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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Main Authors: Andrés Muñoz-Villamizar, Elyn L. Solano-Charris, Lorena Reyes-Rubiano, Javier Faulin
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Logistics
Subjects:
Online Access:https://www.mdpi.com/2305-6290/5/1/17
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spelling 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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