TY - JOUR
T1 - Oil spill response planning with consideration of physicochemical evolution of the oil slick
T2 - A multiobjective optimization approach
AU - Zhong, Zhixia
AU - You, Fengqi
PY - 2011/8/10
Y1 - 2011/8/10
N2 - This paper addresses the optimal planning of oil spill response operations under economic and responsive criteria, with consideration of oil weathering process. The economic criterion is measured by total cost, while the measure of responsiveness is the time span of the entire response operations. A bi-criterion, multiperiod mixed-integer linear programming (MILP) model is developed that simultaneously predicts the optimal time trajectories of oil volume and slick area, transportation profile, response resource utilization levels, cleanup schedule, and coastal protection plan. The MILP model integrates with the prediction of an oil weathering model that accounts for oil physicochemical properties, spilled amount, hydrodynamics, and weather conditions. The multi-objective optimization model is solved with the epsilon-constraint method and produces a Pareto optimal curve that reveals how the optimal total cost and response operations change under different specifications of responsiveness. We present two illustrative examples for oil spill incidents in the Gulf of Mexico and New England.
AB - This paper addresses the optimal planning of oil spill response operations under economic and responsive criteria, with consideration of oil weathering process. The economic criterion is measured by total cost, while the measure of responsiveness is the time span of the entire response operations. A bi-criterion, multiperiod mixed-integer linear programming (MILP) model is developed that simultaneously predicts the optimal time trajectories of oil volume and slick area, transportation profile, response resource utilization levels, cleanup schedule, and coastal protection plan. The MILP model integrates with the prediction of an oil weathering model that accounts for oil physicochemical properties, spilled amount, hydrodynamics, and weather conditions. The multi-objective optimization model is solved with the epsilon-constraint method and produces a Pareto optimal curve that reveals how the optimal total cost and response operations change under different specifications of responsiveness. We present two illustrative examples for oil spill incidents in the Gulf of Mexico and New England.
KW - MILP
KW - Multi-objective optimization
KW - ODE
KW - Oil spill response
KW - Planning
UR - http://www.scopus.com/inward/record.url?scp=79960558444&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960558444&partnerID=8YFLogxK
U2 - 10.1016/j.compchemeng.2011.01.009
DO - 10.1016/j.compchemeng.2011.01.009
M3 - Article
AN - SCOPUS:79960558444
VL - 35
SP - 1614
EP - 1630
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
SN - 0098-1354
IS - 8
ER -