Abstract
The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.
Highlights
city logistics terminal (CLT) shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections
Since logistics operators are stressed by increased demands, distribution centers and terminals are usually situated in the vicinity and/or within the urban areas
In addition to CLT location planning, the green p-median model (GMM) presented in this paper proposes the routes of EFV and EUV vehicles serving to supply the CLT users
Summary
Since logistics operators are stressed by increased demands, distribution centers and terminals are usually situated in the vicinity and/or within the urban areas. The multiobjective optimization model is proposed by Wang et al [31] as the supply chain network design considers cost of transportation, handling, and green technology acquisition They measure the CO2 emissions produced by production and distribution facilities. The problem refers to defining the CLT location within the discrete network to minimize harmful effects of logistics delivery vehicles to environmental and human health in urban areas. This approach becomes more important considering the fact that in the future it may be expected that transport companies, in countries with developed industries, will have increased number of EFVs in their fleets while number of ENF vehicles will be reduced.
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