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Metaheuristic Optimization of the Agricultural Biomass Supply Chain: Integrating Strategic, Tactical, and Operational Planning

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  • Seyed Mojib Zahraee

    (Department of Manufacturing, Materials and Mechatronics, School of Engineering, RMIT University, Melbourne, VIC 3053, Australia)

  • Nirajan Shiwakoti

    (Department of Manufacturing, Materials and Mechatronics, School of Engineering, RMIT University, Melbourne, VIC 3053, Australia)

  • Peter Stasinopoulos

    (Department of Manufacturing, Materials and Mechatronics, School of Engineering, RMIT University, Melbourne, VIC 3053, Australia)

Abstract

Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and $7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions.

Suggested Citation

  • Seyed Mojib Zahraee & Nirajan Shiwakoti & Peter Stasinopoulos, 2024. "Metaheuristic Optimization of the Agricultural Biomass Supply Chain: Integrating Strategic, Tactical, and Operational Planning," Energies, MDPI, vol. 17(16), pages 1-35, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4040-:d:1456459
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    References listed on IDEAS

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    1. Cambero, Claudia & Sowlati, Taraneh, 2016. "Incorporating social benefits in multi-objective optimization of forest-based bioenergy and biofuel supply chains," Applied Energy, Elsevier, vol. 178(C), pages 721-735.
    2. Gital Durmaz, Yeşim & Bilgen, Bilge, 2020. "Multi-objective optimization of sustainable biomass supply chain network design," Applied Energy, Elsevier, vol. 272(C).
    3. Zahraee, Seyed Mojib & Shiwakoti, Nirajan & Stasinopoulos, Peter, 2022. "Application of geographical information system and agent-based modeling to estimate particle-gaseous pollutantemissions and transportation cost of woody biomass supply chain," Applied Energy, Elsevier, vol. 309(C).
    4. Liu, D.S. & Tan, K.C. & Huang, S.Y. & Goh, C.K. & Ho, W.K., 2008. "On solving multiobjective bin packing problems using evolutionary particle swarm optimization," European Journal of Operational Research, Elsevier, vol. 190(2), pages 357-382, October.
    5. 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.
    6. 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.
    7. Zahraee, Seyed Mojib & Rahimpour Golroudbary, Saeed & Shiwakoti, Nirajan & Stasinopoulos, Peter, 2021. "Particle-Gaseous pollutant emissions and cost of global biomass supply chain via maritime transportation: Full-scale synergy model," Applied Energy, Elsevier, vol. 303(C).
    8. Mohammad Fattahi & Kannan Govindan & Mehdi Farhadkhani, 2021. "Sustainable supply chain planning for biomass-based power generation with environmental risk and supply uncertainty considerations: a real-life case study," International Journal of Production Research, Taylor & Francis Journals, vol. 59(10), pages 3084-3108, May.
    9. Sarker, Bhaba R. & Wu, Bingqing & Paudel, Krishna P., 2019. "Modeling and optimization of a supply chain of renewable biomass and biogas: Processing plant location," Applied Energy, Elsevier, vol. 239(C), pages 343-355.
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