MULTI-OBJECTIVE OPTIMIZATION MODEL FOR BIOENERGY SUPPLY CHAIN DISTRIBUTION
DOI:
https://doi.org/10.24961/j.tek.ind.pert.2025.35.2.144Abstract
Determining optimal distribution routes in managing efficient transportation distances is a challenge in the
bioenergy supply chain. Adequate distribution arrangements can improve distribution in the bioenergy supply
chain. Optimizing distribution arrangements can help reduce transportation costs, avoid delays, and improve
distribution efficiency. The optimal distribution strategy must consider choosing routes that minimize distribution
distances from agricultural centres to power plants. This study aimed to determine the location of collection points
and optimal distribution routes in the distribution of the bioenergy supply chain. Centre of gravity (COG) method
for locating collection centres. Determination of optimal distribution routes with a combination of ME-MCDM
and spatial Dijkstra approaches. The results showed that the centre point at -60.53'42.6" S and 105.035'70.8" E is
the biomass collection area for suitable bioenergy. Our proposed method of spatial combination of Dijkstra and
multicriteria decision making (ME-MCDM) based on Expert considerations on a more logical bioenergy optimal
distribution solution, taking into account sustainability, has TAV 1700,74 with P (V1, V5) = (V1, V2, V4, V5).
Managerial Implications for Bioenergy-Producing Companies and Policy Stakeholders From operational
efficiency to reducing carbon emissions, this model can help companies better manage their bioenergy distribution
and contribute to business sustainability.
Keywords: bioenergy distribution, Centre of Gravity (COG), model optimisation, ME-MCDM Spatial Dijkstra
References
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Baghaei Daemi, H., Kasap, S. and Mahmoudabadi, A. (2022) ‘Proposing risk equity approaches of risk and distance based in route selection and site location for hazardous material transportation’, Case Studies on Transport Policy, 10(4), pp. 2486–2496. doi: 10.1016/j.cstp.2022.11.001.
Biuki, M., Kazemi, A. and Alinezhad, A. (2020) ‘An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network’, Journal of Cleaner Production, 260, p. 120842. doi: 10.1016/j.jclepro.2020.120842.
Chen, J. et al. (2023) ‘The impact of renewable energy consumption on lithium trade patterns : An industrial chain perspective’, Resources Policy, 85(PA), p. 103837. doi: 10.1016/j.resourpol.2023.103837.
Chia, S. R. et al. (2018) ‘Sustainable approaches for algae utilisation in bioenergy production’, Renewable Energy, 129, pp. 838–852. doi: 10.1016/j.renene.2017.04.001.
Dijkstra, E. W. (1959) ‘Dijkstra.Pptx’, Numer. Math., 271, pp. 269–271.
Duarah, P. et al. (2022) ‘A review on global perspectives of sustainable development in bioenergy generation’, Bioresource Technology, 348(January), p. 126791. doi: 10.1016/j.biortech.2022.126791.
Duc, D. N., Meejaroen, P. and Nananukul, N. (2021) ‘Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration’, Energy Reports, 7, pp. 6833–6843. doi: 10.1016/j.egyr.2021.10.071.
French, S. (2023) ‘Reflections on 50 Years of MCDM: Issues and Future Research Needs’, EURO Journal on Decision Processes, 11(March), p. 100030. doi: 10.1016/j.ejdp.2023.100030.
Gao, E., Sowlati, T. and Akhtari, S. (2019) ‘Profit allocation in collaborative bioenergy and biofuel supply chains’, Energy, 188, p. 116013. doi: 10.1016/j.energy.2019.116013.
Jason et al. (2023) ‘Dijkstra’s algorithm to find the nearest vaccine location’, Procedia Computer Science, 216(2022), pp. 5–12. doi: 10.1016/j.procs.2022.12.105.
Kharrat, N. et al. (2022) ‘A sustainable approach for a collaborative distribution network’, Transportation Engineering, 9(December 2021), p. 100131. doi: 10.1016/j.treng.2022.100131.
Krisnaningsih, E. et al. (2022) ‘Decision Model for Determining the Feasibility of Rice-Based Bioenergy Supply Chain Development Area with Fuzzy Logic-AHP Approach’, IOP Conference Series: Earth and Environmental Science, 1034(1). doi: 10.1088/1755-1315/1034/1/012007.
Li, L. et al. (2023) ‘Spatio-temporal evolution and gravity center change of carbon emissions in the Guangdong-Hong Kong-Macao greater bay area and the influencing factors’, Heliyon, 9(6), p. e16596. doi: 10.1016/j.heliyon.2023.e16596.
Londoño-Pulgarin, D. et al. (2021) ‘Fossil or bioenergy? Global fuel market trends’, Renewable and Sustainable Energy Reviews, 143(October 2020). doi: 10.1016/j.rser.2021.110905.
Manikandan, S. et al. (2023) ‘Critical review of biochemical pathways to transformation of waste and biomass into bioenergy’, Bioresource Technology, 372(December 2022), p. 128679. doi: 10.1016/j.biortech.2023.128679.
Massam, B. H. (1988) ‘Multi-Criteria Decision Making (MCDM) techniques in planning’, Progress in Planning, 30(PART 1), pp. 1–84. doi: 10.1016/0305-9006(88)90012-8.
De Meyer, A. et al. (2014) ‘Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review’, Renewable and Sustainable Energy Reviews, 31, pp. 657–670. doi: 10.1016/j.rser.2013.12.036.
Meyer, M. A. and Priess, J. A. (2014) ‘Indicators of bioenergy-related certification schemes - An analysis of the quality and comprehensiveness for assessing local/regional environmental impacts’, Biomass and Bioenergy, 65, pp. 151–169. doi: 10.1016/j.biombioe.2014.03.041.
Moreno-Solaz, H. et al. (2023) ‘Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method’, Heliyon, 9(4). doi: 10.1016/j.heliyon.2023.e15481.
Noor-E-Alam, M. et al. (2011) ‘Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)’, Knowledge-Based Systems, 24(3), pp. 367–377. doi: 10.1016/j.knosys.2010.10.006.
Peprah, F. et al. (2023) ‘Economic evaluation of solar PV electricity prosumption in Ghana’, Solar Compass, 5(February), p. 100035. doi: 10.1016/j.solcom.2023.100035.
Rabbani, M. et al. (2018) ‘Developing a sustainable supply chain optimization model for switchgrass-based bioenergy production: A case study’, Journal of Cleaner Production, 200, pp. 827–843. doi: 10.1016/j.jclepro.2018.07.226.
Rather, R. A. et al. (2022) ‘Bioenergy: a foundation to environmental sustainability in a changing global climate scenario’, Journal of King Saud University - Science, 34(1), p. 101734. doi: 10.1016/j.jksus.2021.101734.
Razm, S. et al. (2021) ‘A two-phase sequential approach to design bioenergy supply chains under uncertainty and social concerns’, Computers and Chemical Engineering, 145, pp. 0–44. doi: 10.1016/j.compchemeng.2020.107131.
Santos, A. et al. (2019) ‘Assessment and optimization of sustainable forest wood supply chains – A systematic literature review’, Forest Policy and Economics, 105(May), pp. 112–135. doi: 10.1016/j.forpol.2019.05.026.
Sargent, R. G. (2010) ‘Verification And Validation of Simulation Models’, Simulation, pp. 3212–3219.
Sarkar, B. et al. (2021) ‘A flexible biofuel and bioenergy production system with transportation disruption under a sustainable supply chain network’, Journal of Cleaner Production, 317(September 2020), p. 128079. doi: 10.1016/j.jclepro.2021.128079.
Seay, J. R. and Badurdeen, F. F. (2014) ‘Current trends and directions in achieving sustainability in the biofuel and bioenergy supply chain’, Current Opinion in Chemical Engineering, 6, pp. 55–60. doi: 10.1016/j.coche.2014.09.006.
Shahrier, M. and Hasnat, A. (2021) ‘Route optimization issues and initiatives in Bangladesh: The context of regional significance’, Transportation Engineering, 4(December 2020), p. 100054. doi: 10.1016/j.treng.2021.100054.
Techane, L. B. et al. (2022) ‘Geographical information system based optimal path routing of distribution networks’, Heliyon, 8(5), p. e09397. doi: 10.1016/j.heliyon.2022.e09397.
Tesfamichael, B. et al. (2021) ‘Designing and planning of Ethiopia’s biomass-to-biofuel supply chain through integrated strategic-tactical optimization model considering economic dimension’, Computers and Chemical Engineering, 153. doi: 10.1016/j.compchemeng.2021.107425.
Wan, Y. et al. (2023) ‘Conditions for profitable operation of P2X energy hubs to meet local demand under to with energy market access’, Advances in Applied Energy, 10(November 2022). doi: 10.1016/j.adapen.2023.100127.
Wang, S. et al. (2022a) ‘StrawFeed model: An integrated model of straw feedstock supply chain for bioenergy in China’, Resources, Conservation and Recycling, 185(May), p. 106439. doi: 10.1016/j.resconrec.2022.106439.
Wang, S. et al. (2022b) ‘StrawFeed model: An integrated model of straw feedstock supply chain for bioenergy in China’, Resources, Conservation and Recycling, 185(June), p. 106439. doi: 10.1016/j.resconrec.2022.106439.
Yasumiishi, E. M. et al. (2020) ‘Climate-related changes in the biomass and distribution of small pelagic fishes in the eastern Bering Sea during late summer, 2002–2018’, Deep-Sea Research Part II: Topical Studies in Oceanography, 181–182(March), p. 104907. doi: 10.1016/j.dsr2.2020.104907.
Yue, D., You, F. and Snyder, S. W. (2014) ‘Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges’, Computers and Chemical Engineering, 66, pp. 36–56. doi: 10.1016/j.compchemeng.2013.11.016.
Zhang, J. et al. (2022) ‘Optimisation of Logistic Model Using Geographic Information Systems: A Case Study of Biomass-based Combined Heat & Power Generation in China’, Applications in Energy and Combustion Science, 10(September 2021), p. 100060. doi: 10.1016/j.jaecs.2022.100060.
Afzal, A. et al. (2023) ‘Is saving the non-renewable resources worthwhile? Evidence of paradox of plenty on human capital development’, Resources Policy, 83(March), p. 103728. doi: 10.1016/j.resourpol.2023.103728.
Al-Hawari, T. et al. (2022) ‘The effects of pre or post delay variable changes in discrete event simulation and combined DES/system dynamics approaches in modelling supply chain performance’, Journal of Simulation, 16(2), pp. 147–165. doi: 10.1080/17477778.2020.1764399.
Baghaei Daemi, H., Kasap, S. and Mahmoudabadi, A. (2022) ‘Proposing risk equity approaches of risk and distance based in route selection and site location for hazardous material transportation’, Case Studies on Transport Policy, 10(4), pp. 2486–2496. doi: 10.1016/j.cstp.2022.11.001.
Biuki, M., Kazemi, A. and Alinezhad, A. (2020) ‘An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network’, Journal of Cleaner Production, 260, p. 120842. doi: 10.1016/j.jclepro.2020.120842.
Chen, J. et al. (2023) ‘The impact of renewable energy consumption on lithium trade patterns : An industrial chain perspective’, Resources Policy, 85(PA), p. 103837. doi: 10.1016/j.resourpol.2023.103837.
Chia, S. R. et al. (2018) ‘Sustainable approaches for algae utilisation in bioenergy production’, Renewable Energy, 129, pp. 838–852. doi: 10.1016/j.renene.2017.04.001.
Dijkstra, E. W. (1959) ‘Dijkstra.Pptx’, Numer. Math., 271, pp. 269–271.
Duarah, P. et al. (2022) ‘A review on global perspectives of sustainable development in bioenergy generation’, Bioresource Technology, 348(January), p. 126791. doi: 10.1016/j.biortech.2022.126791.
Duc, D. N., Meejaroen, P. and Nananukul, N. (2021) ‘Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration’, Energy Reports, 7, pp. 6833–6843. doi: 10.1016/j.egyr.2021.10.071.
French, S. (2023) ‘Reflections on 50 Years of MCDM: Issues and Future Research Needs’, EURO Journal on Decision Processes, 11(March), p. 100030. doi: 10.1016/j.ejdp.2023.100030.
Gao, E., Sowlati, T. and Akhtari, S. (2019) ‘Profit allocation in collaborative bioenergy and biofuel supply chains’, Energy, 188, p. 116013. doi: 10.1016/j.energy.2019.116013.
Jason et al. (2023) ‘Dijkstra’s algorithm to find the nearest vaccine location’, Procedia Computer Science, 216(2022), pp. 5–12. doi: 10.1016/j.procs.2022.12.105.
Kharrat, N. et al. (2022) ‘A sustainable approach for a collaborative distribution network’, Transportation Engineering, 9(December 2021), p. 100131. doi: 10.1016/j.treng.2022.100131.
Krisnaningsih, E. et al. (2022) ‘Decision Model for Determining the Feasibility of Rice-Based Bioenergy Supply Chain Development Area with Fuzzy Logic-AHP Approach’, IOP Conference Series: Earth and Environmental Science, 1034(1). doi: 10.1088/1755-1315/1034/1/012007.
Li, L. et al. (2023) ‘Spatio-temporal evolution and gravity center change of carbon emissions in the Guangdong-Hong Kong-Macao greater bay area and the influencing factors’, Heliyon, 9(6), p. e16596. doi: 10.1016/j.heliyon.2023.e16596.
Londoño-Pulgarin, D. et al. (2021) ‘Fossil or bioenergy? Global fuel market trends’, Renewable and Sustainable Energy Reviews, 143(October 2020). doi: 10.1016/j.rser.2021.110905.
Manikandan, S. et al. (2023) ‘Critical review of biochemical pathways to transformation of waste and biomass into bioenergy’, Bioresource Technology, 372(December 2022), p. 128679. doi: 10.1016/j.biortech.2023.128679.
Massam, B. H. (1988) ‘Multi-Criteria Decision Making (MCDM) techniques in planning’, Progress in Planning, 30(PART 1), pp. 1–84. doi: 10.1016/0305-9006(88)90012-8.
De Meyer, A. et al. (2014) ‘Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review’, Renewable and Sustainable Energy Reviews, 31, pp. 657–670. doi: 10.1016/j.rser.2013.12.036.
Meyer, M. A. and Priess, J. A. (2014) ‘Indicators of bioenergy-related certification schemes - An analysis of the quality and comprehensiveness for assessing local/regional environmental impacts’, Biomass and Bioenergy, 65, pp. 151–169. doi: 10.1016/j.biombioe.2014.03.041.
Moreno-Solaz, H. et al. (2023) ‘Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method’, Heliyon, 9(4). doi: 10.1016/j.heliyon.2023.e15481.
Noor-E-Alam, M. et al. (2011) ‘Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)’, Knowledge-Based Systems, 24(3), pp. 367–377. doi: 10.1016/j.knosys.2010.10.006.
Peprah, F. et al. (2023) ‘Economic evaluation of solar PV electricity prosumption in Ghana’, Solar Compass, 5(February), p. 100035. doi: 10.1016/j.solcom.2023.100035.
Rabbani, M. et al. (2018) ‘Developing a sustainable supply chain optimization model for switchgrass-based bioenergy production: A case study’, Journal of Cleaner Production, 200, pp. 827–843. doi: 10.1016/j.jclepro.2018.07.226.
Rather, R. A. et al. (2022) ‘Bioenergy: a foundation to environmental sustainability in a changing global climate scenario’, Journal of King Saud University - Science, 34(1), p. 101734. doi: 10.1016/j.jksus.2021.101734.
Razm, S. et al. (2021) ‘A two-phase sequential approach to design bioenergy supply chains under uncertainty and social concerns’, Computers and Chemical Engineering, 145, pp. 0–44. doi: 10.1016/j.compchemeng.2020.107131.
Santos, A. et al. (2019) ‘Assessment and optimization of sustainable forest wood supply chains – A systematic literature review’, Forest Policy and Economics, 105(May), pp. 112–135. doi: 10.1016/j.forpol.2019.05.026.
Sargent, R. G. (2010) ‘Verification And Validation of Simulation Models’, Simulation, pp. 3212–3219.
Sarkar, B. et al. (2021) ‘A flexible biofuel and bioenergy production system with transportation disruption under a sustainable supply chain network’, Journal of Cleaner Production, 317(September 2020), p. 128079. doi: 10.1016/j.jclepro.2021.128079.
Seay, J. R. and Badurdeen, F. F. (2014) ‘Current trends and directions in achieving sustainability in the biofuel and bioenergy supply chain’, Current Opinion in Chemical Engineering, 6, pp. 55–60. doi: 10.1016/j.coche.2014.09.006.
Shahrier, M. and Hasnat, A. (2021) ‘Route optimization issues and initiatives in Bangladesh: The context of regional significance’, Transportation Engineering, 4(December 2020), p. 100054. doi: 10.1016/j.treng.2021.100054.
Techane, L. B. et al. (2022) ‘Geographical information system based optimal path routing of distribution networks’, Heliyon, 8(5), p. e09397. doi: 10.1016/j.heliyon.2022.e09397.
Tesfamichael, B. et al. (2021) ‘Designing and planning of Ethiopia’s biomass-to-biofuel supply chain through integrated strategic-tactical optimization model considering economic dimension’, Computers and Chemical Engineering, 153. doi: 10.1016/j.compchemeng.2021.107425.
Wan, Y. et al. (2023) ‘Conditions for profitable operation of P2X energy hubs to meet local demand under to with energy market access’, Advances in Applied Energy, 10(November 2022). doi: 10.1016/j.adapen.2023.100127.
Wang, S. et al. (2022a) ‘StrawFeed model: An integrated model of straw feedstock supply chain for bioenergy in China’, Resources, Conservation and Recycling, 185(May), p. 106439. doi: 10.1016/j.resconrec.2022.106439.
Wang, S. et al. (2022b) ‘StrawFeed model: An integrated model of straw feedstock supply chain for bioenergy in China’, Resources, Conservation and Recycling, 185(June), p. 106439. doi: 10.1016/j.resconrec.2022.106439.
Yasumiishi, E. M. et al. (2020) ‘Climate-related changes in the biomass and distribution of small pelagic fishes in the eastern Bering Sea during late summer, 2002–2018’, Deep-Sea Research Part II: Topical Studies in Oceanography, 181–182(March), p. 104907. doi: 10.1016/j.dsr2.2020.104907.
Yue, D., You, F. and Snyder, S. W. (2014) ‘Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges’, Computers and Chemical Engineering, 66, pp. 36–56. doi: 10.1016/j.compchemeng.2013.11.016.
Zhang, J. et al. (2022) ‘Optimisation of Logistic Model Using Geographic Information Systems: A Case Study of Biomass-based Combined Heat & Power Generation in China’, Applications in Energy and Combustion Science, 10(September 2021), p. 100060. doi: 10.1016/j.jaecs.2022.100060.
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2025-08-30
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MULTI-OBJECTIVE OPTIMIZATION MODEL FOR BIOENERGY SUPPLY CHAIN DISTRIBUTION. (2025). Jurnal Teknologi Industri Pertanian, 35(2), 144-156. https://doi.org/10.24961/j.tek.ind.pert.2025.35.2.144
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