AN APPROACH TO PRODUCTION SCHEDULING FOR REDUCING CARBON FOOTPRINT IN AN AGRICULTURAL TRANSPORT VEHICLE MANUFACTURING COMPANY: A CASE STUDY IN UTTARADIT PROVINCE
DOI:
https://doi.org/10.55766/sujst11901Keywords:
Agricultural-Cargo Truck, Makespan, Mathematical Programming Model, OptimizationAbstract
This study investigates a production scheduling problem for an agricultural vehicle manufacturer in Uttaradit Province. The primary goal is to generate an improved production schedule using a mathematical model. The model is designed to minimize the total time taken to produce an entire product known as make span, while also tracking the environmental impact, or product carbon footprint (CFP), during the production of each part. A computer program was created and a local search heuristic was used to find the optimal schedule. The data was obtained from customer orders and the company's production and maintenance teams. It included information such as the number of machines they had, how long each machine needed to set up, how long each product needed to produce, how much electricity they used, how much waste they generated, and the rules and constraints they imposed on production. The production scheduling problem was divided into three levels: small, medium, and large, allowing for the determination of the optimal schedule for each scenario. The results show that the mathematical model performs well on all problem sizes. For example, in the small-scale case with six jobs and seven machines, the model took 6,961.23 minutes to compute, which took only 0.214 seconds for all machines to be fully utilized, and the CFP was 91.791 kg of CO2 equivalent. In contrast, the company's original large-scale schedule took 38,561.12 minutes, or approximately 81 working days, at an 8-hour workday. However, with the revised schedule, the time was reduced to 26,365.22 minutes, or approximately 57 working days, a saving of 24 days. Using this mathematical model, the total production time was reduced by 11,139.94 minutes, efficiency was improved by 28.89%, and CFP was reduced by 497.759 kilograms of carbon dioxide equivalent, successfully achieving the main goal of the research.
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