IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v294y2020i1d10.1007_s10479-017-2697-7.html
   My bibliography  Save this article

Mathematical optimization models for fuelwood production

Author

Listed:
  • Konstantinos Petridis

    (University of Macedonia)

  • Garyfallos Arabatzis

    (Democritus University of Thrace)

  • Angelo Sifaleras

    (University of Macedonia)

Abstract

Forests are among the most sensitive systems in nature. This is attributed to the fact that, forests are directly affected by fluctuations in price of fossil fuels. Wood products and especially forest fuel products are accessible by anyone, without any prior processing. As forest fuel is a subsidy for fossil fuels (oil) for heating purposes, households turn to forest fuel especially in countries that are heavily impacted by economic recession. The over-exploitation of this natural resource leads the forest to abnormal situation and eventually to deforestation. The exhaust of the natural resource capital has negative impact not only on the local economy, where fuelwood market contributes especially in mountainous regions, but also on the environmental stability of ecosystems. In this paper, two multi-period Linear Programming models are proposed for management of coppice forests. The aim of these models is to maximize the Net Present Value, which is constructed as a function of the revenue from trading fuelwood (price times the logged quantities) minus the transportation cost from the forest to merchants. Two aspects have been investigated in this paper; sustainability and maximum yield. The sustainability aspect is guaranteed by imposing constraints for equalization of non-logged areas at the end of the planning horizon. With maximum yield aspect, the maximization of the logged quantities (and therefore the maximization of the objective function) is guaranteed. The model is solved for various scenarios regarding transportation cost. The applicability of the model is demonstrated through a real-world case study of an even coppice forest in Achladochori–Aggistro–Sidirokastro. The proposed model is easy to be implemented, since it uses only the initial conditions of the forest (area) and can be applied to even and uneven aged forests.

Suggested Citation

  • Konstantinos Petridis & Garyfallos Arabatzis & Angelo Sifaleras, 2020. "Mathematical optimization models for fuelwood production," Annals of Operations Research, Springer, vol. 294(1), pages 59-74, November.
  • Handle: RePEc:spr:annopr:v:294:y:2020:i:1:d:10.1007_s10479-017-2697-7
    DOI: 10.1007/s10479-017-2697-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2697-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-017-2697-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carlsson, Dick & Ronnqvist, Mikael, 2005. "Supply chain management in forestry--case studies at Sodra Cell AB," European Journal of Operational Research, Elsevier, vol. 163(3), pages 589-616, June.
    2. Mette Termansen, 2007. "Economies of scale and the optimality of rotational dynamics in forestry," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 37(4), pages 643-659, August.
    3. Konstantinos Voudouris & Maurizio Polemio & Nerantzis Kazakis & Angelo Sifaleras, 2010. "An Agricultural Decision Support System for Optimal Land Use Regarding Groundwater Vulnerability," International Journal of Information Systems and Social Change (IJISSC), IGI Global, vol. 1(4), pages 66-79, October.
    4. Grigoroudis, Evangelos & Petridis, Konstantinos & Arabatzis, Garyfallos, 2014. "RDEA: A recursive DEA based algorithm for the optimal design of biomass supply chain networks," Renewable Energy, Elsevier, vol. 71(C), pages 113-122.
    5. Konstantinos Petridis, 2015. "Optimal design of multi-echelon supply chain networks under normally distributed demand," Annals of Operations Research, Springer, vol. 227(1), pages 63-91, April.
    6. Demirci, Mehmet & Bettinger, Pete, 2015. "Using mixed integer multi-objective goal programming for stand tending block designation: A case study from Turkey," Forest Policy and Economics, Elsevier, vol. 55(C), pages 28-36.
    7. Zhang, Fengli & Johnson, Dana M. & Johnson, Mark A., 2012. "Development of a simulation model of biomass supply chain for biofuel production," Renewable Energy, Elsevier, vol. 44(C), pages 380-391.
    8. Flisberg, Patrik & Frisk, Mikael & Rönnqvist, Mikael & Guajardo, Mario, 2015. "Potential savings and cost allocations for forest fuel transportation in Sweden: A country-wide study," Energy, Elsevier, vol. 85(C), pages 353-365.
    9. Arabatzis, Garyfallos & Petridis, Konstantinos & Galatsidas, Spyros & Ioannou, Konstantinos, 2013. "A demand scenario based fuelwood supply chain: A conceptual model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 687-697.
    10. Michael Bussieck & Alexander Meeraus, 2007. "Algebraic modeling for IP and MIP (GAMS)," Annals of Operations Research, Springer, vol. 149(1), pages 49-56, February.
    Full references (including those not matched with items on IDEAS)

    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. Nader Naderializadeh & Kevin A. Crowe, 2020. "Formulating the integrated forest harvest-scheduling model to reduce the cost of the road-networks," Operational Research, Springer, vol. 20(4), pages 2283-2306, December.
    2. Eriksson, Anders & Eliasson, Lars & Sikanen, Lauri & Hansson, Per-Anders & Jirjis, Raida, 2017. "Evaluation of delivery strategies for forest fuels applying a model for Weather-driven Analysis of Forest Fuel Systems (WAFFS)," Applied Energy, Elsevier, vol. 188(C), pages 420-430.
    3. Azadeh, Ali & Vafa Arani, Hamed, 2016. "Biodiesel supply chain optimization via a hybrid system dynamics-mathematical programming approach," Renewable Energy, Elsevier, vol. 93(C), pages 383-403.
    4. 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.
    5. Ardavan Babaei & Majid Khedmati & Mohammad Reza Akbari Jokar, 2023. "A new branch and efficiency algorithm for an optimal design of the supply chain network in view of resilience, inequity and traffic congestion," Annals of Operations Research, Springer, vol. 321(1), pages 49-78, February.
    6. Grigoroudis, Evangelos & Petridis, Konstantinos & Arabatzis, Garyfallos, 2014. "RDEA: A recursive DEA based algorithm for the optimal design of biomass supply chain networks," Renewable Energy, Elsevier, vol. 71(C), pages 113-122.
    7. Konstantinos Petridis & Prasanta Kumar Dey & Ali Emrouznejad, 2017. "A branch and efficiency algorithm for the optimal design of supply chain networks," Annals of Operations Research, Springer, vol. 253(1), pages 545-571, June.
    8. Konstantinos Ioannou & Georgios Tsantopoulos & Garyfallos Arabatzis & Zacharoula Andreopoulou & Eleni Zafeiriou, 2018. "A Spatial Decision Support System Framework for the Evaluation of Biomass Energy Production Locations: Case Study in the Regional Unit of Drama, Greece," Sustainability, MDPI, vol. 10(2), pages 1-22, February.
    9. Mobtaker, A. & Ouhimmou, M. & Audy, J.-F. & Rönnqvist, M., 2021. "A review on decision support systems for tactical logistics planning in the context of forest bioeconomy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    10. repec:thr:techub:10010:y:2020:i:1:p:436-447 is not listed on IDEAS
    11. 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.
    12. Liu, Liwei & Ye, Junhong & Zhao, Yufei & Zhao, Erdong, 2015. "The plight of the biomass power generation industry in China – A supply chain risk perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 680-692.
    13. Anna Duczkowska & Ewa Kulińska & Zbigniew Plutecki & Joanna Rut, 2022. "Sustainable Agro-Biomass Market for Urban Heating Using Centralized District Heating System," Energies, MDPI, vol. 15(12), pages 1-23, June.
    14. Hoogstra-Klein, Marjanke A. & Meijboom, Kars, 2021. "A qualitative exploration of the wood product supply chain – investigating the possibilities and desirability of an increased demand orientation," Forest Policy and Economics, Elsevier, vol. 133(C).
    15. Nguyen, Trung Thanh & Nghiem, Nhung, 2016. "Optimal forest rotation for carbon sequestration and biodiversity conservation by farm income levels," Forest Policy and Economics, Elsevier, vol. 73(C), pages 185-194.
    16. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    17. Petridis, Konstantinos & Tampakoudis, Ioannis & Drogalas, George & Kiosses, Nikolaos, 2022. "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, Elsevier, vol. 61(C).
    18. Rendon-Sagardi, Miguel A. & Sanchez-Ramirez, Cuauhtemoc & Cortes-Robles, Guillermo & Alor-Hernandez, Giner & Cedillo-Campos, Miguel G., 2014. "Dynamic analysis of feasibility in ethanol supply chain for biofuel production in Mexico," Applied Energy, Elsevier, vol. 123(C), pages 358-367.
    19. 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.
    20. Palander, Teijo & Haavikko, Hanna & Kärhä, Kalle, 2018. "Towards sustainable wood procurement in forest industry – The energy efficiency of larger and heavier vehicles in Finland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 100-118.
    21. Milad Kolagar & Seyed Mohammad Hassan Hosseini & Ramin Felegari & Parviz Fattahi, 2020. "Policy-making for renewable energy sources in search of sustainable development: a hybrid DEA-FBWM approach," Environment Systems and Decisions, Springer, vol. 40(4), pages 485-509, December.

    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:spr:annopr:v:294:y:2020:i:1:d:10.1007_s10479-017-2697-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.