A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent ...A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.展开更多
This paper presents a life cycle assessment(LCA) based biofuel supply chain(SC) analysis framework which enables the study of economic, energy and environmental(3E) performances by using multi-objective optimization. ...This paper presents a life cycle assessment(LCA) based biofuel supply chain(SC) analysis framework which enables the study of economic, energy and environmental(3E) performances by using multi-objective optimization. The economic objective is measured by the total annual profit, the energy objective is measured by the average fossil energy(FE) inputs per MJ biofuel and the environmental objective is measured by greenhouse gas(GHG) emissions per MJ biofuel. A multi-objective linear fractional programming(MOLFP) model with multi-conversion pathways is formulated based on the framework and is solved by using the ε-constraint method. The MOLFP problem is turned into a mixed integer linear programming(MILP) problem by setting up the total annual profit as the optimization objective and the average FE inputs per MJ biofuel and GHG emissions per MJ biofuel as constraints. In the case study, this model is used to design an experimental biofuel supply chain in China. A set of the weekly Pareto optimal solutions is obtained. Each non-inferior solution indicates the optimal locations and the amount of biomass produced, locations and capacities of conversion factories, locations and amount of biofuel being supplied in final markets and the flow of mass through the supply chain network(SCN). As the model reveals trade-offs among 3E criteria, we think the framework can be a good support tool of decision for the design of biofuel SC.展开更多
文摘A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.
基金Supported by the Chinese Academy of Engineering(20121667845)
文摘This paper presents a life cycle assessment(LCA) based biofuel supply chain(SC) analysis framework which enables the study of economic, energy and environmental(3E) performances by using multi-objective optimization. The economic objective is measured by the total annual profit, the energy objective is measured by the average fossil energy(FE) inputs per MJ biofuel and the environmental objective is measured by greenhouse gas(GHG) emissions per MJ biofuel. A multi-objective linear fractional programming(MOLFP) model with multi-conversion pathways is formulated based on the framework and is solved by using the ε-constraint method. The MOLFP problem is turned into a mixed integer linear programming(MILP) problem by setting up the total annual profit as the optimization objective and the average FE inputs per MJ biofuel and GHG emissions per MJ biofuel as constraints. In the case study, this model is used to design an experimental biofuel supply chain in China. A set of the weekly Pareto optimal solutions is obtained. Each non-inferior solution indicates the optimal locations and the amount of biomass produced, locations and capacities of conversion factories, locations and amount of biofuel being supplied in final markets and the flow of mass through the supply chain network(SCN). As the model reveals trade-offs among 3E criteria, we think the framework can be a good support tool of decision for the design of biofuel SC.
