Abstract
We present and solve a rich vehicle routing problem based on a practical distribution problem faced by a third-party logistics provider, whose aim is to deliver pharmaceutical products to healthcare facilities in Tuscany. The problem is characterized by having multiple depots, a heterogeneous fleet of vehicles, flexible time windows, periodic demands, incompatibilities between vehicles and customers, a maximum duration for the routes, and a maximum number of customers per route. A multi-start iterated local search algorithm making use of several neighborhoods is proposed to solve the problem. The algorithm has been tested on a large number of instances and obtained good results, both on the real case study and on a number of artificially generated instances.
Highlights
Vehicle routing constitutes a well-known class of combinatorial optimization problems that have been the subject of countless studies since the late 1950’s
This paper introduced and formalized a multi-period vehicle routing problem with time windows inspired by a real situation arising in the field of pharmaceutical distribution
The interest for the problem is motivated by the fact that it is a real case study, but it generalizes many other vehicle routing variants from the literature
Summary
Vehicle routing constitutes a well-known class of combinatorial optimization problems that have been the subject of countless studies since the late 1950’s This is justified by their high potential of applications in many real-life situations, especially in the distribution of goods and in supply chain management, see, e.g., Golden et al [1], Schmid et al [2], and Toth and Vigo [3]. To the best of our knowledge the proposed RVRP has never been addressed in the literature The combination of these constraints makes the problem very interesting to study. To speed up the local search and improve convergence towards good solutions, the ILS makes use of auxiliary data structures and accepts infeasible solutions at the expense of additional penalty costs It uses a multi-start method to further diversify the search.
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