Application of Inexact Full-infinite Chance-constrained Mixed Integer Optimization Model in Solid Waste Management

In order to more effectively reflect the multiple uncertainty in the solid waste management system, a combination of the optimization methods was proposed to solve the uncertainty problems e.g. of the interval parameters, functional parameters and random parameters, and an inexact full-infinite chance-constrained mixed integer linear programming (IFICCMILP) model was proposed. Conventional inexact mixed integer programming model (IMILP) and IFICCMILP were used and compared to expound the applicability and characteristics of the latter. The optimization result of IFICCMILP had great differences from IMILP due to the diversity of parameters input and strict constraint conditions. The system costs of conventional model were lower than the proposed model due to the only one objective function and the reduced number of constraints. However, the proposed model was more reliable since it guaranteed that all the constraints and objectives were satisfied, and thus the system risk due to less consideration of possible constraints and other parameters could be avoided. Moreover, the proposed model presented optimization plans of different risk levels, which provided more decision space for the decision makers.