Automatic Policy Search using Population-Based Hyper-heuristics for the Integrated Procurement and Perishable Inventory Problem

TL;DR

This paper introduces a hyper-heuristic framework using metaheuristics like GA and PSO to optimize policies for managing perishable inventory with multiple uncertainties, outperforming traditional uniform policies.

Contribution

It develops a novel hyper-heuristic approach that automates policy selection and sourcing decisions at the item level, demonstrating significant performance improvements.

Findings

Hyper-heuristic framework outperforms traditional policies.

GA and EGA achieve the best results.

Item-level policy construction yields substantial gains.

Abstract

This paper addresses the problem of managing perishable inventory under multiple sources of uncertainty, including stochastic demand, unreliable supplier fulfillment, and probabilistic product shelf life. We develop a discrete-event simulation environment to compare two optimization strategies for this multi-item, multi-supplier problem. The first strategy optimizes uniform classic policies (e.g., Constant Order and Base Stock) by tuning their parameters globally, complemented by a direct search to select the best-fitting suppliers for the integrated problem. The second approach is a hyper-heuristic approach, driven by metaheuristics such as a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This framework constructs a composite policy by automating the selection of the heuristic type, its parameters, and the sourcing suppliers on an item-by-item basis. Computational results from twelve distinct instances demonstrate that the hyper-heuristic framework consistently identifies superior policies, with GA and EGA exhibiting the best overall performance. Our primary contribution is verifying that this item-level policy construction yields significant performance gains over simpler global policies, thereby justifying the associated computational cost.