Adaptive variable neighborhood search solution methods for the fleet size and mix pollution location-inventory-routing problem

Abstract

This work introduces the Fleet-size and Mix Pollution Location-Inventory-Routing Problem with Just-in-Time replenishment policy and Capacity Planning. This problem extends the strategic-level decisions of classic LIRP by considering capacity selection decisions and heterogeneous fleet composition. An MIP formulation of this new complex combinatorial optimization problem is proposed and small-sized problem instances are solved using the CPLEX solver. For the solution of more realistic-sized problem instances, a General Variable Neighborhood Search (GVNS)-based framework is adopted. Novel adaptive shaking methods are proposed as intelligent components of the developed GVNS algorithms to further improve their performance. To evaluate the proposed GVNS schemes, several problem instances are randomly generated by following specific instructions from the literature and adopting real vehicles' parameters. Comparisons between these solutions and the corresponding ones achieved by CPLEX are made. The computational results indicate the efficiency of the proposed GVNS-based algorithms, with the best GVNS scheme to produce 7% better solutions than CPLEX for small problems. Finally, the economic and environmental impacts of using either homogeneous or heterogeneous fleet of vehicles are examined.