Akademik Veri Yönetim Sistemi
ULUSLARARASI İKSAD İKTİSAT KONGRESİ, Ankara, Türkiye, 27 Şubat 2022, ss.52-53
Biomass energy is the conversion of vegetable
oil wastes, agricultural harvest and forest products and animal wastes into
energy after a series of processes. Considering the lifetime of fossil-based
energies, energy production via using environmentally friendly biomass for the
recycling of wastes may be the indispensable energy type of the future. Among
the renewable energy sources, biomass energy, which is the only renewable
energy source that has social and economic effects together; It has the
potential to affect agriculture, industry, transportation and many other areas.
Unlike other types of renewable energy, it has the ability to be stored and
converted into energy at any time. Although biomass energy has important
advantages, the collection, storage and processing processes of wastes should
be analyzed well. Biomass raw material should be collected by transporters from
different villages and settlements and brought to the main center. Energy
production using biomass has an important place in terms of waste evaluation
and sustainable economy. The most important issue in energy production using
biomass is the minimization of collection costs. Optimum collection routes
should be determined to minimize collection costs. The effect of vehicle
routing processes, which also affect energy production and costs, on the
production of biomass energy from renewable energies is inevitable. While the
vehicle routing problem can be solved by linear programming under deterministic
conditions, the problem becomes NP-hard due to the increase in the number of
vehicles, the addition of fuel and capacity constraints, and the increase in
supply points. Since the problem is NP-hard, it cannot be solved by linear
programming. In this study, the vehicle routing problem for biomass collection
is modeled considering the capacity of
the carriers, distances, road conditions, distances from the main center to the
units, and energy production and cost of the storage capacity. The proposed
model is solved using particle swarm optimization