Integration of efficient multi-objective ant-colony and a heuristic method to solve a novel multi-objective mixed load school bus routing model

摘要

In this paper, a novel mixed-load school bus routing problem (MLSBRP) is introduced. MLSBRP assumes that several students from different schools can simultaneously take a ride on the same bus. A bi-objective mixed integer linear programming (BO-MILP) formulation is proposed to model MLSBRP. The objectives are: a) minimizing the number of the buses; and b) minimizing the average riding time of the students. A hybrid multi-objective ant colony optimization (h-MOACO) algorithm, incorporating a novel routing heuristic algorithm, is developed to solve the associated BO-MILP. Performance of the proposed h-MOACO is compared with those achieved by commercial operation research software called CPLEX, and a customized NSGAII algorithm through multi-objective diversity and accuracy metrics over several small-size and large-size test problems, respectively. Sensitivity analysis are conducted on the main parameters of the MLSBRP. The computational results indicate the capability of the new proposed MLSBRP and suitability of the proposed h-MOACO algorithm. (C) 2018 Elsevier B.V. All rights reserved.