Managing the waste generated after response operations is the most challenging part of an offshore oil spill. A waste estimation is required before deciding on the transportation, treatment, and disposal of each type of oil spill waste. So, firstly, this thesis developed a system dynamics model to estimate the quantity of each type of oily waste generated after oil spill response operations, considering different aspects (e.g., weather conditions, the spilled oil volume and characteristics, response time and equipment). The results of the model for an actual oil spill in BC, Canada (2016), as the case study, showed a 86% average accuracy. Sensitivity analysis of the case study illustrated that a five-hour decrease in the response arrival time could increase the oil recovery by 26%. Moreover, sensitivity analysis showed a possibility of 45% overuse of sorbents for the case study. Response surface methodology (RSM) also was conducted, and the significant interaction effects between sea temperature and response arrival time on recovered oil and between sorbent boom weight and sorbent booms usage rate on solid waste were demonstrated. In addition to the oil spill response waste (OSRW) quantity estimation model, the study developed a scenariobased decision-making framework as the second objective to provide the most monetary beneficial strategies to deal with each collected OSRW under different scenarios of impact factors (e.g., waste quantity, waste quality, location, capacity, and availability of treatment and disposal facilities). An optimization model with an objective of minimizing net costs was developed to evaluate all scenarios using hypothetical and actual data. Results were categorized to develop the decisionmaking framework. It was illustrated that oil processing is the best option for managing liquid oily waste from spilled refined oil. For liquid oily waste from crude spilled oil, the oil refinery is the best option if the quantity is above a defined limit in this study. For solid oily waste management, pyrolysis is the most appropriate destination. The optimum solutions and sensitivity analysis for the actual data of a case study validated the results.
