摘要：Last-mile operations in forward and reverse logistics are responsible for a large part of the costs, emissions, and times in supply chains. These operations have increased due to the growth of electronic commerce and direct-to-consumer strategies. We propose a novel data- and model-driven framework to support decision making for urban distribution. The methodology is composed of diverse, hybrid, and complementary techniques integrated by a decision support system. This approach focuses on key elements of megacities such as socio-demographic diversity, portfolio mix, logistics fragmentation, high congestion factors, and dense commercial areas. The methodological framework will allow decision makers to create early warning systems and, with the implementation of optimization, machine learning, and simulation models together, make the best utilization of resources. The advantages of the system include flexibility in decision making, social welfare, increased productivity, and reductions in cost and environmental impacts. A real-world illustrative example is presented under conditions in one of the most congested cities: the megacity of Bogota, Colombia. Data come from a retail organization operating in the city. A network of stakeholders is analyzed to understand the complex urban distribution. The execution of the methodology was capable of solving a complex problem reducing the number of vehicles utilized, increasing the resource capacity utilization, and reducing the cost of operations of the fleet, meeting all constraints. These constraints included the window of operations and accomplishing the total number of deliveries. Furthermore, the methodology could accomplish the learning function using deep reinforcement learning in reasonable computational times. This preliminary analysis shows the potential benefits, especially in understudied metropolitan areas from emerging markets, supporting a more effective delivery process, and encouraging proactive, dynamic decision making during the execution stage.
