A Stochastic Simulation-Optimization Method for Generating Waste Management Alternatives Using Population-Based Algorithms

While solving difficult stochastic engineering problems, it is often desirable to generate several quantifiably good options that provide contrasting perspectives. These alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process of creating maximally different solution sets has been referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization has frequently been used to solve computationally difficult, stochastic problems. This paper applies an MGA method that can create sets of maximally different alternatives for any simulation-optimization approach that employs a population-based algorithm. This algorithmic approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The efficacy of this stochastic MGA method is demonstrated on a waste management facility expansion case.

Forecasting Electricity Consumption Using Fuzzy Time Series

Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity. Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption. The procedure of the FTS and trapezoidal membership function was described together with January data. FTS is able to forecast UTHM electricity consumption quite well.