Formulation and Solution of the Stochastic Truck and Trailer Routing Problem

In manufacturing and service industries, transportation often faces uncertain conditions. While current research on the truck and trailer routing problem (TTRP) mostly uses deterministic methods, they fall short in addressing uncertainties in travel and service times. This study aims to improve TTRP models by incorporating randomness in travel and service durations and specific time windows, better mirroring real-world scenarios. The enhanced model uses the multipoint simulated annealing (M-SA) method for practical application. The study involves 144 benchmark instances across six levels, starting with generating feasible solutions, then refining them using M-SA. A stochastic programming model with recourse (SPR) was used for problem formulation. Sensitivity analysis assessed the impact of various parameters and compared solutions obtained from M-SA and the analysis, showing minimal differences and thus the effectiveness of the proposed algorithm in solving the stochastic TTRP. The paper concludes with suggestions for future research.

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Published
2024-10-17
DOI
https://doi.org/10.3390/engproc2024076017
Primary Topic
Vehicle Routing Optimization Methods
Type
article
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article

Formulation and Solution of the Stochastic Truck and Trailer Routing Problem

Golam Kabir, Seyedmehdi Mirmohammadsadeghi
Vehicle Routing Optimization Methods
article

Formulation and Solution of the Stochastic Truck and Trailer Routing Problem

Golam Kabir, Seyedmehdi Mirmohammadsadeghi
article en

Abstract

In manufacturing and service industries, transportation often faces uncertain conditions. While current research on the truck and trailer routing problem (TTRP) mostly uses deterministic methods, they fall short in addressing uncertainties in travel and service times. This study aims to improve TTRP models by incorporating randomness in travel and service durations and specific time windows, better mirroring real-world scenarios. The enhanced model uses the multipoint simulated annealing (M-SA) method for practical application. The study involves 144 benchmark instances across six levels, starting with generating feasible solutions, then refining them using M-SA. A stochastic programming model with recourse (SPR) was used for problem formulation. Sensitivity analysis assessed the impact of various parameters and compared solutions obtained from M-SA and the analysis, showing minimal differences and thus the effectiveness of the proposed algorithm in solving the stochastic TTRP. The paper concludes with suggestions for future research.

University of Regina (CA)
Openalex Percentile: Top 74%
Vehicle Routing Optimization Methods
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