An application of artificial intelligence for solving multi-visit scheduling and routing of multi-heterogeneous resources

This research focuses on the development of an artificial multiple intelligence system (AMIS) for solving multi-visit scheduling and routing of multi-heterogeneous resources. The proposed method has been developed as a decision-making tool for solving mechanical sugarcane harvest operations which have been replacing the manual harvesting system with the sugarcane field burning. The mechanical sugarcane harvesting system is a fresh one with a high potential reduction of CO2 emission. Two resources which are fuel service staff teams and technician teams were considered to support the mechanical harvester operations in order to improve the harvesters' productivity and stability of its sugarcane supply by minimizing the downtime or waiting time of harvesters. Based on this approach, not only the sugar production is efficient, but also the harvesting which is the inbound activity is fuel-efficient. This problem was formulated as the allocation and scheduling of multi-Heterogeneous Resources with consideration of transportation for both resources and service operations. Sugarcane harvesters which get services from the workforce are geographically scattered in each time period. There are various technicians and fuel service staff with different skills giving services to the harvesters. The workforce allocation (WFAllcn) and the sequences and routing (WFSeqRoute) sub-problems were modeled as the integrating problem with the objective function to maximize the sugarcane harvested by all harvesters. To solve the problem, the Artificial Multiple Intelligence System (AMIS), which was developed with new intelligence box selection rules, is firstly developed. Using this approach, allocation and scheduling, and routing of technicians and fuel service staff in sugarcane mechanical harvest operations is very efficient.