Many models of real world problems, such as the toxic waste transportation and location problem, produce solutions that "make the best of a bad situation". Yet in many cases, giving the model better choices with which to work could produce far superior results. We introduce a framework for proactive optimization, defined as identification of the structural parameters within an OR problem that cause optimal solutions to be less than satisfactory, followed by an exogenous search for better options to add to the model. The proactive methodology is illustrated by a multiobjective, mixed-integer, location-allocation model with technology choice variables. A new technology, solar-driven waste detoxification, is compared with toxic waste incineration on three traditionally conflicting criteria: cost, a new risk measure (μg/m3 person hrs), and a new disequity measure (MinMaxSum kg*km). The solar process is found to improve all three objectives considerably. Sensitivity analysis indicates the robustness of the results in terms of cost, risk, and sunlight availability.
- Proactive optimization
- toxic waste
ASJC Scopus subject areas
- Geography, Planning and Development