A Multi-Skilled Approach to Property Maintenance Considering Temporal, Spatial and Resource Constraints

With the continued increase in age of the United States housing and building stock, as well as the continued need to maintain properties across the U.S., the need for timely, cost-optimal maintenance is ever more critical. This paper proposes the application of a mathematical model to aid in the scheduling and assignment of construction and maintenance tasks, considering the multi-skilled workforce. The benefit of this approach is to take advantage of the economies of scale that can be developed using cross-functional skilled workers with varying levels of competence and efficiency. This approach schedules and assigns tasks using data from maintenance task software datasets, using the least-cost, competent worker available for the job while also considering the trade-off between skilled labor cost and travel costs, both in terms of travel wage and vehicle wear and tear. The model is enhanced to include pairing between a mentor and an apprentice, where combined efficiency and pairing costs are considered at the same time as travel costs. Due to the practical nature of this research, a case organization was used and data from that firm was analyzed so that operational insights into the necessity of such a model could be considered. The mathematical backbone of the optimization approach to multi-skilled resource allocation considers the temporal and spatial demands of a geographically dispersed property management program. Actual, as opposed to sample, data allows us to evaluate the real financial implications on the case firm, if such an approach to scheduling is used. The generalization of this data provides excellent fit for a model that can be used to assign the best capable worker to the most cost-efficient task, considering deadlines, priorities and availability. Results of this scheduling approach provide significant cost and resource reductions over the historical firm performance, though practical considerations should temper that expectation. The above approach offers exceptional scalability and adaptability with the continued advancement of algorithm approaches to network-distribution and peer-to-peer work platforms

Techno-economic Optimization of Food Waste Diversion to Treatment Facilities to Determine Cost Effectiveness of Policy Incentives

New York state recently passed a food waste (FW) disposal ban, which will prohibit land-filling of FW produced by commercial entities generating more than 2 tons/week by the year 2022. This will redirect 370,000 tons/year of FW from landfills. In this work, we investigate three questions about the landfill ban: 1) How can this waste be diverted to treatment facilities at lowest system cost? 2) To what degree can an expanded anaerobic digestion portfolio help with the state’s Renewable Portfolio Standards? 3) What policies can help with the transition of waste-to-energy technologies? We develop a mixed-integer linear programming model that identifies lowest cost solutions to FW disposal by finding the optimal locations, capacities, FW intake, and energy generation around the state. Results suggest that a mix of composting (61%) and FW-only digestion (39%) is the cheapest response to the landfill ban without complementary policies but adding policy support (capital or production subsidies) can increase electricity production from digestion by 10x at a government cost between $55 and$315 per ton of CO2 abated