2021
DOI: 10.1002/int.22427
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Multiobjective fuzzy vehicle routing using Twitter data: Reimagining the delivery of essential goods

Abstract: The world faced a major disruption in the form of the coronavirus disease (COVID-19) pandemic, which caused many countries to impose severe restrictions on movement, popularly known as "lockdown." These lockdowns impacted transportation adversely, leading to massive disruptions in global and local supply chains. As the local markets were shut down, more people started turning to e-commerce logistics platforms offering doorstep deliveries of essential items (food and medicines). This resulted in an explosion in… Show more

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Cited by 15 publications
(18 citation statements)
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“…Multiple objectives: As presented in Table 7, multiple objectives were discussed in a range of articles. Most of these papers adopted a decomposition-based algorithm to transform multi-objectives into one equivalent objective, and the weight of each goal in the objective function can be derived from min-max normalization [70], ML [85], and the decision maker's experience [66,67,84,85,95]. Note that only one work introduced dynamism in the weights, which were dependent on the real-time information of customers' request [85].…”
Section: Solution Methodsmentioning
confidence: 99%
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“…Multiple objectives: As presented in Table 7, multiple objectives were discussed in a range of articles. Most of these papers adopted a decomposition-based algorithm to transform multi-objectives into one equivalent objective, and the weight of each goal in the objective function can be derived from min-max normalization [70], ML [85], and the decision maker's experience [66,67,84,85,95]. Note that only one work introduced dynamism in the weights, which were dependent on the real-time information of customers' request [85].…”
Section: Solution Methodsmentioning
confidence: 99%
“…Most of these papers adopted a decomposition-based algorithm to transform multi-objectives into one equivalent objective, and the weight of each goal in the objective function can be derived from min-max normalization [70], ML [85], and the decision maker's experience [66,67,84,85,95]. Note that only one work introduced dynamism in the weights, which were dependent on the real-time information of customers' request [85]. Other decomposition-based methods such as Chebyshev method [52], Membership function [82] and Goal Programming(GP) [77,81,105] were also utilized to construct a compromising model for solving multi-objectives problems.…”
Section: Solution Methodsmentioning
confidence: 99%
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