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.

翻译：旅行小偷问题（TTP）是一个著名的多组件优化问题，它刻画了城市间旅行路线与物品打包这两个组件之间的相互依存关系。旅行中打包问题（PWT）是TTP中一个NP难的子问题，其目标是在给定固定旅行路线的条件下优化物品打包方案。在许多求解器中，打包组件常采用贪心启发式方法处理。在此类方法中，合适的贪心函数对于贪心算法的成功至关重要。本文针对PWT问题引入了新的奖励函数，并将其扩展至超启发式框架以获取更优性能。此外，我们还研究了适用于贪心方法的带有机会约束的PWT问题，并将新引入的奖励函数应用于随机权重场景。实验结果清晰表明，在确定性与随机约束条件下，定制化启发式方法相较于标准启发式方法具有显著优势。