This paper studies the optimization of cold chain integrated inventory routing problem while considering carbon emissions. First, the carbon footprint in inventory and transportation process for cold chain logistics is accurately identified and quantified. Secondly, based on the carbon regulations, which are carbon cap, carbon cap and offset, carbon cap and trade, and carbon tax regulations, four green cold chain inventory routing optimization models that minimize the total cost are constructed, respectively. Subsequently, a genetic simulated annealing algorithm (GASA) is developed in order to efficiently solve the models, which combines the advantages of the two algorithms. The effectiveness of the algorithm and the models is verified by numerical comparison experiments. Further, a set of numerical experiments is conducted to examine in detail the effectiveness of each regulation with the change of cap, carbon price, and unit fuel price in order to investigate the difference of these regulations’ impacts on the cold chain logistics. The research results show that (a) the cap and price plays a relatively important role, for their value setting may even lead to the invalidation of the regulations and the development of the enterprises; (b) carbon cap and carbon tax regulations are more powerful when compared to the other two regulations, which reduce more carbon emissions, but also pose more challenge to the enterprises’ economic development; (c) overall, cap and trade regulation is better than cap and offset regulation, because, when the cap is not sufficient, the two regulations are almost as good, but when the cap is sufficient, the offset policy is invalid; and, (d) unlike the traditional logistics, the increase of unit fuel price will not reduce carbon emissions. Several practical managerial implications for government and enterprises are also provided based on research results.
Optimization of Integrated Inventory Routing Problem for Cold Chain Logistics Considering Carbon Footprint and Carbon Regulations
