A new Simheuristics procedure for stochastic combinatorial optimization

TL;DR

This paper introduces a novel Simheuristic method for stochastic combinatorial optimization that adaptively balances deterministic and stochastic focus, improving solution efficiency in real-world scheduling problems.

Contribution

The paper presents a new Simheuristic procedure that dynamically switches focus based on a statistical model, enhancing optimization performance over existing strategies.

Findings

Outperforms standard Simheuristics in real-life scheduling tasks

Effectively balances exploration and exploitation during optimization

Demonstrates improved computational efficiency

Abstract

Ignoring uncertainty in combinatorial optimization leads to suboptimal decisions in practice. Nevertheless, the focus is often on deterministic combinatorial optimization problems, mainly because they are already challenging enough without stochasticity. To make it easier to address stochasticity in combinatorial optimization, Simheuristics have been developed that allow solving stochastic combinatorial optimization problems. We propose a new Simheuristic procedure that dynamically changes the optimization focus between a deterministic and stochastic perspective based upon a statistical model. By doing so, an adequate trade-off is made between exploration and exploitation of the solution space during the optimization. We numerically show that the new Simheuristic procedure solves real-life stochastic scheduling problems more efficiently than standard Simheuristics strategies.