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Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time...

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Main Authors: Liu, Lihua, He, Aneng, Tian, Tian, Lee, Lai Soon, Seow, Hsin-Vonn
Format: Article
Language:English
Published: MDPI 2024
Online Access:http://psasir.upm.edu.my/id/eprint/111989/1/mathematics-12-02367-v2.pdf
http://psasir.upm.edu.my/id/eprint/111989/
https://www.mdpi.com/2227-7390/12/15/2367
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spelling my.upm.eprints.1119892024-09-10T06:10:11Z http://psasir.upm.edu.my/id/eprint/111989/ Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction Liu, Lihua He, Aneng Tian, Tian Lee, Lai Soon Seow, Hsin-Vonn In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction. MDPI 2024-07-29 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/111989/1/mathematics-12-02367-v2.pdf Liu, Lihua and He, Aneng and Tian, Tian and Lee, Lai Soon and Seow, Hsin-Vonn (2024) Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction. Mathematics, 12 (15). art. no. 2367. pp. 1-35. ISSN 2227-7390 https://www.mdpi.com/2227-7390/12/15/2367 10.3390/math12152367
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction.
format Article
author Liu, Lihua
He, Aneng
Tian, Tian
Lee, Lai Soon
Seow, Hsin-Vonn
spellingShingle Liu, Lihua
He, Aneng
Tian, Tian
Lee, Lai Soon
Seow, Hsin-Vonn
Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
author_facet Liu, Lihua
He, Aneng
Tian, Tian
Lee, Lai Soon
Seow, Hsin-Vonn
author_sort Liu, Lihua
title Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
title_short Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
title_full Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
title_fullStr Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
title_full_unstemmed Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
title_sort bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
publisher MDPI
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/111989/1/mathematics-12-02367-v2.pdf
http://psasir.upm.edu.my/id/eprint/111989/
https://www.mdpi.com/2227-7390/12/15/2367
_version_ 1811686074780483584
score 13.211869

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