Measuring Disruptions in Last-Mile Delivery Operations
The rapid growth of urbanisation and e-commerce has increased the number of home deliveries that need to be made in retail operations. Consequently, there is also an increase in unexpected incidents, such as adverse traffic, unavailability of parking space, and vehicle breakdowns. These disruptions...
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doaj-6a7b7389a99d4888b39bd5f0648ce57d2021-03-16T00:02:53ZengMDPI AGLogistics2305-62902021-03-015171710.3390/logistics5010017Measuring Disruptions in Last-Mile Delivery OperationsAndrés Muñoz-Villamizar0Elyn L. Solano-Charris1Lorena Reyes-Rubiano2Javier Faulin3International School of Economic and Administrative Sciences, Universidad de La Sabana, 140013 Chía, ColombiaInternational School of Economic and Administrative Sciences, Universidad de La Sabana, 140013 Chía, ColombiaInternational School of Economic and Administrative Sciences, Universidad de La Sabana, 140013 Chía, ColombiaDepartment of Statistics and Operations Research, Institute of Smart Cities, Public University of Navarra, 31006 Pamplona, SpainThe rapid growth of urbanisation and e-commerce has increased the number of home deliveries that need to be made in retail operations. Consequently, there is also an increase in unexpected incidents, such as adverse traffic, unavailability of parking space, and vehicle breakdowns. These disruptions result in delays, higher costs, and lower service levels in the last-mile delivery operation. Motivated by free, innovative, and efficient tools, such as the Google application programming interface (API) and Google OR, we built a model to measure the impact of disruptions in the last-mile delivery operation. Our model considers customers’ geographic information, speed estimation between nodes, routing optimisation, and disruption evaluation. Disruptions are considered here as external factors such as accidents and road works that imply the closure of or slow access to certain roads. Computational experiments, based on a set of real data from three different cities around the world, which contrast in size and characteristics (i.e., Boston, US; Bogotá, Colombia; and Pamplona, Spain), were conducted to validate our approach. The tests consider 50 different instances of up to 100 customers per city and analyse the impact of disruptions in terms of travelled time and distance. Our results provide managerial insights for key stakeholders (i.e., carriers, consumers, and government) to define policies and development plans that improve the resilience and capabilities of cities’ transportation systems.https://www.mdpi.com/2305-6290/5/1/17last-mile deliverydisruptionsGoogle APIGoogle OR |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andrés Muñoz-Villamizar Elyn L. Solano-Charris Lorena Reyes-Rubiano Javier Faulin |
spellingShingle |
Andrés Muñoz-Villamizar Elyn L. Solano-Charris Lorena Reyes-Rubiano Javier Faulin Measuring Disruptions in Last-Mile Delivery Operations Logistics last-mile delivery disruptions Google API Google OR |
author_facet |
Andrés Muñoz-Villamizar Elyn L. Solano-Charris Lorena Reyes-Rubiano Javier Faulin |
author_sort |
Andrés Muñoz-Villamizar |
title |
Measuring Disruptions in Last-Mile Delivery Operations |
title_short |
Measuring Disruptions in Last-Mile Delivery Operations |
title_full |
Measuring Disruptions in Last-Mile Delivery Operations |
title_fullStr |
Measuring Disruptions in Last-Mile Delivery Operations |
title_full_unstemmed |
Measuring Disruptions in Last-Mile Delivery Operations |
title_sort |
measuring disruptions in last-mile delivery operations |
publisher |
MDPI AG |
series |
Logistics |
issn |
2305-6290 |
publishDate |
2021-03-01 |
description |
The rapid growth of urbanisation and e-commerce has increased the number of home deliveries that need to be made in retail operations. Consequently, there is also an increase in unexpected incidents, such as adverse traffic, unavailability of parking space, and vehicle breakdowns. These disruptions result in delays, higher costs, and lower service levels in the last-mile delivery operation. Motivated by free, innovative, and efficient tools, such as the Google application programming interface (API) and Google OR, we built a model to measure the impact of disruptions in the last-mile delivery operation. Our model considers customers’ geographic information, speed estimation between nodes, routing optimisation, and disruption evaluation. Disruptions are considered here as external factors such as accidents and road works that imply the closure of or slow access to certain roads. Computational experiments, based on a set of real data from three different cities around the world, which contrast in size and characteristics (i.e., Boston, US; Bogotá, Colombia; and Pamplona, Spain), were conducted to validate our approach. The tests consider 50 different instances of up to 100 customers per city and analyse the impact of disruptions in terms of travelled time and distance. Our results provide managerial insights for key stakeholders (i.e., carriers, consumers, and government) to define policies and development plans that improve the resilience and capabilities of cities’ transportation systems. |
topic |
last-mile delivery disruptions Google API Google OR |
url |
https://www.mdpi.com/2305-6290/5/1/17 |
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