Efficiencies of Single-Objective Linear Programing and Multi-Objective Optimization: A Case of Electricity Generation in Thailand

Single-objective linear programming has been employed as an optimization technique for most single-objective problems to obtain the best solutions for further enhancement. Recently, practical problems are required to fulfill more than one objective function. Although repeated application of single-objective linear programming can solve problems with more than one objective function, multi-objective optimization techniques were introduced for the optimization of problems with more than one objective function. It is worthwhile to investigate the efficiencies of single-objective linear programming and multi-objective optimization, applied to the same complex objective functions. Meanwhile, information on electricity generation in Thailand in 2024 is adopted as the case study for comparison for the optimization of three objective functions: minimum unit cost, minimum greenhouse gas emission, and maximum employment. The results show that the total time required to run the three objective functions with the linear programming technique is shorter than the runtime required by multi-objective optimization. There are no significant differences in the memory used by both techniques. Both optimization techniques clearly indicate optimized combinations of power sources. Single-objective inear programming shows a possible saveing as much as 69,538.44 million Baht in generation costs in 2024, 14.782 million tons of CO2eq greenhouse gas emissions, and 638 more jobs.