A Lagrangian Decomposition Algorithm for Robust Green Transportation Location Problem

TL;DR

This paper presents a Lagrangian decomposition algorithm for a robust green transportation location problem with uncertain demand, balancing costs and environmental impact, and demonstrating computational efficiency over commercial solvers.

Contribution

It introduces a novel Lagrangian decomposition approach to solve a robust green transportation problem efficiently, providing tight bounds and reducing computational time.

Findings

The proposed algorithm achieves a close lower bound faster than commercial solvers.

It effectively balances transportation costs and air pollution in uncertain demand scenarios.

The approach enhances computational efficiency for complex green transportation models.

Abstract

In this paper, a green transportation location problem is considered with uncertain demand parameter. Increasing robustness influences the number of trucks for sending goods and products, and consequently, makes the air pollution enhance. In this paper, two green approaches are introduced which demand is the main uncertain parameter in both. These approaches are addressed to provide a trade-off between using available trucks and buying new hybrid trucks for evaluating total costs besides air pollution. Due to growing complexity, a Lagrangian decomposition algorithm is applied to find a tight lower bound for each approach. In this propounded algorithm, the main model is decomposed into master and subproblems to speed up convergence with a tight gap. Finally, the suggested algorithm is compared with commercial solver regarding total cost and computational time. Due to computational results for the proposed approach, the Lagrangian decomposition algorithm is provided a close lower bound in less time against commercial solver.