Greedy Approaches for Packing While Travelling with Deterministic and Stochastic Constraints

TL;DR

This paper introduces new greedy heuristics and a hyper-heuristic framework for the packing while travelling problem, improving solution quality under deterministic and stochastic constraints.

Contribution

It develops tailored reward functions and extends them to stochastic weights within a hyper-heuristic framework for PWT.

Findings

Tailored heuristics outperform standard heuristics in deterministic PWT.

Reward functions effectively adapt to stochastic weights in PWT.

Experimental results show significant improvements in solution quality.

Abstract

The travelling thief problem (TTP) is a well-known multi-component optimisation problem that captures the interdependence between two components: the tour across cities and the packing of items. The packing while travelling problem (PWT) is an NP-hard subproblem of TTP where the packing of items should be optimised for a given fixed tour. In many solvers, the packing component is often addressed using greedy heuristics. Here, the use of suitable greedy functions is essential for the success of greedy algorithms. In this paper, we introduce new reward functions tailored to the PWT and extend them to a hyper-heuristic framework to achieve further advantage. Furthermore, we investigate the chance constrained PWT for greedy approaches and adopt the newly introduced reward functions for stochastic weights. The experimental results clearly demonstrate the benefit of the tailored heuristics over the standard heuristics in both deterministic and stochastic constraints.