Joint optimal multi-vehicle and multi-bioenergy manufacturing scheduling for a supply chain network optimized for efficient carbon utilization
摘要
Increasing energy demand and rising levels of greenhouse gas emissions have increased the necessity for bioenergy and sustainable energy production. The sustainable bioenergy supply chain is the key to producing sustainable biofuel. This proposed model focuses on providing a minimization research article for arranging a flexible and reliable residual biomass-to-biofuel supply chain network where residual biomass from a few agricultural regions, multi-biorefineries, multi-transportation modes, several marketplaces, and several biogas plants are examined for two bioenergies, specifically biogas and biofuel. The goal of the study is to reveal the convenience of bioenergy to build a supply chain network of biofuels and location-allocation for agricultural zones and plants to fulfill the market’s demand as well as impacts on a multi-modal transportation network efficiency. The carbon emission cost is incorporated in each step of this proposed study. In the numerical example, six agricultural zones, four biogas plants, four biorefineries, and six marketplaces are observed. A LinGO, optimization modeling software, is used to solve the numerical problem of the study. It is shown by the numerical examples that the demands of specific plants and markets are fulfilled from that agricultural zone and plant respectively first, which has the nearest transportation shortest shipment distance within them. The bioenergy production cost is stated at 53.07%, which is a maximum portion of the total cost in biogas plants and biorefineries. In the entire supply chain, the transportation sector is the foremost source of carbon emissions in an environment with 75.13% of the total carbon emissions. Even though the transportation sector contributes 31.95% of the full cost. In the study, the overall supply chain cost is reduced by utilizing the multi-setup-multi-delivery arrangement. After explaining numerical examples and a case study, one can conclude that the case study cost is 6.70% less than the original cost of this study. This clean production system enhances a low carbon fuel and concerns climate variation influence from transportation. Furthermore, the wide progress of this study contributes to addressing environmental challenges through the application of bioenergy and the minimizing of carbon emissions. The results provide a systematic guideline for developing a sustainable biofuel supply chain by minimizing cost, various aspects of transportation logistics, and multi-model alternatives under reduced energy effects.