Greedy Approaches for Packing While Travelling with Deterministic and Stochastic Constraints

2026-04-15

AI Summary

This work addresses the NP-hard optimization challenge in the Packing While Travelling (PWT) problem, where loading decisions and travel costs are tightly coupled under a fixed route. To tackle this, the authors propose a customized greedy reward function tailored for PWT and, for the first time, integrate it into a hyper-heuristic framework capable of handling both deterministic and chance-constrained stochastic weight scenarios. By synergistically combining greedy heuristics, hyper-heuristic strategies, and stochastic optimization techniques, the approach maintains computational efficiency while effectively managing uncertainty. Experimental results demonstrate that the proposed method significantly outperforms existing standard heuristics under both deterministic and stochastic constraints, confirming its efficacy and robustness.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Packing While Travelling
• Travelling Thief Problem
• Greedy Heuristics
• Stochastic Constraints
• NP-hard

Innovation

• reward function
• hyper-heuristic
• chance-constrained optimization
• packing while travelling
• greedy heuristic