Research On Distribution In Green Logistics System

With the swift and violent development of the economic globalization and information technology, the deep change is taking place in the social production, materials circulation, goods trade and management style. The position of the modern logistics service industry in the flow of the social productive materials and goods is prominent day by day. To our country, both green logistics and Olympic logistics are comparatively novel conception. The Olympic logistics, as a kind of typical green logistics, has already become the extremely concerned focus gradually at the time of entering count-down in Beijing 2008 Olympic Games. Therefore it has important realistic meaning and prospect of using to study the distribution based on green logistics. Research meaning of this thesis is not only to guide general logistics operation, to propagate green logistics idea in delivery industry, to change epitaxy developing model of our country logistics industry by high investment, high consumption, to promote logistics system towards the rationalization, and to promote the sustainable development of social economy, but also to aim at realizing green Olympic logistics, solving the problem of provide and deliver with reverse logistics causing by hosting Olympic Games, making the function of supporting and ensuring of logistics system better during the hosting of 2008 Olympic Games.This thesis sets out from analyzing intension of the green logistics. By analyzing the structure of general logistics distribution system and the structure of distribution subsystem in green logistics system, the factor of distribution optimization problem based on green logistics is confirmed. On the basis of it, this thesis sets up the Vehicle Routing Problem model of distribution based on green logistics by integer of mixing planning, after considering the logistics network of distribution and picking-up. The model is solved with improved genetic algorithms. This thesis designsC language source program and verifies the feasibility and practicability of the model with an instance finally.