Bi-objective Hybrid Algorithm for Half-open Refined Oil Secondary Distribution Problem with Workload Balancing

The refined oil secondary distribution problem under the half-open distribution is investigated, in which trucks can choose any depot for replenishment or termination during travel. In actual distribution, the workload balancing for truck drivers should also be considered. A measure of workload balancing is defined, i.e., the minimization of the extreme difference between the longest and the shortest two-trip working time sum. Meanwhile, the minimization of the distribution cost is considered, and a bi-objective optimization model is developed. Considering the complexity of the problem, a bi-objective hybrid algorithm (BOHA) is proposed to solve the large-scale problem. In BOHA, the improved C-W saving algorithm is applied to obtain the initial population, and the neighborhood search operators and the population improvement strategy are designed. Numerical experiments are conducted through the actual data of a refined oil company in China. The computational results show that the BOHA can obtain a better Pareto optimal solution set in a shorter time.