A corn-stover harvest scheduling problem arising in cellulosic ethanol production. (December 2017)

• Record Type: Journal Article
• Authors: Aguayo, Maichel M.; Sarin, Subhash C.; Cundiff, John S.; Comer, Kevin; Clark, Tim
• Is Part Of: Biomass and bioenergy. Volume 107(2017:Dec.)
• Page Start: 102
• Page End: 112
• Publication Date: 2017-12
• Subjects: Biomass logistics -- Harvest scheduling -- Corn-stover supply chain management -- Integer programming -- Mathematical modeling; Biomass energy -- Periodicals; Biomass -- Periodicals; Energy-Generating Resources -- Periodicals; Bioénergie -- Périodiques; 333.9539
• DOI: 10.1016/j.biombioe.2017.09.013
• Languages: English
• ISSNs: 0961-9534

Abstract

Abstract: In this paper, we address a corn-stover harvest scheduling problem (CSHSP) that arises when a cellulosic ethanol plant contracts with farmers to harvest corn stover after the grain harvest has been completed. The plant contracts a fleet of harvesting crews, which must be assigned by the plant scheduler to harvest fields as they are called in by the farmers over the harvest season. First, we study the static CSHSP, in which the call in times for the fields are assumed known at the beginning of the season, and propose a mathematical programming-based approach that we show to generate solutions with an average optimality gap of only 6.1% for real-life-inspired instances. We also consider the dynamic CSHSP, in which the call in times are not known at the beginning of the harvest season and the requests arrive randomly over time. The method that we develop for the solution of this problem incurs costs that are about 4.8% higher, on average, than those incurred for the static case. These results exhibit the proposed approach to be robust for use by a plant scheduler in his/her efforts to optimize harvest scheduling as the actual season unfolds. Our proposed approach can also effectively deal with uncertainties encountered in a commercial harvest. Highlights: Both the static and dynamic corn stover harvest scheduling problems (CSHSPs) are addressed. A novel mixed integer programming formulation for the static CSHSP is developed. Solution approaches are developed for both the static and dynamic CSHSPs. The effectiveness of the proposed approaches is demonstrated using a real-life data set. This proposed methodology can be used to deal with uncertainties faced as the actual harvest season unfolds.