Development of Heuristics in Sugarcane Harvest Scheduling for Mechanical Harvester in Sugarcane Supply Chain

Abstract

This chapter focuses on the inbound logistic section of the sugarcane industry, which is one of the important industries in Thailand. For inbound logistics, there are three major operation steps: cultivation, harvest, and transportation. From the three operations of inbound logistics, the highest inbound logistic cost is harvest. At present, there are three harvest patterns in Thailand: (1) labor cutting and loading, (2) labor cutting and trans-loader truck, and (3) mechanized harvester (cane harvester). The cane harvester usage tends to increase because of lack of labor and increasing cane production costs. In addition, the pattern of harvesting management depends on the experience and expertise of operators. So it is necessary to develop decision-making tools for high management efficiency and reduced risk of uncertainty. In this chapter, a genetic algorithm (GA) was developed to solve the harvest scheduling problem, which consists of 2 main parts: (1) sugarcane field clustering and (2) harvester routing. The objective is to reduce the harvesting cost by minimizing harvester travel distance. Experimental results show that the developed heuristic is quite effective and gives better result than the current practice from the real case of the sugarcane industry.