A multi-objective modeling approach to harvesting resource scheduling: Decision support for a more sustainable Thai sugar industry

Abstract

This paper develops a multi-objective modeling approach for the scheduling of harvesting resources in the Thai sugar industry, in which different objectives stemming from different industry stakeholders are concurrently optimized with the overall goal to create a more sustainable sugar supply chain. In addition to traditional economic objectives, the environmental impact of sugarcane farm burning is included into the model to better reflect the current harvesting practice, where sugarcane growers often resort to burning their fields due to the lack of available harvesting resources during the season. An evolutionary algorithm based on a variant of Particle Swarm Optimization (PSO) is also devised to help solve the resulting Multi-Objective Harvesting Resource Scheduling Problem (MOHRSP), which normally becomes intractable for real-life problem instances. We find that the proposed PSO framework is notably efficient as it provides diverse sets of non-dominated solutions with markedly low coefficients of variation in a reasonable amount of time. We also find that, by sacrificing a slight amount of sugar production volume, the whole sugar supply chain could be largely improved, especially for the sugarcane growers, whose profitability turns out to be sensitive in the trade-offs with other objectives.