Optimization of Integrated Inventory Routing Problem for Cold Chain Logistics Considering Carbon Footprint and Carbon Regulations

Abstract

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.

A Sustainable Distribution Design for Multi-Quality Multiple-Cold-Chain Products: An Integrated Inspection Strategies Approach

“…GhannadPour et al [24] analyzed the effect of carbon tax policy on carbon emissions in the distribution design of the cold chain and proposed a dynamic vehicle-routing problem with fuzzy travel time. In Li et al [25], a simultaneous analysis of inventory control and distribution planning was performed to reduce the carbon emission of cold-chain products.…”

“…Furthermore, cost has frequently been used as a performance assessment criterion and although different functions of cost have been modelled, the literature lacks in analyzing inspection cost (IC) and its impact on the overall cost solution. [19] 2019 Li et al [25] 2019 Song et al [30] 2020 Yan et al [31] 2020 Qiang et al [32] 2020 Wang et al [33] 2020 Zhao et al [34] 2020 TE = transportation emissions, DE = deterioration emissions, Ho = homogeneous vehicles, He = heterogeneous vehicles, PC = penalty cost, IC = inspection cost.…”

Optimization of Conventional and Green Vehicles Composition under Carbon Emission Cap

“…Heinold and Meisel [8] addressed a routing problem for freight companies where the CO 2 emission cap must be satisfied for each delivery order in a road and rail transportation intermodal network. Li et al [9] optimized an inventory routing problem for cold chain logistics under the CO 2 emission cap. Jharkharia and Das [10] examined the impact of the CO 2 emission cap in a vehicle routing problem with integrated delivery and pickup operations.…”

A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions

“…Detailed information is displayed in Table 6, including of the position and amount of waste. And we set the other parameters of the CCLCVRP model according to the former studies [44,51,52], which are shown in Table 7. CCP is an efficient tool to solve the stochastic problem and credibility level γ in CCP is predetermined representing the probability of path success without overloading and reflecting the dispatcher's risk preference.…”