Development of a Stochastic Multicriteria Algorithm for Generating Waste Management Facility Expansion Alternatives

When addressing waste management (WM) planning issues, it is frequently useful to present a large number of quantifiably excellent solutions that offer distinctive, contrasting views. This is because, when supporting decision models are required, WM planning frequently entails complex problems with a wide range of performance targets and design criteria that are challenging to identify and explain. The produced alternatives must satisfy all of the aforementioned system requirements while being maximally distinct from one another in the needed decision space. Using modelling to generate alternatives (MGA), you may produce as many different sets of solutions as you can. Simulation-optimization techniques have been routinely used to tackle computationally difficult stochastic WM issues. The stochastic multicriteria MGA strategy for WM planning is described in this study, and it may provide sets of maximally varied alternatives for any simulation-optimization technique that employs a population-based solution algorithm. This algorithmic approach is computationally effective since it offers the required number of maximally diversified solution alternatives in a single computing run of the procedure. The effectiveness of this stochastic MGA technique is demonstrated on a “real-world” waste management facility expansion instance.

Author (s) Details:

Julian Scott Yeomans,
OMIS Area, Schulich School of Business, York University, Toronto, M3J 1P3, Canada.

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Keywords: Modelling-to-generate-alternatives, simulation-optimization, waste management planning, population-based algorithms, multicriteria, algorithms, facility expansion.

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