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

Yan, Li; Lim, Ming K.; Tseng, Ming‐Lang

doi:10.1108/imds-07-2018-0314

Cited by 102 publications

(104 citation statements)

References 38 publications

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“…These factors are used to optimize the route plan and experimental evaluation of the proposed method is performed. Li et al [26] propose the mathematical optimization model that illustrates how to reduce overall costs, including vehicle operating costs, cost of quality loss, product freshness, cost of delay, energy costs, and greenhouse gas emissions. It also calculates the impact of maximum vehicle load changes on costs and greenhouse gas emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

The Model of Vehicle and Route Selection for Energy Saving

2021

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The World Bank, United Nations, the Organization for Economic Cooperation and Development, and others are in line with the governments of countries that are strongly interested in the sustainable development of countries, regions, and enterprises. One of the aspects that affects the indicators and prospects of sustainable development is the efficiency of energy source use. Nationwide reductions in the greenhouse gas emissions of motor vehicles could have a direct effect on ambient temperature and reducing the effects of global warming, which can affect future environmental, societal, and economic development. Significant reductions in fuel consumption can be achieved by increasing the efficiency of use, and the performance, of current cargo vehicles. This aspect is directly related to cargo delivery systems and supply chain efficiency and effectiveness. The article solves the problem of increasing the effectiveness of cargo delivery and proposes a model that would minimize transportation costs that are directly related to fuel consumption, shortening transportation time. The model addresses the problem of a lack of models evaluating the efficiency of cargo to Lithuania that is using several different modes of transportation. For the solution to this problem, the article examines the complexity of the rational use of land and water vehicles depending on the type of cargo transported, the technical capabilities of the vehicles (loading, speed, environmental pollution, fuel consumption, etc.), and the type (cars, railways, ships). The novelty of the findings is based on the availability to select the most appropriate vehicles, on a case-by-case basis, from the available options, depending on their environmental performance and energy efficiency. This model, later in this article, is used for calculations of Lithuanian companies for selecting the most rational vehicle by identifying the most appropriate route, as well as assessing the dynamics of the economic and physical indicators. The model allows for creating dependencies between the main indicators characterizing the transport process—the cost, the time of transport, and the safety, taking into account the dynamics of economic and physical indicators, that lead to a very important issue—reducing the amount of energy required to provide products and services.

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Section: Literature Reviewmentioning

confidence: 99%

The Model of Vehicle and Route Selection for Energy Saving

2021

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“…Several researchers have set their sights on reducing GHG generated from vehicle transporting and distribution process. Li et al [26] took the full set of GHG emissions composed of CO2, CH4 and N2O into account when modeling the green cold chain VRP. Kazemian et al developed a novel model for capacitated VRP model with time-dependent speed limits aiming to minimize two green objectives, total fuel consumption and GHG emission [27].…”

Section: Vehicle Routing Problem With Environmental Concernsmentioning

confidence: 99%

“…Constraint (25) ensures each vehicle's uploading quantity meets its visited customers' demands. Constraint (26) guarantees that traveling among depots is not allowed, respectively. Constraints (27) and (28) indicate products' balancing flow between two echelon layers.…”

Section: Model Establishmentmentioning

confidence: 99%

Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window

Wang

Wen

2020

Sustainability

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Due to the rise of social and environmental concerns on global climate change, developing the low-carbon economy is a necessary strategic step to respond to greenhouse effect and incorporate sustainability. As such, there is a new trend for the cold chain industry to establish the low-carbon vehicle routing optimization model which takes costs and carbon emissions as the measurements of performance. This paper studies a low-carbon vehicle routing problem (LC-VRP) derived from a real cold chain logistics network with several practical constraints, which also takes customer satisfaction into account. A low-carbon two-echelon heterogeneous-fleet vehicle routing problem (LC-2EHVRP) model for cold chain third-party logistics servers (3PL) with mixed time window under a carbon trading policy is constructed in this paper and aims at minimizing costs, carbon emissions and maximizing total customer satisfaction simultaneously. To find the optimal solution of such a nondeterministic polynomial (NP) hard problem, we proposed an adaptive genetic algorithm (AGA) approach validated by a numerical benchmark test. Furthermore, a real cold chain case study is presented to demonstrate the influence of the mixed time window’s changing which affect customers’ final satisfaction and the carbon trading settings on LC-2EHVRP model. Experiment of LC-2EHVRP model without customer satisfaction consideration is also designed as a control group. Results show that customer satisfaction is a critical influencer for companies to plan multi-echelon vehicle routing strategy, and current modest carbon price and trading quota settings in China have only a minimal effect on emissions’ control. Several managerial suggestions are given to cold chain logistics enterprises, governments, and even consumers to help improve the development of cold chain logistics.

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“…To respect the time windows associated to each node, the arrival time at node i must not be lower than ETi, otherwise a waiting time will be calculated by equation (12). And if the departure time is higher than LTi, a tardiness time will be calculated by equation (13):…”

Section: Mathematical Modelmentioning

confidence: 99%

“…For the optimization of the total cost, (Li et al, 2019) proposed an algorithm based on the PSO for solving the problem of ecological vehicle routing. The cost function integrated various costs, namely, penalty cost, energy cost, GHG emissions costs and others.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Dridi¹,

Alaïa²,

Borne³

et al. 2019

SIC

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This paper presents a variant of the vehicle routing problem (VRP) that combines several constraints. This variant addresses the pickup and delivery problem (PDP), the use of multiple vehicles (m), the multi-depots (MD) and the time constraint (TW): m-MDPDPTW. In the m-MDPDPTW, one should build a route made up of several pairs (customer / supplier), which starts and ends at the same depot and respects the precedence and capacity constraints. For solving this problem, two approaches based on particle swarms are proposed with a view to minimizing the total distance travelled by all vehicles. The results yielded by these two algorithms, the continuous PSO and the discrete PSO, are then compared by making use of the benchmarks generated by Li and Lim.

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

Cited by 102 publications

References 38 publications

The Model of Vehicle and Route Selection for Energy Saving

The Model of Vehicle and Route Selection for Energy Saving

Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

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