Vol 5 No 11 (2019): IJRDO - Journal of Applied Science (ISSN: 2455-6653)

Waste Management Planning Using Stochastic Population-Based Procedures

Julian Scott Yeomans
OMIS Area, Schulich School of Business York University, 4700 Keele Street Toronto, ON, M3J 1P3 Canada
Published November 30, 2019
  • Waste management,
  • Population-based algorithms,
  • Metaheuristics,
  • Modelling-to-generate-alternatives,
  • Simulation-Optimization
How to Cite
Yeomans , J. S. (2019). Waste Management Planning Using Stochastic Population-Based Procedures. IJRDO - Journal of Applied Science (ISSN: 2455-6653), 5(11), 01-17. Retrieved from https://ijrdo.org/index.php/as/article/view/3342


When solving waste management (WM) problems, it is often preferable to consider a number of quantifiably good alternatives that provide multiple, disparate viewpoints. This is because solid waste planning generally involves complicated problems that are riddled with incompatible performance objectives and contain inconsistent design requirements that are very difficult to quantify and capture when supporting decision models must be constructed. These potential alternatives need to satisfy the required system performance criteria and yet be maximally different from each other in the decision space. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization approaches have frequently been employed to solve computationally difficult problems containing the significant stochastic uncertainties in waste management. This paper outlines an MGA approach for WM planning that can generate sets of maximally different alternatives for any stochastic, simulation-optimization method that employs a population-based solution procedure. 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 approach for creating alternatives is demonstrated using a “real world” waste management planning case.


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