Search results

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

Curbing (without banning) potentially environmentally‐damaging activities that have global, rather than local, effects raises challenges analogous to those faced by a community lacking legislative powers that has to restrict access to a common pasture in order to make its use sustainable. A local community achieves autonomy in a matter such as this by consensual cooperation. In the absence of a world coercive authority, global environmental problems (in which a measure of world autonomy is needed) have to be met similarly by consensual co‐operation among governments. The conditions under which local consensual cooperation have been observed to be successful may also be relevant to global consensual cooperation. In particular there must be clear rules, and devices for interpreting them; they must be acceptable to all parties; and monitoring of compliance is crucial. Even in such cases of quasi‐voluntary compliance, graduated sanctions for infringement, or analogous arrangements, are quite likely to play a vital part. In an international regime for reducing greenhouse‐gas emissions, it is essential that rules should be devised that will appeal as fair and practically tolerable to opinion in both rich and poor countries and to both high and low per capita emitters. This will rule out a regime of uniform percentage reductions without balancing compensation. It will also rule out a regime based on equal per capita claims to engage in the restricted activity. It is desirable that the rules also act to make the allocation of the reductions in the potentially damaging activity efficient. This will favour rules under which financial signals reflecting marginal costs or benefits play some part in the allocation of any target aggregates. It will probably be essential, given prevalent views of justice and differing valuations of environmental goals between rich and poor nations, that the arrangements involve transfers of resources from richer, higher‐per‐capita polluting countries to poorer, lower per capita polluting countries. Nevertheless, reducing emissions sufficiently through a system of tradable quotas summing to the targeted total of emissions – which might seem to meet both this requirement and the need for efficient marginal incentives – has, in its simple form in which the quotas issued are proportional to countries’ populations, little chance of being acceptable to rich, high‐emitter nations. An attempt is made to explore solutions to these dilemmas, leading on from the arrangements made under the Kyoto protocol of the UN Framework Convention on Climate Change.

This 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.

This 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.

The 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.

There are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle.

Existing 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.

Due to considerable contributions of the construction industry to the global carbon emissions, a great deal of attention is placed on possible incorporation of carbon footprint minimization as an important objective in the planning of construction operations. The purpose of this paper is to present a framework to estimate and minimize the carbon emissions of the concrete placing operation through identifying the optimal number of pumps and the inter-arrival time of truck mixers.

The proposed framework integrates discrete event simulation and multi-objective optimization to estimate and minimize the carbon emission, costs and production rate of the concrete placing operation. An actual construction project is used to demonstrate the application of the proposed framework. Furthermore, a sensitivity analysis is performed to investigate the sensitivity of the results to variations in modeling parameters including the ratio of idle to non-idle emission rates of equipment and the activity duration distributions.

The results of the case study highlight that variations in the number of pumps and inter-arrival time of truck mixers significantly affect the carbon emissions, cost and production rate of the concrete placing operation. Furthermore, the results of the sensitivity analysis show that variations in the ratio of idle to non-idle emission rates for pumps and truck mixers have little effects on the selected setting for the project. This is contrary to the effect of uncertainty in the activity duration distributions, which was found to be significant.

Results of this study provide an insight into the trade-off between carbon emissions, cost and production rate of the concrete placing operation.

This paper focuses on the terms of reference of the 1997 Kyoto Protocol of the United Nations Framework Convention on Climate Change, which was drafted to support environmental sustainability through the mitigation of global warming. The paper provides information on the main features of the protocol, especially the commercial incentives for companies in industrialised countries to reduce greenhouse gas emissions. Particular attention is paid to the role of the operations manager and strategist in the selection of processes, plant and equipment to meet these commercial incentives, and the location of industrial facilities under conditions of political and economic uncertainty. The paper demonstrates the importance of the political and economic factors influencing environmental investment decisions, particularly those factors which often lie outside the usual terms of reference of operations managers.

The performance of earthmoving operations, in terms of emissions, production and cost, is dependent on many variables and has been the study of a number of publications. Such publications look at typical operation design and management, without establishing what the penalties or bonuses might be for non-standard, but still observed, practices. To fill this gap in knowledge, this paper examines alternative loading policies of zero waiting-time loading, fractional loading and double-sided loading, and compares the performance of these with standard single-sided loading.

Original recursive relationships, that are amenable to Monte Carlo simulation, are derived. Case study data are used to illustrate the emissions, production and cost penalties or bonuses.

Double-sided loading contributes the least impact to the environment and is the most cost effective. Zero waiting-time loading performs the worst in terms of environmental impact and cost. Minimizing truck waiting times through using fractional loading is generally not an attractive policy because it leads to an increase in unit emissions and unit costs. The consequences of adopting fractional loading are detailed. Optimum unit emissions and optimum unit cost are coincident with respect to fleet size for single- and double-sided loading policies. That is, by minimizing unit cost, as in traditional practice, then least impact on the environment is obtained. Not minimizing unit cost will lead to unnecessary emissions.

The results of this paper will be of interest to those designing and managing earthmoving operations.

All modeling and results presented in the paper do not exist elsewhere in the literature.

Extant studies have discussed numerous carbon reduction drivers, but there is a dearth of holistic review and understanding of the dynamic interrelationships between the drivers from a system perspective. Thus, this study aims to bridge that gap.

The study conducted a review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses and adopted interpretive structural modelling (ISM) to analyse and prioritise the drivers.

Eighteen drivers were identified and grouped into five, namely, policy instruments, bid-related, cost and risk, education and training, and reward and penalty drivers. The ISM revealed two hierarchical levels of the drivers with only higher cost of electricity/fuel on the higher level, making it the most important driver that could influence others.

The study presents an overview of decarbonisation drivers in the literature and would be of benefit to the government and stakeholders towards achieving net zero emissions in the construction industry.

The findings of the study present drivers of carbon reduction and prioritise and categorise them for tailored interventions within the construction sector. Also, it could serve as foundational knowledge for further study in the construction process decarbonisation research area.

This paper aims to calibrate carbon price trajectories that maximize social welfare where banking and borrowing rules are applied.

Typically, there has been a consensus that banking and borrowing rules within the cap-and-trade system improve social welfare. This additional flexibility can achieve compliance cost smoothing by transferring carbon permits inter-temporally; however, there is also a side effect. Regulated agents have the freedom to escape from the given emissions limit by reallocating previously granted permits.

The market system’s flexibility can cause environmental damage by deviating annual or periodic emission limits, which can invalidate the original purpose of cap-and-trade. This paper demonstrates how the socially desirable price trajectory differs from the one that favors the private sector.

Few studies have focused on the negative effects of combining the cap-and-trade with the inter-temporal regulation (banking and borrowing), which most policymakers and regulated firms can easily miss.