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 CO₂ 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.

Background: The Vehicle Routing Problem with Time Windows (VRPTW) has been extensively researched due to its applicability across various real-world domains, including logistics, healthcare, and distribution systems. With the global elderly population projected to continue increasing, the demand for home healthcare (HHC) services is also on the rise. This work focuses on a specific application within an HHC system, aiming to minimize the total completion time for a fleet of vehicles delivering healthcare services to patients at home. Methods: We propose a mathematical model for the VRPTW, targeting a reduction in both customer and server waiting times on each route and seeking to decrease the total completion time. Our proposed algorithm employs a tabu search to narrow the search space, leveraging a greedy algorithm to establish the tabu list. Results: Our experimental results, based on Solomon’s benchmark datasets, demonstrate that the proposed algorithms achieve optimal solutions, particularly in minimizing total completion time compared to traditional methods, in a case study involving 400 customers where vehicles’ hours are restricted to align with caregivers’ average daily working hours. Conclusions: Our algorithm resulted in a 59% reduction in the number of vehicles required compared to the most recent algorithms, which combine k-mean clustering and local search.

We studied the N-species competitive coexistence model with direct effect on habitat destruction to analyze the behaviors of abundant and extinct species in the system caused by habitat loss. The nontrivial equilibrium points of the system are determined for a general habitat destruction function. For the trivial equilibrium, species that survived the habitat destruction are identified using eigenvalues of the Jacobian matrix. Solutions of the system are also presented using the recursive method. Three special cases of habitat destruction functions are addressed: continuous destruction, which is a typical habitat destruction; sudden habitat destruction, which is similar to natural phenomena such as earthquakes or floods; and sudden habitat destruction with aftershocks. The proportional abundances of 50 species are numerically portrayed in each case. We found that the survival of a species is guaranteed if its corresponding eigenvalue is positive. However, the fact that a species has negative corresponding eigenvalue does not guarantee its extinction, as this also depends on the initial number of that species.