A simulation driven optimization algorithm for scheduling sorting center operations

TL;DR

This paper presents a simulation-driven optimization algorithm for scheduling sorting center operations, aiming to maximize throughput by dynamically adjusting mappings based on real-time demand and workforce forecasts.

Contribution

It introduces a novel optimization approach that integrates real-time forecasts with simulation to enhance sorting center efficiency and robustness.

Findings

Improved throughput in simulated sorting operations.

Enhanced robustness against environmental stochasticity.

Effective real-time adjustment of sorting mappings.

Abstract

Parcel sorting operations in logistics enterprises aim to achieve a high throughput of parcels through sorting centers. These sorting centers are composed of large circular conveyor belts on which incoming parcels are placed, with multiple arms known as chutes for sorting the parcels by destination, followed by packing into roller cages and loading onto outbound trucks. Modern sorting systems need to complement their hardware innovations with sophisticated algorithms and software to map destinations and workforce to specific chutes. While state of the art systems operate with fixed mappings, we propose an optimization approach that runs before every shift, and uses real-time forecast of destination demand and labor availability in order to maximize throughput. We use simulation to improve the performance and robustness of the optimization solution to stochasticity in the environment, through closed-loop tuning of the optimization parameters.