elsevier, vol. 204(3), pages 377-390, August.
    3. Vera, David & Carabias, Julio & Jurado, Francisco & Ruiz-Reyes, Nicolás, 2010. "A Honey Bee Foraging approach for optimal location of a biomass power plant," Applied Energy, Elsevier, vol. 87(7), pages 2119-2127, July.
    4. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    5. Gueguim Kana, E.B. & Oloke, J.K. & Lateef, A. & Adesiyan, M.O., 2012. "Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm," Renewable Energy, Elsevier, vol. 46(C), pages 276-281.
    6. Luca Maria Gambardella & Marco Dorigo, 2000. "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem," INFORMS Journal on Computing, INFORMS, vol. 12(3), pages 237-255, August.
    7. Pan, Quan-Ke & Wang, Ling & Li, Jun-Qing & Duan, Jun-Hua, 2014. "A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation," Omega, Elsevier, vol. 45(C), pages 42-56.
    8. Eglese, R. W., 1990. "Simulated annealing: A tool for operational research," European Journal of Operational Research, Elsevier, vol. 46(3), pages 271-281, June.
    9. Olivares-Benitez, Elias & Ríos-Mercado, Roger Z. & González-Velarde, José Luis, 2013. "A metaheuristic algorithm to solve the selection of transportation channels in supply chain design," International Journal of Production Economics, Elsevier, vol. 145(1), pages 161-172.
    10. Henry Venema & Paul Calamai, 2003. "Bioenergy Systems Planning Using Location–Allocation and Landscape Ecology Design Principles," Annals of Operations Research, Springer, vol. 123(1), pages 241-264, October.
    11. De Meyer, Annelies & Cattrysse, Dirk & Rasinmäki, Jussi & Van Orshoven, Jos, 2014. "Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 657-670.
    12. Taillard, E., 1990. "Some efficient heuristic methods for the flow shop sequencing problem," European Journal of Operational Research, Elsevier, vol. 47(1), pages 65-74, July.
    13. Sharma, B. & Ingalls, R.G. & Jones, C.L. & Khanchi, A., 2013. "Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 608-627.
    14. Md. Bedarul Alam & Reino Pulkki & Chander Shahi & Thakur Upadhyay, 2012. "Modeling Woody Biomass Procurement for Bioenergy Production at the Atikokan Generating Station in Northwestern Ontario, Canada," Energies, MDPI, vol. 5(12), pages 1-21, December.
    15. Niknam, Taher & Firouzi, Bahman Bahmani, 2009. "A practical algorithm for distribution state estimation including renewable energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2309-2316.
    16. Koulamas, C & Antony, SR & Jaen, R, 1994. "A survey of simulated annealing applications to operations research problems," Omega, Elsevier, vol. 22(1), pages 41-56, January.
    17. Namuli, R. & Jaumard, B. & Awasthi, A. & Pillay, P., 2013. "Optimisation of biomass waste to energy conversion systems for rural grid-connected applications," Applied Energy, Elsevier, vol. 102(C), pages 1013-1021.
    18. Christian Blum, 2008. "Beam-ACO for Simple Assembly Line Balancing," INFORMS Journal on Computing, INFORMS, vol. 20(4), pages 618-627, November.
    19. Flisberg, Patrik & Lidén, Bertil & Rönnqvist, Mikael, 2007. "A hybrid method based on linear programming and tabu search for routing of logging trucks," Discussion Papers 2007/14, Norwegian School of Economics, Department of Business and Management Science.
    20. Kurka, Thomas & Blackwood, David, 2013. "Selection of MCA methods to support decision making for renewable energy developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 225-233.
    21. Thomas, Douglas J. & Griffin, Paul M., 1996. "Coordinated supply chain management," European Journal of Operational Research, Elsevier, vol. 94(1), pages 1-15, October.
    22. Jäger-Waldau, Arnulf & Ossenbrink, Heinz, 2004. "Progress of electricity from biomass, wind and photovoltaics in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(2), pages 157-182, April.
    23. Jadranka Skorin-Kapov, 1990. "Tabu Search Applied to the Quadratic Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 2(1), pages 33-45, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fernández-Puratich, Harald & Rebolledo-Leiva, Ricardo & Hernández, Diógenes & Gómez-Lagos, Javier E. & Armengot-Carbo, Bruno & Oliver-Villanueva, José Vicente, 2021. "Bi-objective optimization of multiple agro-industrial wastes supply to a cogeneration system promoting local circular bioeconomy," Applied Energy, Elsevier, vol. 300(C).
    2. Andrzej Jezierski & Cezary Mańkowski & Rafał Śpiewak, 2021. "Energy Savings Analysis in Logistics of a Wind Farm Repowering Process: A Case Study," Energies, MDPI, vol. 14(17), pages 1-23, September.
    3. Saghaei, Mahsa & Ghaderi, Hadi & Soleimani, Hamed, 2020. "Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand," Energy, Elsevier, vol. 197(C).
    4. Kanematsu, Yuichiro & Oosawa, Kazutake & Okubo, Tatsuya & Kikuchi, Yasunori, 2017. "Designing the scale of a woody biomass CHP considering local forestry reformation: A case study of Tanegashima, Japan," Applied Energy, Elsevier, vol. 198(C), pages 160-172.
    5. Huang, Endai & Zhang, Xiaolei & Rodriguez, Luis & Khanna, Madhu & de Jong, Sierk & Ting, K.C. & Ying, Yibin & Lin, Tao, 2019. "Multi-objective optimization for sustainable renewable jet fuel production: A case study of corn stover based supply chain system in Midwestern U.S," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    6. Eliana M. A. Guerreiro & Maicon Silva & Marcio Guerreiro & Taís Carvalho & Attilio Converti & Hugo Valadares Siqueira & Cassiano Moro Piekarski, 2022. "Framework for Optimized Analysis of Waste Bioenergy Projects," Energies, MDPI, vol. 15(17), pages 1-15, August.
    7. Leonel J. R. Nunes & Sandra Silva, 2023. "Optimization of the Residual Biomass Supply Chain: Process Characterization and Cost Analysis," Logistics, MDPI, vol. 7(3), pages 1-21, August.
    8. Mahmoud Abdelsalam & Hatem Y. Diab, 2019. "Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-Grid Using a Meta-Heuristic MVO Algorithm," Energies, MDPI, vol. 12(21), pages 1-16, October.
    9. Mirkouei, Amin & Haapala, Karl R. & Sessions, John & Murthy, Ganti S., 2017. "A review and future directions in techno-economic modeling and optimization of upstream forest biomass to bio-oil supply chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 15-35.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ba, Birome Holo & Prins, Christian & Prodhon, Caroline, 2016. "Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective," Renewable Energy, Elsevier, vol. 87(P2), pages 977-989.
    2. Martinez-Valencia, Lina & Garcia-Perez, Manuel & Wolcott, Michael P., 2021. "Supply chain configuration of sustainable aviation fuel: Review, challenges, and pathways for including environmental and social benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    3. De Meyer, Annelies & Cattrysse, Dirk & Van Orshoven, Jos, 2015. "A generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS)," European Journal of Operational Research, Elsevier, vol. 245(1), pages 247-264.
    4. Chinese, D. & Patrizio, P. & Nardin, G., 2014. "Effects of changes in Italian bioenergy promotion schemes for agricultural biogas projects: Insights from a regional optimization model," Energy Policy, Elsevier, vol. 75(C), pages 189-205.
    5. Saghaei, Mahsa & Ghaderi, Hadi & Soleimani, Hamed, 2020. "Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand," Energy, Elsevier, vol. 197(C).
    6. Malladi, Krishna Teja & Sowlati, Taraneh, 2018. "Biomass logistics: A review of important features, optimization modeling and the new trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 587-599.
    7. Patrizio, P. & Leduc, S. & Chinese, D. & Dotzauer, E. & Kraxner, F., 2015. "Biomethane as transport fuel – A comparison with other biogas utilization pathways in northern Italy," Applied Energy, Elsevier, vol. 157(C), pages 25-34.
    8. Li, Lei & Manier, Hervé & Manier, Marie-Ange, 2019. "Hydrogen supply chain network design: An optimization-oriented review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 342-360.
    9. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.
    10. Ganesan, Viswanath Kumar & Sivakumar, Appa Iyer, 2006. "Scheduling in static jobshops for minimizing mean flowtime subject to minimum total deviation of job completion times," International Journal of Production Economics, Elsevier, vol. 103(2), pages 633-647, October.
    11. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    12. Yazan, Devrim Murat & Mandras, Giovanni & Garau, Giorgio, 2017. "Environmental and economic sustainability of integrated production in bio-refineries: The thistle case in Sardinia," Renewable Energy, Elsevier, vol. 102(PB), pages 349-360.
    13. Espinoza Pérez, Andrea Teresa & Camargo, Mauricio & Narváez Rincón, Paulo César & Alfaro Marchant, Miguel, 2017. "Key challenges and requirements for sustainable and industrialized biorefinery supply chain design and management: A bibliographic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 350-359.
    14. Hadi Karimi & Sandra D. Ekşioğlu & Michael Carbajales-Dale, 2021. "A biobjective chance constrained optimization model to evaluate the economic and environmental impacts of biopower supply chains," Annals of Operations Research, Springer, vol. 296(1), pages 95-130, January.
    15. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    16. Jonker, J.G.G. & Junginger, H.M. & Verstegen, J.A. & Lin, T. & Rodríguez, L.F. & Ting, K.C. & Faaij, A.P.C. & van der Hilst, F., 2016. "Supply chain optimization of sugarcane first generation and eucalyptus second generation ethanol production in Brazil," Applied Energy, Elsevier, vol. 173(C), pages 494-510.
    17. Kasmuri, N.H. & Kamarudin, S.K. & Abdullah, S.R.S. & Hasan, H.A. & Som, A.Md., 2017. "Process system engineering aspect of bio-alcohol fuel production from biomass via pyrolysis: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 914-923.
    18. Fatima Benbouzid-Si Tayeb & Karima Benatchba & Abd-Essalam Messiaid, 2018. "Game theory-based integration of scheduling with flexible and periodic maintenance planning in the permutation flowshop sequencing problem," Operational Research, Springer, vol. 18(1), pages 221-255, April.
    19. Wu, Juanjuan & Zhang, Jian & Yi, Weiming & Cai, Hongzhen & Li, Yang & Su, Zhanpeng, 2022. "Agri-biomass supply chain optimization in north China: Model development and application," Energy, Elsevier, vol. 239(PD).
    20. Bairamzadeh, Samira & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach," Renewable Energy, Elsevier, vol. 116(PA), pages 500-517.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7640-7672:d:42525. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.