Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model


  • Foo Fong Yeng ᵃMathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM) Johor Branch, Pasir Gudang Campus, Jalan Purnama, Bandar Seri Alam, 81750 Masai, Johor, Malaysia ; ᵇDepartment of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, MalaysiaMathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM) Johor Branch, Pasir Gudang Campus, Jalan Purnama, Bandar Seri Alam, 81750 Masai, Johor, Malaysia ; bDepartment of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, MalaysiaMathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM) Johor Branch, Pasir Gudang Campus, Jalan Purnama, Bandar Seri Alam, 81750 Masai, Johor, Malaysia ; bDepartment of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Zaitul Marlizawati Zainuddin ᵇDepartment of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia; ᶜUTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Ibnu Sina Institute for Scientific and Industrial Research (ISI-SIR), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Hang See Pheng Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia




Location-routing problem, biomass supply chain, palm oil biomass supply chain, mixed integer linear programming, multi-objective optimization


Malaysia can convert agricultural wastes (biomass) into biofuel to reduce fossil fuel dependency and solve the disposal problem. As one of the largest palm oil producers, Malaysia has an abundance of palm oil biomass, but the biomass has high humidity, low energy density, and is scattered geographically. Establishing collection facilities with pretreatment operations is suggested to collect the biomass and improve its quality. Nevertheless, the facility placement and vehicle routing decisions significantly affect the total cost and operational efficiency. Hence, this study develops a model to address the location-routing problem and quantifies the pretreatment operation to customize the process in the biomass supply chain. This research also addresses sustainability from all dimensions through multi-objective optimization. The model minimizes costs, reduces negative social impacts by considering population densities, and measures environmental performance through CO2 emissions. The study first optimized each objective function separately and then conducted a multi-objective optimization using a weighted sum approach. Optimizing each objective function individually will achieve the best outcome for each dimension, but enhancing one objective would impair the others. However, multi-objective optimization shows some compensation for the performances where economic, social, and environmental indicator values decreased by 0.36%, 6.58%, and 15.28%, respectively. The results demonstrate that the model adjusts the locational and routing decisions based on different goals.


Cao, J. X., Zhang, Z. & Zhou, Y. (2021). A location-routing problem for biomass supply chains. Comput Ind Eng., 152, 107017.

Aziz, N. F., Chamhuri, N. & Batt, P. J. (2021). Barriers and benefits arising from the adoption of sustainable certification for smallholder oil palm producers in Malaysia: A systematic review of literature. Sustainability, 13(18), 10009.

Ismail, N. W., Kamal, S. N. M., Firdaus, M. & Hariri, N. M. (2022). Export demand of palm oil in Malaysia: Analysis using ARDL approach. Asian Journal of Agriculture and Rural Development, 12(3), 157-163.

Sukiran, M. A., Abnisa, F., Wan Daud, W. M. A., Abu Bakar, N. & Loh, S. K. (2017). A review of torrefaction of oil palm solid wastes for biofuel production. Energy Convers and Manage., 149, 101-120.

Menon, N. R., Ab Rahman, Z. & Abu Bakar, N. (2003). Empty fruit bunches evaluation: Mulch in plantation vs. fuel for electricity generation. Oil Palm Industry Economic Journal, 3, 15-20.

Che Hamzah, N. H., Yahya, A., Che Man, H. & Samsu Baharuddin, A. (2018). Effect of pretreatments on compost production from shredded oil palm empty fruit bunch with palm oil mill effluent anaerobic sludge and chicken manure. Bioresources, 13(3), 4998-5012.

Kulim Malaysia. (2019). KULIM (Malaysia) Berhad Integrated Annual Report 2019. http://integrated-report.kulim.com.my/files/document/1080/KULIM (Malaysia) Berhad Integrated Annual Report 2019.pdf.

Méndez-Vázquez, M. A., Gómez-Castro, F. I., Ponce-Ortega, J. M., Serafín-Muñoz, A. H., Santibañez-Aguilar, J. E. & El-Halwagi, M. M. (2017). Mathematical optimization of a supply chain for the production of fuel pellets from residual biomass. Clean Technol and Envir., 19(3), 721-734.

Sarker, B. R., Wu, B. & Paudel, K. P. (2018). Optimal number and location of storage hubs and biogas production reactors in farmlands with allocation of multiple feedstocks. Appl Math Model, 55, 447-465.

Sarker, B. R., Wu, B. & Paudel, K. P. (2019). Modeling and optimization of a supply chain of renewable biomass and biogas: Processing plant location. Appl Energ., 239, 343-355.

] Saadati, M. & Hosseininezhad, S. J. (2019). Designing a hub location model in a bagasse-based bioethanol supply chain network in Iran (case study: Iran sugar industry). Biomass and Bioenerg., 122, 238-256.

Serrano-Hernandez, A. & Faulin, J. (2019). Locating a biorefinery in Northern Spain: Decision making and economic consequences. Socio Econ Plan Sci., 66, 82–91.

Schröder, T., Lauven, L. P. & Geldermann, J. (2018). Improving biorefinery planning: Integration of spatial data using exact optimization nested in an evolutionary strategy. Eur J of Oper Res., 264(3), 1005-1019.

Zhang, F., Wang, J., Liu, S., Zhang, S. & Sutherland, J. W. (2017). Integrating GIS with optimization method for a biofuel feedstock supply chain. Biomass and Bioenerg., 98, 194-205.

Soha, T., Papp, L., Csontos, C. & Munkacsy, B. (2021). The importance of high crop residue demand on biogas plant site selection, scaling and feedstock allocation - A regional scale concept in a Hungarian study area. Renew Sust Energ Rev., 141, 110822.

Sahoo, K., Hawkins, G. L., Yao, X. A., Samples, K. & Mani, S. (2016). GIS-based biomass assessment and supply logistics system for a sustainable biorefinery: A case study with cotton stalks in the Southeastern US. Appl Energ., 182, 260-273.

Jayarathna, L., Kent, G., O’Hara, I. & Hobson, P. (2020). A Geographical information system based framework to identify optimal location and size of biomass energy plants using single or multiple biomass types. Appl Energ., 275, 115398.

Razm, S., Dolgui, A., Hammami, R., Brahimi, N., Nickel, S. & Sahebi, H. (2021). A two-phase sequential approach to design bioenergy supply chains under uncertainty and social concerns. Comput Chem Eng., 145, 107131.

Zhang, F., Johnson, D., Johnson, M., Watkins, D., Froese, R. & Wang, J. (2016). Decision support system integrating GIS with simulation and optimisation for a biofuel supply chain. Renew Energ., 85, 740-748.

León-Olivares, E., Minor-Popocatl, H., Aguilar-Mejía, O. & Sánchez-Partida, D. (2020). Optimization of the supply chain in the production of ethanol from agricultural biomass using Mixed-Integer Linear Programming (MILP): A Case Study. Math Probl Eng., 2020, 6029507.

Castro-Peña, M. Y., Peñuela, C. A. & González, J. G. (2019). Design of a supply chain to produce ethanol from one residuum and two coffee by-products. Uncertain Supply Chain Management, 7(4), 767-782.

Galanopoulos, C., Barletta, D. & Zondervan, E. (2018). A decision support platform for a bio-based supply chain: Application to the region of Lower Saxony and Bremen (Germany). Comput Chem Eng., 115, 233-242.

San Juan, J. L. G., Aviso, K. B., Tan, R. R. & Sy, C. L. (2019). A multi-objective optimization model for the design of biomass co-firing networks integrating feedstock quality considerations. Energies, 12(11), 2252.

Chugh, S., Yu, T. E., Jackson, S. W., Larson, J. A., English, B. C. & Cho, S.-H. (2016). Economic analysis of alternative logistics systems for Tennessee-produced switchgrass to penetrate energy markets. Biomass and Bioenerg., 85, 25-34.

Rabbani, M., Saravi, N. A., Farrokhi-Asl, H., Lim, S. F. W. T. & Tahaei, Z. (2018). Developing a sustainable supply chain optimization model for switchgrass-based bioenergy production: A case study. J Clean Prod., 200, 827-843.

Park, Y. S., Szmerekovsky, J. & Dybing, A. (2019). Optimal location of biogas plants in supply chains under carbon effects: Insight from a case study on animal manure in North Dakota. J Adv Transport, 2019, 5978753.

Gital Durmaz, Y. & Bilgen, B. (2020). Multi-objective optimization of sustainable biomass supply chain network design. Appl Energ., 272, 115259.

Rabbani, M., Momen, S., Akbarian-Saravi, N., Farrokhi-Asl, H. & Ghelichi, Z. (2020). Optimal design for sustainable bioethanol supply chain considering the bioethanol production strategies: A case study. Comput Chem Eng., 134, 106720.

Ganev, E. I., Dzhelil, Y. R., Ivanov, B. B., Vaklieva-Bancheva, N. G. & Kirilova, E. G. (2020). Optimal design of a sustainable integrated biodiesel/diesel supply chain using first and second generations bioresources. Chem Engineer Trans., 81, 67-72.

Mahjoub, N., Sahebi, H., Mazdeh, M. & Teymouri, A. (2020). Optimal design of the second and third generation biofuel supply network by a multi-objective model. J Clean Prod., 256, 120355.

Ivanov, B. (2018). Multi-period deterministic model of sustainable integrated of hybrid first and second generation bioethanol supply chains for synthesis and renovation. Bulg Chem Commun., 50, 24-35.

Hosseinalizadeh, R., Arshadi Khamseh, A. & Akhlaghi, M. M. (2019). A multi-objective and multi-period model to design a strategic development program for biodiesel fuels. Sustainable Energy Technologies and Assessments, 36, 100545.

De Meyer, A., Cattrysse, D. & Van Orshoven, J. (2015). A generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS). Eur J of Oper Res., 245(1), 247-264.

Arabi, M., Yaghoubi, S. & Tajik, J. (2019). A mathematical model for microalgae-based biobutanol supply chain network design under harvesting and drying uncertainties. Energy, 179, 1004-1016.

Arabi, M., Yaghoubi, S. & Tajik, J. (2019). Algal biofuel supply chain network design with variable demand under alternative fuel price uncertainty: A case study. Comput Chem Eng., 130, 106528.

Mohseni, S. & Pishvaee, M. S. (2016). A robust programming approach towards design and optimization of microalgae-based biofuel supply chain. Comput Ind Eng., 100, 58-71.

Ghaderi, H., Moini, A. & Pishvaee, M. S. (2018). A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design. J Clean Prod., 179, 368-406.

Kwon, O. & Han, J. (2021). Waste-to-bioethanol supply chain network: A deterministic model. Appl Energ., 300, 117381.

Zhao, X.-G. & Li, A. (2016). A multi-objective sustainable location model for biomass power plants: Case of China. Energy, 112, 1184-1193.

Salleh, S. F., Gunawan, M. F., Zulkarnain, M. F. B., Shamsuddin, A. H. & Abdullah, T. A. R. T. (2019). Modelling and optimization of biomass supply chain for bioenergy production. Journal of Environmental Treatment Techniques, 7(4), 689-695.

Woo, H., Acuna, M., Moroni, M., Taskhiri, M. S. & Turner, P. (2018). Optimizing the location of biomass energy facilities by integrating Multi-Criteria Analysis (MCA) and Geographical Information Systems (GIS). Forests, 9, 585.

Sahoo, K., Mani, S., Das, L. & Bettinger, P. (2018). GIS-based assessment of sustainable crop residues for optimal siting of biogas plants. Biomass and Bioenerg., 110, 63-74.

Laasasenaho, K., Lensu, A., Lauhanen, R. & Rintala, J. (2019). GIS-data related route optimization, hierarchical clustering, location optimization, and kernel density methods are useful for promoting distributed bioenergy plant planning in rural areas. Sustainable Energy Technologies and Assessments. 32, 47-57.

Rivera-Cadavid, L., Manyoma-Velásquez, P. C. & Manotas-Duque, D. F. (2019). Supply chain optimization for energy cogeneration using sugarcane crop residues (SCR). Sustainability-Basel., 11(23), 6565.

Wang, R., Chang, S., Cui, X., Li, J., Ma, L., Kumar, A., Nie, Y. & Cai, W. (2021). Retrofitting coal-fired power plants with biomass co-firing and carbon capture and storage for net zero carbon emission: A plant-by-plant assessment framework. GCB Bioenergy, 13(1), 143-160.

Tiammee, S. & Likasiri, C. (2020). Sustainability in corn production management: A multi-objective approach. J Clean Prod, 257, 120855.

She, J., Chung, W. & Han, H. (2019). Economic and environmental optimization of the forest supply chain for timber and bioenergy production from beetle-killed forests in Northern Colorado. Forests, 10(8), 689.

How, B. S. and Lam, H. L. (2017). Integrated biomass supply chain in Malaysia: A sustainable strategy. Chem Engineer Trans, 61, 1573-1578.

Torjai, L. & Kruzslicz, F. (2016). Mixed integer programming formulations for the Biomass Truck Scheduling problem. Cent Europ J Oper Re., 24(3), 731-745.

Soares, R., Marques, A., Amorim, P. & Rasinmäki, J. (2019). Multiple vehicle synchronisation in a full truck-load pickup and delivery problem: A case-study in the biomass supply chain. Eur J of Oper Res., 277(1), 174-194.

Pinho, T. M., Coelho, J. P., Veiga, G., Moreira, A. P. & Boaventura-Cunha, J. (2017). A multilayer model predictive control methodology applied to a biomass supply chain operational level. Complexity, 2017, 5402896.

Fokkema, J. E., Land, M. J., Coelho, L. C., Wortmann, H. & Huitema, G. B. (2020). A continuous-time supply-driven inventory-constrained routing problem. Omega (United Kingdom), 92, 102151.

Malladi, K. T., Quirion-Blais, O. & Sowlati, T. (2018). Development of a decision support tool for optimizing the short-term logistics of forest-based biomass. Appl Energ., 216, 662-677.

Cárdenas-Barrón, L. E. & Melo, R. A. (2021). A fast and effective MIP-based heuristic for a selective and periodic inventory routing problem in reverse logistics. Omega (United Kingdom), 103, 102394.

Vahdanjoo, M., Norremark, M. & Sorensen, C. G. (2021). A system for optimizing the process of straw bale retrieval. Sustainability-Basel, 13(14), 7722.

Zamar, D. S., Gopaluni, B. & Sokhansanj, S. (2017). Optimization of sawmill residues collection for bioenergy production. Appl Energ., 202, 487-495.

Cao, J. X., Wang, X. & Gao, J. (2021). A two-echelon location-routing problem for biomass logistics systems. Biosyst Eng., 202, 106-118.

Li, S., Wang, Z., Wang, X., Zhang, D. & Liu, Y. (2019). Integrated optimization model of a biomass feedstock delivery problem with carbon emissions constraints and split loads. Comput Ind Eng., 137, 106013.

Habibi, F., Asadi, E. & Sadjadi, S. J. (2018). A location-inventory-routing optimization model for cost effective design of microalgae biofuel distribution system: A case study in Iran. Energy Strateg Rev., 22, 82-93.

Asadi, E., Habibi, F., Nickel, S. & Sahebi, H. (2018). A bi-objective stochastic location-inventory-routing model for microalgae-based biofuel supply chain. Appl Energ., 228, 2235-2261.

Morales Chavez, M. M., Costa, Y. & Sarache, W. (2021). A three-objective stochastic location-inventory-routing model for agricultural waste-based biofuel supply chain. Comput Ind Eng., 162, 107759.

Delfani, F., Kazemi, A., Seyedhosseini, S. M. & Niaki, S. T. A. (2020). A green hazardous waste location-routing problem considering the risks associated with transportation and population. International Journal of Engineering, Transactions B: Applications, 33(11), 2272-2284.

Karaoglan, I., and Altiparmak, F (2010). A hybrid genetic algorithm for the location-routing problem with simultaneous pickup and delivery. The 40th International Conference on Computers and Industrial Engineering: Soft Computing Techniques for Advanced Manufacturing, and Service Systems, CIE40 2010, Awaji, Japan, p. 1–6. IEEE.

Theeraviriya, C., Pitakaso, R., Sillapasa, K. & Kaewman, S. (2019). Location decision making and transportation route planning considering fuel consumption. Journal of Open Innovation: Technology, Market, and Complexity, 5(2), 27.

Theeraviriya, C., Ruamboon, K., & Praseeratasang, N. (2021). Solving the multi-level location routing problem considering the environmental impact using a hybrid metaheuristic. International Journal of Engineering Business Management, 13, 1-17.

Tirkolaee, E. B., Abbasian, P. & Weber, G.-W. (2021). Sustainable fuzzy multi-trip location-routing problem for medical waste management during the COVID-19 outbreak. SCI Total Environ., 756, 143607.

Roni, M. S., Eksioglu, S. D., Cafferty, K. G. & Jacobson, J. J. (2017). A multi-objective, hub-and-spoke model to design and manage biofuel supply chains. Ann of Oper Res., 249, 351-380.

Lam, H. L., Ng, W. P. Q., Ng, R. T. L., Ng, E. H., Aziz, M. K. A. & Ng, D. K. S. (2013). Green strategy for sustainable waste-to-energy supply chain. Energy, 57, 4-16.

Sultana, A., Kumar, A. & Harfield, D. (2010). Development of agri-pellet production cost and optimum size. Bioresource Technol., 101(14), 5609-5621.

Mani, S., Sokhansanj, S., Bi, X. & Turhollow, A. (2006). Economics of producing fuel pellets from biomass. Appl Eng Agric, 22(3), 421-426.

Lamers, P., Roni, M. S., Tumuluru, J. S., Jacobson, J. J., Cafferty, K. G., Hansen, J. K., Kenney, K., Teymouri, F. & Bals, B. (2015). Techno-economic analysis of decentralized biomass processing depots. Bioresource Technol., 194, 205-213.

Razm, S., Nickel, S., Saidi-mehrabad, M. & Sahebi, H. (2019). A global bioenergy supply network redesign through integrating transfer pricing under uncertain condition. J Clean Prod., 208, 1081-1095.

How, B. S., Tan, K. Y. & Lam, H. L. (2016). Transportation decision tool for optimisation of integrated biomass flow with vehicle capacity constraints. J Clean Prod, 136, 197-223.

Greenhouse gas protocol. (2017). Calculation tools. Emission factors from cross-sector tools. https://ghgprotocol.org/calculation-tools (accessed Dec. 31, 2022).

Official Portal of Ministry of Finance Malaysia. (2022). Retail Price of Petroleum Products from 8 December 202 to 14 December 2022. Press Release. https://www.mof.gov.my/portal/en/news/press-release/retail-price/retail-price-of-petroleum-products-from-1-december-2022-to-7-december-2022 (accessed Dec. 31, 2022).