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A Multi-Objective Harvest Scheduling Model for Sustainable Tea Supply Chains under Capacity and Farmer Preference Constraints
Achieving sustainability in agriculture requires resolving multiple optimization challenges. Among these, the harvest scheduling problem stands out. This paper explores scheduling tea harvests. The problem is to devise a harvest timetable that accounts for farmer quotas while respecting the constraints of both the collection center and the processing plant. The tea harvest is a cooperative effort between the grower and the factory. Factory administration is focused on resource utilization. Consequently, the process must incorporate the factory’s capacity, the collection centers’ capacities, and the frequency of transport trips. From the grower’s standpoint, it is observed that farming is often not their sole profession. On harvest days, growers are frequently absent from their main occupations. Integrating their preferred harvest days into the schedule is critical to the longevity of agricultural practices. Two separate mathematical formulations are introduced to tackle this problem. The initial formulation reduces the weekly transport frequency of factory vehicles, operating within the bounds of factory and collection center capacities. The subsequent formulation both curtails transport frequency and endeavors to satisfy grower preferences to the greatest extent feasible. A practical implementation was conducted in an area encompassing 12 collection centers, 988 growers, and 3392 decares of tea plantations. The outcomes demonstrate that the alignment rate with grower harvest preferences could be increased from 52% to 97% without altering the factory’s transport frequency. This finding reveals that accommodating grower preferences on harvest day imposes no negative consequences on the factory. Instead, it was determined that this practice bolsters both sustainability and incentive within agriculture. Additionally, the findings confirm the models’ effectiveness in resolving the problem.
Journal of Intelligent Agriculture and Food Systems
Review | Open access | 15 March 2025 | Article: 106