Heterogeneous truck fleet optimization

The efficient management of a diverse truck fleet presents a significant challenge within the realm of logistics and transportation management. The primary objective revolves around optimizing fleet efficiency by assigning the most suitable truck type to each task, all while accommodating an array o...

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Bibliographic Details
Published inSadhana (Bangalore) Vol. 49; no. 3
Main Authors Mishra, Dev, Mitra, Rony
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 14.08.2024
Springer Nature B.V
Subjects
Constraints
Efficiency
Engineering
Genetic algorithms
Linear programming
Logistics
Mixed integer
Nonlinear programming
Optimization
Software
System effectiveness
Transportation management
Transportation systems
truck fleet
Mixed-integer non linear programming
optimization
transportation
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Summary:The efficient management of a diverse truck fleet presents a significant challenge within the realm of logistics and transportation management. The primary objective revolves around optimizing fleet efficiency by assigning the most suitable truck type to each task, all while accommodating an array of constraints such as delivery time windows, capacity limits. In this study, we introduce a Mixed Integer Non Linear Programming (MINLP) model tailored for the optimization of heterogeneous truck fleets, facilitating the precise allocation of trucks to their respective roles. Employing the CPLEX as our decision-making tool, we conduct computational experiments that underscore the effectiveness of our MINLP model and CPLEX solver in enhancing the operational efficiency of heterogeneous truck fleets while adhering rigorously to various constraints and compare the solution with Genetic Algorithm. The findings convincingly demonstrate the potential of our proposed methodology to furnish logistics and transportation managers with invaluable insights, ultimately contributing to the sustainability and efficiency of modern logistics and transportation systems.
ISSN:0973-7677
0256-2499
0973-7677
DOI:10.1007/s12046-024-02578-w