Quantification of social metrics for use in optimization: An application to solid waste management

Abstract

Solid waste management (SWM) is an interdisciplinary field which requires a range of metrics to make informed decisions. Social indicators are of high interest to decision-makers but are particularly difficult to integrate into optimization frameworks, largely due to challenges of quantification. This study presents a methodology for quantifying a social metric for integration into sustainability assessment of solid waste management (SWM) systems using optimization. To identify social indicators, waste managers were consulted in Columbia, Missouri, USA. Meetings were held prior to indicator creation and reviewed mid-project with stakeholders. A number of concerns that could be categorized as social were raised. For the two most pressing issues to managers, quantitative metrics were created. First, SWM experiences high employee turnover, largely due to low wages. Turnover leads to less efficiency in collection and treatment, gaps in service, and cost to citizens. Hence, the first social metric proposed represents turnover of employees including loss of productivity, hiring and replacement costs, and quit rate. Second, this work estimated the value of exposure risk associated with manual material handling activities. This second social metric considered a worker's physical exposure to risk via activities of lifting, carrying, placing, emptying, and sitting. These social metrics were used within a multi-criterion decision-making framework for SWM, extending the traditional focus on economic and environmental objective functions. Results illustrate the trade-offs among these conflicting criteria and provide managerial insights into the costs and benefits of different waste management strategies.