Q. Tan

An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

In this study, an interactive decision support system (UREM-IDSS) has been developed based on an inexact optimization model (UREM, University of Regina Energy Model) to aid decision makers in planning energy management systems. Optimization modeling, scenario development, user interaction, policy analysis and visual display are seamlessly integrated into the UREM-IDSS. Uncertainties in energy-related parameters are effectively addressed through the interval linear programming (ILP) approach, improving the robustness of the UREM-IDSS for real-world applications. Thus, it can be used as an efficient tool for analyzing and visualizing impacts of energy and environmental policies, regional/community sustainable development strategies, emission reduction measures and climate change in an interactive, flexible and dynamic context. The Region of Waterloo has been selected to demonstrate the applicability and capability of the UREM-IDSS. A variety of scenarios (including a reference case) have been identified based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy, and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting complex tradeoffs among energy-related, environmental and economic considerations. Results indicate that the UREM-IDSS can be successfully used for evaluating and analyzing not only the effects of an individual policy scenario, but also the variations between different scenarios compared with a reference case. Also, the UREM-IDSS can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo, and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies, emission reduction measures, and climate change within an integrated and dynamic framework.

I-VFRP: An interval-valued fuzzy robust programming approach for municipal waste-management planning under uncertainty

In this study, an interval-valued fuzzy robust programming (I-VFRP) model has been developed and applied to municipal solid-waste management under uncertainty. The I-VFRP model can explicitly address system uncertainties with multiple presentations, and can directly communicate the waste managers confidence gradients into the optimization process, facilitating the reflection of weak or strong confidence when subjectively estimating parameter values. Parameters in the I-VFRP model can be represented as either intervals or interval-valued fuzzy sets. Thus, variations of the waste managers confidence gradients over defining parameters can be effectively handled through interval-valued membership functions, leading to enhanced robustness of the optimization efforts. The results of a theoretical case study indicate that useful solutions for planning municipal solid-waste-management practices can be generated. The waste managers confidence gradients over various subjective judgments can be directly incorporated into the modeling formulation and solution process. The results also suggest that the proposed methodology can be applied to practical problems that are associated with complex and uncertain information.

Multi-Source Multi-Sector Sustainable Water Supply Under Multiple Uncertainties: An Inexact Fuzzy-Stochastic Quadratic Programming Approach

This paper presents the development and the first application of a superiority–inferiority-based inexact fuzzy-stochastic quadratic programming (SI-IFSQP) approach for sustainable water supply under multiple uncertainties. SI-IFSQP improves conventional nonlinear programming by tackling multiple uncertainties within an individual parameter; SI-IFSQP is also superior to existing inexact methods due to its reflection of economies of scale and reduction of computational requirements. An interactive solution algorithm with high computational efficiency was also proposed. The application of SI-IFSQP to long-term planning of a multi-source multi-sector water supply system demonstrated its applicability. The close reflection of system complexities, such as multiple uncertainties, scale economies and dynamic parameters, could enhance the robustness of the optimization process as well as the acceptability of obtained results. Corresponding to varied system conditions and decision priorities, the interval solutions from SI-IFSQP could help generate a series of long-term water supply strategies under a number of economic, environmental, ecological, and water-security targets.

An interval full-infinite programming method to supporting environmental decision-making

An interval full-infinite programming (IFIP) method is developed by introducing a concept of functional intervals into an optimization framework. Since the solutions of the problem should be ‘globally’ optimal under all possible levels of the associated impact factors, the number of objectives and constraints is infinite. To solve the IFIP problem, it is converted to two interactive semi-infinite programming (SIP) submodels that can be solved by conventional SIP solution algorithms. The IFIP method is applied to a solid waste management system to illustrate its performance in supporting decision-making. Compared to conventional interval linear programming (ILP) methods, the IFIP is capable of addressing uncertainties arising from not only the imprecise information but also complex relations to external impact factors. Compared to SIP that can only handle problems containing infinite constraints, the IFIP approaches are useful for addressing inexact problems with infinite objectives and constraints.