A Proximal Policy Optimization Approach for Food Delivery Problem with Reassignment Due to Order Cancellation
SSRN, ISSN: 1556-5068
2023
- 305Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Unexpected cancellation of food delivery orders poses significant challenges to resource allocation planning and could lead to reduced revenue for service providers. This paper addresses this issue by developing an optimization framework that can reassign canceled orders to alternative customers with the objectives of maximizing revenue and minimizing resource wastage. The problem is formulated as a route-based Markov decision process, termed the Dynamic Routing and Pricing Problem with Cancellation (DRPPC). A solution approach based on the proximal policy optimization strategy is introduced as a computationally effective way of solving the optimization problem with the use of reinforcement learning techniques. Experimental results demonstrate that the proposed computational method outperforms selected benchmark approaches with higher revenue from varied customer segments. This investigation advances the intersection of urban logistics and reinforcement learning, offering actionable strategies for enhanced operational resilience in food delivery services.
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