Adaptive large neighborhood search for harvest scheduling: A case study of rice inbound logistics in An Giang province

Abstract

This paper aims to present a research-based model of scheduling harvesting problems. It states that this sector has made significant improvements in recent years in figuring out how to optimize the cost in the supply chain of paddy rice. In this paper, based on the previous methods, I extend an existing approach and add more external factors to deal with harvesting scheduling problems. On the other hand, there are two mathematical models with specific parameters applied to solve sperate problems: minimize the travelling distance for small fields and operation cost for large fields, by more complexible data which approach clearly with reality. The framework paper described here is both simple and universal aim for comparison outputs with historical data. The paper finds that significant results can be conducted for optimizing the outputs when using these models and harvesting plan could be built for company reference. Moreover, to enhance these outputs, I also apply Adaptive Large Neighborhood Search (ALNS). The key limitation in this research lies in the difficulty in linking two sperate models. For further research, I would like to expand to consider more external factors and create a consistent model to analysis.