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A vehicle routing problem in timber logistics incorporating a comprehensive carbon emission reduction strategy is proposed. Carbon emission reduction based on an optimization model is established to minimize the total transportation cost while reducing carbon emissions and empty-loading mileage. To solve the problem efficiently, a hybrid algorithm that combines a greedy algorithm with a genetic algorithm featuring adaptive and elimination mechanisms is developed. The hybrid algorithm is featured with adaptive crossover and mutation probabilities and eliminates unsuitable individuals with elimination mechanisms, which improves the searching efficiency and quality of the optimal solution. Numerical experiments are conducted to verify the feasibility of the proposed methods. The results demonstrate that the hybrid algorithm reduces the total mileage travelled by 17.26% and the carbon emissions during empty-loading by about 38.71%. Based on the optimization results, it is concluded that reasonable route planning can provide a solid support to improve the economics, timeliness, and environmental sustainability of the timber logistics, which is conducive to realizing a sustainable forestry supply chain.
Details
Algorithms;
Emissions;
Transportation industry;
Timber;
Optimization;
Roads & highways;
Greedy algorithms;
Transportation planning;
Carbon;
Energy consumption;
Emissions control;
Efficiency;
Optimization models;
Vehicles;
Adaptive algorithms;
Sustainable forestry;
Genetic algorithms;
Greenhouse gases;
Sustainable development;
Route planning;
Carbon dioxide;
Search algorithms;
Supply chains;
Linear programming;
Logistics;
Environmental protection;
Vehicle routing;
Cost control