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Related Experiment Video

Updated: Aug 26, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Uncertain multilevel programming with application to omni-channel vehicle routing problem.

Rong Gao¹, Yebao Ma¹, Dan A Ralescu²

¹School of Economics and Management, Hebei University of Technology, Tianjin, 300401 China.

Journal of Ambient Intelligence and Humanized Computing

|October 3, 2022

Summary

This study introduces new models for multilevel programming with uncertainty, optimizing decentralized decisions. The research demonstrates improved distribution efficiency in vehicle routing by balancing decision-maker interests.

Keywords:

Multilevel programming Omni-channel vehicle routing Uncertain variable

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Last Updated: Aug 26, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Area of Science:

Operations Research
Decision Science
Optimization

Background:

Multilevel programming addresses decentralized decision-making.
Real-world problems often involve uncertainties and volatile factors.
Existing models may not adequately handle uncertainty in multilevel programming.

Purpose of the Study:

To develop models for multilevel programming with uncertain parameters.
To address indeterminacies in decentralized decision-making problems.
To optimize distribution efficiency in complex scenarios.

Main Methods:

Construction of uncertain expected value multilevel programming model.
Development of chance-constrained multilevel programming.
Conversion of models to equivalent forms.
Application of genetic algorithms for Stackelberg-Nash equilibrium solutions.

Main Results:

The proposed models effectively handle uncertainty in multilevel programming.
Equivalent forms of the models were successfully derived.
Stackelberg-Nash equilibrium solutions were obtained using a genetic algorithm.
The models were applied to the omni-channel vehicle routing problem.

Conclusions:

The established models optimize distribution efficiency.
Coordinating decision-maker interests is key to optimization.
The approach provides a robust solution for uncertain decentralized decision-making.