A tri-objective analytics model for green transportation planning under uncertainty considering capacity constraints and driving behavior
Year: 2026
Type: article
Abstract: Transportation planning poses a significant challenge in modern logistics, where factors such as volume, weight, and driving patterns critically affect system performance. While volume and weight constraints affect vehicle utilization and distribution strategies, variations in driving patterns considerably impact fuel consumption and carbon emissions. Collectively, these aspects define the 4D-TP, which integrates both economic efficiency and environmental sustainability. This study proposes a tri-objective, minimization-oriented green 4D-TP model that accounts for volume and weight capacities and driving patterns under uncertainty, with problem parameters represented by an uncertain matrix. The model simultaneously minimizes transportation time, overall cost, and carbon emissions and facilitates a comparative assessment of carbon emissions under protective (defensive) and aggressive driving behaviors. To address uncertainty, two alternative formulations, such as the expected value model and the chance-constrained model, are developed and subsequently converted into crisp equivalents using the properties of uncertainty theory. These models are further reduced to single-objective formulations through the minimizing distance function method, goal programming, and convex combination approaches. A real-world numerical case study is presented and solved using the LINGO 18.0 optimization toolbox to demonstrate the model’s applicability. The results indicate that adopting the right driving pattern yields superior outcomes in terms of both environmental stainability and economic efficiency. For a good driving pattern, the minimum transportation cost (3262.50 units) is obtained by the convex combination method, the minimum transportation time (1226.78 units) by the minimizing distance function method, and the minimum carbon emission (1625.63 units) by the goal programming method. These results highlight the effectiveness of different methods in optimizing specific transportation objectives. Finally, sensitivity analysis, model validation, managerial implications, comparative evaluation, and potential directions for future research are discussed.
Authors: Palash Sahoo, Tapan Senapati, Sarbast Moslem
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,790
OpenAlex ID: W7214741985