A Stochastic Simulation-Optimization Method for Generating Waste Management Alternatives Using Population-Based Algorithms
Abstract
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.
Full Text:
PDFDOI: https://doi.org/10.22158/asir.v3n3p92
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 Julian Scott Yeomans
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright © SCHOLINK INC. ISSN 2474-4972 (Print) ISSN 2474-4980 (Online)