Data-Driven Methodology to Support Long-Lasting Logistics and Decision Making for Urban Last-Mile Operations

Edgar Gutierrez-Franco, Christopher Mejia-Argueta and Luis Rabelo
Additional contact information
Edgar Gutierrez-Franco: Center for Latin-American Logistics Innovation, Massachusetts Institute of Technology, Global SCALE Network, Cambridge, MA 02139, USA
Christopher Mejia-Argueta: Food and Retail Operations Lab, Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Luis Rabelo: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 162993, USA

Sustainability, 2021, vol. 13, issue 11, 1-33

Abstract: 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.

Keywords: urban logistics; emerging markets; nanostores; customer-centric supply chains; hybrid methods; prescriptive analytics; framework; digital twin (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/11/6230/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/11/6230/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:11:p:6230-:d:566875

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().