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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Vydáno v:Mathematics (Basel) Ročník 12; číslo 15; s. 2367
Hlavní autoři: Liu, Lihua, He, Aneng, Tian, Tian, Lee, Lai Soon, Seow, Hsin-Vonn
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2024
Témata:
Algorithms
Brand loyalty
Carbon
carbon trading scheme
Case studies
Cost analysis
Costs
Customer satisfaction
Emissions trading
Energy consumption
Entropy (statistics)
entropy–TOPSIS
Genetic algorithms
Greenhouse gases
Integer programming
Inventory
Inventory control
location inventory routing
Logistics management
Maximization
Mixed integer
Nonlinear programming
Normal distribution
NSGA-II
Operating costs
Optimization
Pareto optimum
Random variables
Sensitivity analysis
Sorting algorithms
Supply chains
Weighting methods
Windows (intervals)
China
Malaysia
ISSN:2227-7390, 2227-7390
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Shrnutí: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12152367