Abstract:In Northern Thailand, the size and topographical structure of farmland makes it necessary for operators of small-scale waste management systems to be able to reach their clients in an effective manner. Over the past decades, corn contract farming has increased, and the chief method for eliminating waste from these farms has chiefly been open burning on the fields, which produces enormous amounts of greenhouse gases (GHG) and Polycyclic Aromatic Hydrocarbons (PAHs). To find a way to reduce GHG emissions in the corn production system, this work focuses on finding clusters with minimum transportation time from waste disposal centers. To solve the clustering problems, four models are created and solved on AIMMS and MATLAB. Simulation results indicate that the number of clients essentially affects the performance of the procedure. The case studies are on corn production management in Chiang Mai, the region's economic capital, as well as in 9 provinces in Northern Thailand, including Chiang Mai, whose combined corn production comprises 32.73 percent of the national production. With roughly 15% of the corn cobs and husks involved in the study, we found that by changing the waste elimination process, the total CO 2 emissions can be reduced by up to 12,008.40 tons per year in Chiang Mai and up to 180,198.14 tons per year in the 9 provinces of Northern Thailand.

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The authors study the corn crop residue management system for 16 provinces in northern Thailand encompassing 127 agricultural cooperatives (co-ops), 974 corn fields and 274 customers. To solve the system's problems, we find clusters where coops will pick up crop residues from corn fields, process them into biomass fuel and sell the fuel to customers. Each cluster consists of a coop , a set of corn fields and a set of customers, the latter two being on separate routes from the coop. To minimize the system's transportation cost and balance transportation cost between clusters, we propose a mathematical model with two objective functions, construct two heuristics, and apply the two heuristics to solve the problem.