Abstract
Reverse logistics can be defined as a set of practices and processes for managing returns from the consumer to the manufacturer, simultaneously with direct flow management. In this context, we have chosen to study an important variant of the Vehicle Routing Problem (VRP) which is the Multi-Depot Vehicle Routing Problem with Simultaneous Delivery and Pickup and Inventory Restrictions (MD-VRPSDP-IR). This problem involves designing routes from multiple depots that simultaneously satisfy delivery and pickup requests from a set of customers, while taking into account depot stock levels. This study proposes a hybrid Genetic Algorithm which incorporates three different procedures, including a newly developed one called the K- Nearest Depot heuristic, to assign customers to depots and also the Sweep algorithm for routes construction, and the Farthest Insertion heuristic to improve solutions. Computational results show that our methods outperform the previous ones for MD-VRPSDP.
Highlights
Our current production system is based on the use and processing of raw materials into finished products
We compare the performance of Genetic Algorithms (GAs), which have the best results will be used in the tests that follow
To validate the Mixed Integer Linear Program (MILP) model for the MD-VRPSDP-IR proposed in this paper, we compare our GA results with those obtained by CPLEX, for a small instance, through an illustrative example
Summary
Our current production system is based on the use and processing of raw materials into finished products. In the distribution system of food market chains [2], or in the urban public transport systems [3] In this problem, each depot has a homogeneous vehicle fleet that must ensure the satisfaction of known delivery and pickup requests of a set of customers. To avoid any confusion between certain variants of the VRP, we would like to clarify that the problem treated in this work is an extension of the VRPB (Vehicle Routing Problem with Backhauls), where the origin and the destination of all products delivered and picked up from customers are the depot. The presence of combined delivery and pickup demands in our problem, and restrictions on depot capacities mean that additional tests are required to preserve feasibility.
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