Greedy Approaches for Packing While Travelling with Deterministic and Stochastic Constraints
Thilina Pathirage Don, Aneta Neumann, Frank Neumann
TLDR
New greedy reward functions and a hyper-heuristic framework improve Packing While Travelling problem solutions for deterministic and stochastic constraints.
Key contributions
- Introduces novel greedy reward functions specifically tailored for the Packing While Travelling (PWT) problem.
- Extends these new reward functions within a hyper-heuristic framework for enhanced optimization.
- Adapts the tailored reward functions to address the chance-constrained PWT with stochastic weights.
- Demonstrates superior performance of tailored heuristics over standard ones in both deterministic and stochastic settings.
Why it matters
The PWT problem is an NP-hard challenge in logistics. This paper offers more effective greedy approaches, crucial for real-world optimized solutions. Its robust methods, applicable to both deterministic and stochastic constraints, improve decision-making in complex, uncertain settings.
Original 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.
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