A Modified Adaptive Genetic Algorithm for Multi-product Multi-period Inventory Routing Problem

TL;DR

This paper introduces a Modified Adaptive Genetic Algorithm to efficiently solve complex multi-product, multi-period inventory routing problems in supply chains, outperforming existing methods in computational experiments.

Contribution

The study develops a novel MAGA approach tailored for multi-product, multi-period IRP, demonstrating superior performance over Cplex and existing heuristics.

Findings

MAGA outperforms Cplex and heuristics in solution quality

The approach is effective on randomly generated instances

Computational experiments validate the method's efficiency

Abstract

Recent developments in urbanization and e-commerce have pushed businesses to deploy efficient systems to decrease their supply chain cost. Vendor Managed Inventory (VMI) is one of the most widely used strategies to effectively manage supply chains with multiple parties. VMI implementation asks for solving the Inventory Routing Problem (IRP). This study considers a multi-product multi-period inventory routing problem, including a supplier, set of customers, and a fleet of heterogeneous vehicles. Due to the complex nature of the IRP, we developed a Modified Adaptive Genetic Algorithm (MAGA) to solve a variety of instances efficiently. As a benchmark, we considered the results obtained by Cplex software and an efficient heuristic from the literature. Through extensive computational experiments on a set of randomly generated instances, and using different metrics, we show that our approach distinctly outperforms the other two methods.