Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters

Logistics and transport are the core of many industrial and business processes. One of the most promising segments in the field is the optimization of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environments and covers a fraction of real industrial applications due to complex combinatorial algorithms required to be promptly executed. In this paper, a real-world case study in all its complexity is observed and formulated as a real-world vehicle routing problem (VRP). To be able to cope with the complexity computationally, we propose a new procedure based on an adaptive memory metaheuristic combined with local search. The initial solution is obtained with the Clarke-Wright savings algorithm extended here by introducing a dropout factor to include a demanded stochastic attribute. The procedure and corresponding algorithms are tested first on the two existing scientific state-of-art benchmarks and further on the real industrial case study, which considers capacities, time windows, soft time windows, heterogeneous vehicles, dynamic fuel consumption, multi-trip delivery, crew skills, split delivery and, finally, time-dependent routes as the most significant factor. In comparison with the current state-of-the-art algorithms for vehicle routing problem with a large number of constraints, we obtain an average savings of 2.03% in delivery time and 20.98% in total delivery costs.