A green vehicle routing model based on modified particle swarm optimization for cold chain logistics

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

This study aims to design vehicle routes based on cost minimisation and the minimisation of greenhouse gasses (GHG) emissions to help companies solve the vehicle routing problem with pickup and delivery (VRPPD) via particle swarm optimisation (PSO). An effective metaheuristics search technique called particle swarm optimisation (PSO) was applied to design the optimal route for these problems. Simulated data from Li and Lim (2001) were used to evaluate the PSO performance for solving green vehicle routing problems with pickup and delivery (Green VRPPD). The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to customers living in a specific area called a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed. PSO has been confirmed for solving VRPPD effectively. The study compared the results based on the use of two different objective functions with fuel consumption from diesel and liquefied petroleum gas (LPG). It indicates that solving VRPPD by considering the emissions of direct greenhouse gases as an objective function provides cleaner routes, rather than considering total cost as the objective function for all test cases. However, as Green VRPPD requires more vehicles and longer travel distances, this requires a greater total cost than considering the total cost as the objective function. Considering the types of fuels used, it is obvious that LPG is more environmentally friendly than diesel by up to 53.61 %. This paper should be of interest to a broad readership, including those concerned with vehicle routing problems, transportation, logistics, and environmental management. The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed.

In order to reduce the cost pressure on cold-chain logistics brought by the carbon tax policy, this paper investigates optimization of Vehicle Routing Problem (VRP) with time windows for cold-chain logistics based on carbon tax in China. Then, a green and low-carbon cold chain logistics distribution route optimization model with minimum cost is constructed. Taking the lowest cost as the objective function, the total cost of distribution includes the following costs: the fixed costs which generate in distribution process of vehicle, transportation costs, damage costs, refrigeration costs, penalty costs, shortage costs and carbon emission costs. This paper further proposes a Cycle Evolutionary Genetic Algorithm (CEGA) to solve the model. Meanwhile, actual data are used with CEGA to carry out numerical experiments in order to discuss changes of distribution routes with different carbon emissions under different carbon taxes and their influence on the total distribution cost. The critical carbon tax value of carbon emissions and distribution cost is obtained through experimental analysis. The research results of this paper provide effective advice, which is not only for the government on carbon tax decision, but also for the logistics companies on controlling carbon emissions during distribution.

In recent years, reducing emissions has become an important issue. Besides reducing the economic costs, reducing the fuel consumption decreases emissions, pollutant impact and increases society health as well. Green vehicle routing problem are a major key to reduce hazardous effects of transportation such as air pollution, Greenhouse Gas (GHG) emissions, noise and the like. Generally, the amount of pollution emitted by a vehicle over an arc depends on many factors like vehicle load, travel speed, travel distance, road slop and etc. Vehicle load has a major effect among other factors on amount of emissions and influences the route selection. Some works were completed on the estimation of the cost of the GHG emissions. Therefore, the effect of the carried load in fuel consumption is contributed in the model by minimizing a weighted load function. This paper presents a new method for vehicle routing problem with minimizing fuel consumption and number of vehicles. Distributing managers are often interested in minimizing fuel consumption caused by two reasons: 1) reducing fuel consumption caused to reduce the service cost, economic costs and increasing customer’s satisfaction, and 2) reducing fuel consumption is a way for reducing pollutant negative impact on our environment and increasing society health. Also, minimizing the number of vehicles is caused the reducing in fixed and other related cost. It is proven that VRPs belong to the category of NP-Hard problems thus due to the complexity of VRP with exact methods in large-scale problems, a meta-heuristic method based on particle swarm optimization is proposed, so called improved particle swarm optimization (IPSO). In addition, to show the efficiency of the proposed IPSO, a number of test problems in small and large sizes are proposed and solved by the IPSO. Then, the obtained results are evaluated with the results obtained by Lingo.

PurposeThis paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic conditions, and then a low-carbon cold chain logistics routing optimization model was proposed. The purpose of this paper is to minimize the carbon emission and distribution cost, which includes vehicle operation cost, product freshness cost, quality loss cost, penalty cost and transportation cost.Design/methodology/approachThis study proposed a mathematical optimization model, considering the distribution cost and carbon emission. The improved Nondominated Sorting Genetic Algorithm II algorithm was used to solve the model to obtain the Pareto frontal solution set.FindingsThe result of this study showed that this model can more accurately assess distribution costs and carbon emissions than those do not take real-time traffic conditions in the actual road network into account and provided guidance for cold chain logistics companies to choose a distribution strategy and for the government to develop a carbon tax.Research limitations/implicationsThere are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle.Originality/valueExisting research on low-carbon VRP for cold chain logistics ignores the complexity of the road network and the time-varying traffic conditions, resulting in nonmeaningful planned distribution routes and furthermore low carbon cannot be discussed. This study takes the complexity of the road network and the time-varying traffic conditions into account, describing the distribution costs and carbon emissions accurately and providing the necessary prerequisites for achieving low carbon.

Under fierce market competition and the demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emissions for better development. In order to simultaneously consider cost, customer satisfaction, and carbon emissions in the cold chain logistics path optimization problem, based on the idea of cost–benefit, this paper proposes a comprehensive cold chain vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer. For customer satisfaction, this paper uses the punctuality of delivery as the evaluation standard. For carbon emissions, this paper introduces the carbon trading mechanism to calculate carbon emissions costs. An actual case data is used with a cycle evolutionary genetic algorithm to carry out computational experiments in the model. First, the effectiveness of the algorithm and model were verified by a numerical comparison experiment. The optimization results of the model show that increasing the total cost by a small amount can greatly improve average customer satisfaction, thereby obtaining a highly cost-effective solution. Second, the impact of carbon price on total costs, carbon emissions, and average customer satisfaction have also been numerically analyzed. The experimental results show that as carbon price increases, there are two opposite trends in total costs, depending on whether carbon quota is sufficient. Increasing carbon price within a certain range can effectively reduce carbon emissions, but at the same time it will reduce average customer satisfaction to a certain extent; there is a trade-off between carbon emissions and customer satisfaction. This model enriches the optimization research of cold chain logistics distribution, and the study results complement the impact research of carbon price on carbon emissions and customer satisfaction. Finally, some practical managerial implications for enterprises and government are offered.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Vehicle routing problem in cold Chain logistics: A joint distribution model with carbon trading mechanisms

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

Taking Stock of Strategies on Climate Change and the Way Forward: A Strategic Climate Change Framework for Australia

BackgroundClimate impacts are rarely considered in health impact and economic assessments of public health programs. This study estimates the greenhouse gas (GHG) emissions averted by a novel oral SARS-CoV-2 (COVID-19) vaccine compared with four existing intramuscular vaccines: AstraZeneca's COVISHIELD®, Pfizer/BioNTech's COMIRNATY®, Moderna's mRNA-1273, and Johnson & Johnson's Ad26.COV2.S COVID-19 vaccine.MethodsWe estimated GHG emissions averted for five vaccine modalities across nine countries. GHG emissions averted were derived from differences in cold chain logistics, production of vaccine supplies, and medical waste disposal. Countryspecific data including population coverage and electricity production mix were included in GHG emissions calculations. Results are presented in averted GHG per vaccine course and country level based on modeled vaccination demand.FindingsPer course, an oral vaccine is estimated to avert between 0.007 and 0.024 kgCO2e compared with Johnson & Johnson, 0.013 to 0.048 kgCO2e compared with AstraZeneca, 0.23 to 0.108 kgCO2e compared with Moderna, and 0.134 to 0.466 kgCO2e compared with Pfizer/BioNTech. The total GHG averted varied across countries based upon predicted demand, mix of electrical production, and vaccination strategy with the largest emissions reductions projected for India and the United States.InterpretationOur results demonstrate large potential GHG emissions reductions from the use of oral vs. intramuscular vaccines for mass COVID-19 vaccination programs. Up to 82.25 million kgCO2e could be averted from utilization of an oral vaccine in the United States alone, which is equivalent to eliminating 17,700 automobiles from the road for one year.FundingFunding was provided by Vaxart, Inc. Vaxart, Inc. is currently developing an oral COVID-19 vaccine, the characteristics of which were utilized to define the thermostable oral vaccine discussed in this study. Apart from providing data on the characteristics of the oral vaccine under development, the funders had no influence over the study design, methods, statistical analyses, results, framing of results, decision to submit the manuscript for publication, or choice of journal.

BackgroundPolicies to mitigate climate change by reducing greenhouse gas (GHG) emissions can yield public health benefits by also reducing emissions of hazardous co-pollutants, such as air toxics and particulate matter. Socioeconomically disadvantaged communities are typically disproportionately exposed to air pollutants, and therefore climate policy could also potentially reduce these environmental inequities. We sought to explore potential social disparities in GHG and co-pollutant emissions under an existing carbon trading program—the dominant approach to GHG regulation in the US and globally.Methods and findingsWe examined the relationship between multiple measures of neighborhood disadvantage and the location of GHG and co-pollutant emissions from facilities regulated under California’s cap-and-trade program—the world’s fourth largest operational carbon trading program. We examined temporal patterns in annual average emissions of GHGs, particulate matter (PM2.5), nitrogen oxides, sulfur oxides, volatile organic compounds, and air toxics before (January 1, 2011–December 31, 2012) and after (January 1, 2013–December 31, 2015) the initiation of carbon trading. We found that facilities regulated under California’s cap-and-trade program are disproportionately located in economically disadvantaged neighborhoods with higher proportions of residents of color, and that the quantities of co-pollutant emissions from these facilities were correlated with GHG emissions through time. Moreover, the majority (52%) of regulated facilities reported higher annual average local (in-state) GHG emissions since the initiation of trading. Neighborhoods that experienced increases in annual average GHG and co-pollutant emissions from regulated facilities nearby after trading began had higher proportions of people of color and poor, less educated, and linguistically isolated residents, compared to neighborhoods that experienced decreases in GHGs. These study results reflect preliminary emissions and social equity patterns of the first 3 years of California’s cap-and-trade program for which data are available. Due to data limitations, this analysis did not assess the emissions and equity implications of GHG reductions from transportation-related emission sources. Future emission patterns may shift, due to changes in industrial production decisions and policy initiatives that further incentivize local GHG and co-pollutant reductions in disadvantaged communities.ConclusionsTo our knowledge, this is the first study to examine social disparities in GHG and co-pollutant emissions under an existing carbon trading program. Our results indicate that, thus far, California’s cap-and-trade program has not yielded improvements in environmental equity with respect to health-damaging co-pollutant emissions. This could change, however, as the cap on GHG emissions is gradually lowered in the future. The incorporation of additional policy and regulatory elements that incentivize more local emission reductions in disadvantaged communities could enhance the local air quality and environmental equity benefits of California’s climate change mitigation efforts.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

This paper presents the comparison of pickup and delivery with time window (PDPTW) and green vehicle routing for pickup and delivery problems, with time windows (Green-PDPTW) by using differential evolution (DE) algorithm. The main idea of PDPTW is to design the optimal route for transportation by minimising the total cost. Green-PDPTW aims to design the route by minimising the emission of direct greenhouse gases, i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). These two concepts were verified by eight standard benchmark instances. DE algorithm is proposed to design the optimal route for these two problems. The computational experiments demonstrate that designing route by minimising greenhouse gases emission provides cleaner routes than designing routes by minimising total cost. However, it is not as economical as considering the minimum total cost as the objective function since it requires more vehicles and total distance than route that designed based on the minimum total cost concept.

This paper presents the comparison of pickup and delivery with time window (PDPTW) and green vehicle routing for pickup and delivery problems, with time windows (Green-PDPTW) by using differential evolution (DE) algorithm. The main idea of PDPTW is to design the optimal route for transportation by minimising the total cost. Green-PDPTW aims to design the route by minimising the emission of direct greenhouse gases, i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). These two concepts were verified by eight standard benchmark instances. DE algorithm is proposed to design the optimal route for these two problems. The computational experiments demonstrate that designing route by minimising greenhouse gases emission provides cleaner routes than designing routes by minimising total cost. However, it is not as economical as considering the minimum total cost as the objective function since it requires more vehicles and total distance than route that designed based on the minimum total cost concept.