A Two-step Heuristic for the Periodic Demand Estimation Problem

TL;DR

This paper introduces a new two-step heuristic for the Periodic Demand Estimation problem, improving solution quality and scalability by relaxing assumptions and employing clustering and optimization techniques, validated on real railway data.

Contribution

It extends previous PDE formulations by relaxing assumptions and proposes a scalable two-step heuristic combining clustering and optimization methods.

Findings

The heuristic produces high-quality solutions on real data.

It outperforms existing methods in solution quality.

The approach effectively handles large-scale instances.

Abstract

Freight carriers rely on tactical plans to satisfy demand in a cost-effective way. For computational tractability in real large-scale settings, such plans are typically computed by solving deterministic and cyclic formulations. An important input is the periodic demand, i.e., the demand that is expected to repeat in each period of the planning horizon. Motivated by the discrepancy between time series forecasts of demand in each period and the periodic demand, Laage et al. (2021) recently introduced the Periodic Demand Estimation (PDE) problem and showed that it has a high value. However, they made strong assumptions on the solution space so that the problem could be solved by enumeration. In this paper we significantly extend their work. We propose a new PDE formulation that relaxes the strong assumptions on the solution space. We solve large instances of this formulation with a two-step heuristic. The first step reduces the dimension of the feasible space by performing clustering of commodities based on instance-specific information about demand and supply interactions. The formulation along with the first step allow to solve the problem in a second step by either metaheuristics or the state-of-the-art black-box optimization solver NOMAD. In an extensive empirical study using real data from the Canadian National Railway Company, we show that our methodology produces high quality solutions and outperforms existing ones.