There is a wide range of sequential decision problems in transportation and logistics that require dealing with uncertainty. There are four classes of policies that we can draw on for different types of decisions, but many problems in transportation and logistics will ultimately require some form of direct lookahead policy (DLA) where we optimize decisions over some horizon to make a decision now. The most common strategy is to use a deterministic lookahead (think Google maps), but what if you want to handle uncertainty? In this paper, we identify two major strategies for designing practical, implementable lookahead policies which handle uncertainty in fundamentally different ways. The first is a suitably parameterized deterministic lookahead, where the parameterization is tuned in a stochastic simulator. The second uses an approximate stochastic lookahead, where we identify six classes of approximations, one of which involves designing a “policy-within-a-policy,” for which we turn to all four classes of policies. We claim that our approximate lookahead model spans all the classical stochastic optimization tools for lookahead policies, while opening up pathways for new policies. But we also insist that the idea of a parameterized deterministic lookahead is a powerful new idea that offers features that, for some problems, can outperform the more familiar stochastic lookahead policies.

中文翻译：

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为运输和物流中的顺序决策问题设计前瞻性策略

运输和物流中存在大量需要处理不确定性的顺序决策问题。对于不同类型的决策，我们可以利用四类政策，但运输和物流中的许多问题最终将需要某种形式的直接前瞻性政策 (DLA)，在这种政策中，我们在某个范围内优化决策以立即做出决定。最常见的策略是使用确定性前瞻（想想谷歌地图），但如果你想处理不确定性怎么办？在本文中，我们确定了设计实用、可实施的前瞻性政策的两种主要策略，这些政策以根本不同的方式处理不确定性。第一个是适当参数化的确定性前瞻，其中参数化在随机模拟器中进行调整。第二个使用近似随机前瞻，我们确定了六类近似值，其中一个涉及设计“策略中的策略”，为此我们转向所有四类策略。我们声称我们的近似前瞻模型涵盖了前瞻策略的所有经典随机优化工具，同时为新策略开辟了道路。但我们也坚持认为，参数化确定性前瞻的想法是一个强大的新想法，它提供的功能对于某些问题可以胜过更熟悉的随机前瞻策略。我们声称我们的近似前瞻模型涵盖了前瞻策略的所有经典随机优化工具，同时为新策略开辟了道路。但我们也坚持认为，参数化确定性前瞻的想法是一个强大的新想法，它提供的功能对于某些问题可以胜过更熟悉的随机前瞻策略。我们声称我们的近似前瞻模型涵盖了前瞻策略的所有经典随机优化工具，同时为新策略开辟了道路。但我们也坚持认为，参数化确定性前瞻的想法是一个强大的新想法，它提供的功能对于某些问题可以胜过更熟悉的随机前瞻策略。