Abstract
The authors previously analysed a real-world solid waste management (SWM) system using the solid waste optimization life-cycle framework (SWOLF) to identify optimal SWM strategies that meet modelled objectives (e.g. cost, environmental impacts, landfill diversion). While mathematically optimal strategies can support SWM decision making, they may not be readily implementable because of unmodelled objectives (e.g. practical limitations, social preferences, political and management considerations). A mathematical programming technique extending SWOLF is used to systematically identify, for several scenarios, different ‘optimal’ SWM strategies that are maximally different from each other in terms of waste flows, while meeting modelled objectives and constraints. The performance with respect to unmodelled issues was analysed to demonstrate the flexibility in potential strategies. Practitioner feedback highlighted implementation challenges due to existing practices; however, insights gained from this exercise led to more plausible and acceptable strategies by incrementally modifying the initial SWM alternatives generated.
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