The Trucking Sector Optimization Model: A tool for predicting carrier and shipper responses to policies aiming to reduce GHG emissions

2013 ◽  
Vol 59 ◽  
pp. 85-107 ◽  
Author(s):  
Sebastian E. Guerrero ◽  
Samer M. Madanat ◽  
Robert C. Leachman
Keyword(s):  
Optimization Model ◽  
Ghg Emissions ◽  
Trucking Sector

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

2019 ◽  
Vol 119 (3) ◽  
pp. 473-494 ◽  
Author(s):  
Yan Li ◽  
Ming K. Lim ◽  
Ming-Lang Tseng
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Optimization Model ◽  
Ghg Emissions ◽  
Particle Swarm ◽  
Cold Chain ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Content Type ◽  
Modified Particle Swarm Optimization

PurposeThis paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions.Design/methodology/approachThis study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case.FindingsThe results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning.Research limitations/implicationsThere are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions.Originality/valuePrior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

scholarly journals A Concurrence Optimization Model for Low-Carbon Product Family Design and the Procurement Plan of Components under Uncertainty

2021 ◽  
Vol 13 (19) ◽  
pp. 10764
Author(s):  
Qi Wang ◽  
Peipei Qi ◽  
Shipei Li
Keyword(s):  
Optimization Model ◽  
Supplier Selection ◽  
Ghg Emissions ◽  
Product Family ◽  
Decision Variable ◽  
Low Carbon ◽  
Product Family Design ◽  
Ghg Emission ◽  
Order Allocation ◽  
Carbon Product

With the increase in pollution and people’s awareness of the environment, reducing greenhouse gas (GHG) emissions from products has attracted more and more attention. Companies and researchers are seeking appropriate methods to reduce the GHG emissions of products. Currently, product family design is widely used for meeting the diverse needs of customers. In order to reduce the GHG emission of products, some methods for low-carbon product family design have been presented in recent years. However, in the existing research, the related GHG emission data of a product family are given as crisp values, which cannot assess GHG emissions accurately. In addition, the procurement planning of components has not been fully concerned, and the supplier selection has only been considered. To this end, in this study, a concurrence optimization model was developed for the low-carbon product family design and the procurement plan of components under uncertainty. In the model, the relevant GHG emissions were considered as the uncertain number rather than the crisp value, and the uncertain GHG emissions model of the product family was established. Meanwhile, the order allocation of the supplier was considered as the decision variable in the model. To solve the uncertain optimization problem, a genetic algorithm was developed. Finally, a case study was performed to illustrate the effectiveness of the proposed approach. The results showed that the proposed model can help decision-makers to simultaneously determine the configuration of product variants, the procurement strategy of components, and the price strategies of product variants based on the objective of maximizing profit and minimizing GHG emission under uncertainty. Moreover, the concurrent optimization of low-carbon product family design and order allocation can bring the company greater profit and lower GHG emissions than just considering supplier selection in low-carbon product family design.

An optimization model of self-paced tracking

1984 ◽  
Author(s):  
M. A. Montazer ◽  
Colin G. Drury
Keyword(s):  
Optimization Model

Costs and Benefits of the Transition to Low-carbon Economyin Russia: Perspectives up to 2050

2014 ◽  
pp. 70-91 ◽  
Author(s):  
I. Bashmakov ◽  
A. Myshak
Keyword(s):  
Emission Reduction ◽  
High Probability ◽  
Ghg Emissions ◽  
Mitigation Measures ◽  
Low Carbon ◽  
Costs And Benefits ◽  
Ghg Emission ◽  
Research Groups ◽  
Emissions Mitigation ◽  
Very High

This paper investigates costs and benefits associated with low-carbon economic development pathways realization to the mid XXI century. 30 scenarios covering practically all “visions of the future” were developed by several research groups based on scenario assumptions agreed upon in advance. It is shown that with a very high probability Russian energy-related GHG emissions will reach the peak before 2050, which will be at least 11% below the 1990 emission level. The height of the peak depends on portfolio of GHG emissions mitigation measures. Efforts to keep 2050 GHG emissions 25-30% below the 1990 level bring no GDP losses. GDP impact of deep GHG emission reduction - by 50% of the 1990 level - varies from plus 4% to minus 9%. Finally, very deep GHG emission reduction - by 80% - may bring GDP losses of over 10%.

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