A rolling horizon heuristic approach for a multi-stage stochastic waste collection problem

TL;DR

This paper introduces a multi-stage stochastic optimization model with a rolling horizon heuristic for waste collection, incorporating stochastic waste generation and providing insights through extensive experiments on real data.

Contribution

It presents a novel stochastic optimization framework with a rolling horizon approach for multi-stage waste collection, addressing uncertainty in waste accumulation.

Findings

The approach improves decision-making under waste generation uncertainty.

Stochastic modeling significantly impacts routing efficiency.

The method performs well on real-world data from Portugal.

Abstract

In this paper we present a multi-stage stochastic optimization model to solve an inventory routing problem for recyclable waste collection. The objective is the maximization of the total expected profit of the waste collection company. The decisions are related to the selection of the bins to be visited and the corresponding routing plan in a predefined time horizon. Stochasticity in waste accumulation is modeled through scenario trees generated via conditional density estimation and dynamic stochastic approximation techniques. The proposed formulation is solved through a rolling horizon approach, providing a worst-case analysis on its performance. Extensive computational experiments are carried out on small- and large-sized instances based on real data provided by a large Portuguese waste collection company. The impact of stochasticity on waste generation is examined through stochastic measures, and the performance of the rolling horizon approach is evaluated. Some managerial insights on different configurations of the instances are finally discussed.