solving the capacitated hybrid vehicle routing problem using a random general variable neighborhood search: computational experiments

Green logistics is developed due to the rise of the negative effect of logistics on the environment; and notably the transportation sector. to find advantageous solutions to both business and the environment, some governments require incorporating new environmental limits and objectives studying the vehicle routing problems. in this context, a particular np-hard problem in this paper is studied called the capacitated hybrid vehicle routing problem. we proposed an integer linear program for the capacitated case and we developed a random general variable neighbourhood search algorithm, which can handle the problem with a significant number of customers. we solved small instances with mutually the standard solver cplex and with our proposed algorithm. outcomes show that our approach provided the same results for 55 % of the cases and superior solutions for 40 % of the instances compared to the results provided by cplex. in parallel, we modify a large set of benchmark instances proposed in previous works by introducing the product demand of each customer and we solve it by the random general variable neighbourhood search algorithm. results of numerical testing show that our method delivers the best-known solutions in computation times of just 3 seconds on average.