Minimax regret optimization analysis for a regional solid waste management system

Solid waste management (SWM) facilities are crucial for environmental management and public health in urban regions. Due to the waste management hierarchy, one of the greatest challenges that organizations face today is to figure out how to diversify the treatment options, increase the reliability of infrastructure systems, and leverage the redistribution of waste streams among incineration, compost, recycling, and other facilities to their competitive advantage region wide. Systems analysis plays an important role for regionalization assessment of integrated SWM systems, leading to provide decision makers with break-through insights and risk-informed strategies. This paper aims to apply a minimax regret optimization analysis for improving SWM strategies in the Lower Rio Grande Valley (LRGV), an economically fast growing region in the US. Based on different environmental, economic, legal, and social conditions, event-based simulation in the first stage links estimated waste streams in major cities in LRGV with possible solid waste management alternatives. The optimization analysis in the second stage emphasizes the trade-offs and associated regret evaluation with respect to predetermined scenarios. Such optimization analyses with multiple criteria have featured notable successes, either by public or private efforts, in diverting recyclables, green waste, yard waste, and biosolids from the municipal solid waste streams to upcoming waste-to-energy, composting, and recycling facilities. Model outputs may link prescribed regret scenarios in decision making with various scales of regionalization. policies. The insights drawn from the system-oriented, forward-looking, and preventative study can eventually help decision-makers and stakeholders gain a scientific understanding of the consequences of short-term and long-term decisions relating to sustainable SWM in the fast-growing US-Mexico borderland. (c) 2006 Elsevier Ltd. All rights reserved.