A three-dimensional ant colony optimization algorithm for multi-compartment vehicle routing problem considering carbon emissions

Guo, Ning; Qian, Bin; Na, Jing; Hu, Rong; Mao, Jianlin

doi:10.1016/j.asoc.2022.109326

Cited by 23 publications

(4 citation statements)

References 58 publications

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“…For example, in order to reduce environmental impact, researchers attempted to reduce greenhouse gas emissions from vehicles and promote sustainable development by considering carbon emissions in route planning. They have established mathematical models that take carbon emissions into account and have studied optimization problems using algorithms such as PSO and ACO (Li et al, 2019; Cai et al, 2021; Chen et al, 2021; Guo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Li,

Peng,

Deng

et al. 2024

DCE

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Pharmaceutical distribution routing problem is a key problem for pharmaceutical enterprises, since efficient schedules can enhance resource utilization and reduce operating costs. Meanwhile, it is a complicated combinatorial optimization problem. Existing research mainly focused on delivery route lengths or distribution costs minimization, while seldom considered customer priority and carbon emissions simultaneously. However, considering the customer priority and carbon emissions simultaneously will not only help to enhance customer satisfaction, but also help to reduce the carbon emissions. In this article, we consider the customer priority and carbon emission minimization simultaneously in the pharmaceutical distribution routing problem, the corresponding problem is named pharmaceutical distribution routing problem considering customer priority and carbon emissions. A corresponding mathematical model is formulated, the objectives of which are minimizing fixed cost, refrigeration cost, fuel consumption cost, carbon emission cost, and penalty cost for violating time windows. Moreover, a hybrid genetic algorithm (HGA) is proposed to solve the problem. The framework of the proposed HGA is genetic algorithm (GA), where an effective local search based on variable neighborhood search (VNS) is specially designed and incorporated to improve the intensification abilities. In the proposed HGA, crossover with adaptive probability and mutation with adaptive probability are utilized to enhance the algorithm performance. Finally, the proposed HGA is compared with four optimization algorithms, and experimental results have demonstrated the effectiveness of the HGA.

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Section: Introductionmentioning

confidence: 99%

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Li,

Peng,

Deng

et al. 2024

DCE

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“…Madden et al [23] built a model to estimate carbon emissions from curbside organic waste collection based on waste collection route data, which showed that curbside collection was the largest contributor to overall transport emissions. Guo, Qian, et al [24] proposed a three-dimensional ant colony optimization algorithm (TDACO) to solve the multi-compartment vehicle routing problem (MCVRP) in industries such as waste collection and incorporated carbon emissions into the state transition rules in the TDACO. Dayanara, Arvitrida, and Siswanto [25] constructed a vehicle routing optimization model with the number of waste collections, time windows, and carbon emissions as constraints.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation

Li,

Deng,

et al. 2023

Sustainability

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In recent years, the waste produced as a result of the production and consumption activities of urban residents has led to significant environmental degradation and resource wastage. This paper focuses on the research object of municipal solid waste (MSW) collection and transportation based on the concept of “sustainable development and green economy”. Firstly, this study examines the current state of urban domestic garbage collection and transportation. It analyzes the following challenges and deficiencies of the existing collection and transportation system: (1) the operating efficiency of garbage collection vehicles is low, resulting in a significant accumulation of waste on the roadside and within the community; (2) the vehicle collection and transportation routes are fixed, and there are empty vehicles running; (3) the amount of garbage on a route exceeds the vehicle’s loading capacity, which requires the vehicle to perform a second round of collection and transportation. To enhance the efficiency of urban garbage collection and transportation and minimize the collection and transportation costs, we are investigating the problem of optimizing the path for green vehicles. To comprehensively optimize the fixed cost, variable cost, and carbon emission cost incurred during vehicle operation, a vehicle routing model with time windows is established, taking into account vehicle load constraints. Carbon emission coefficient and carbon tax parameters are introduced into the model and the “fuel-carbon emission” conversion method is used to measure the carbon cost of enterprises. An improved ant colony optimization (ACO) method is proposed: (1) the introduction of a vehicle load factor improves the ant state transfer method; (2) the updated pheromone method is improved, and additional pheromone is added to both the feasible path and the path with the minimum objective function; (3) the max–min ACO algorithm is introduced to address the issue of premature convergence of the algorithm; (4) the embedding of a 2-opt algorithm further prevents the ACO algorithm from falling into the local optimum. Finally, the calculation results based on the example data demonstrate that the algorithm has a significant advantage over the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The total transportation distance determined by this algorithm is shorter than that of the GA and PSO methods, and the total cost of the scheme is 1.66% and 1.89% lower than that determined by GA and PSO, respectively. Compared to the data from the actual case, the number of vehicles required in the operation of this algorithm and model is reduced by three. Additionally, the total cost, fixed cost, and carbon emission cost incurred by the vehicles during operation were reduced by 31.2%, 60%, and 25.3% respectively. The results of this study help the station to collect and distribute waste efficiently, while also achieving the goals of energy saving, consumption reduction, and emission reduction.

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“…Hübner and Ostermeier [34] considered the easily neglected loading and unloading costs in the MCVRP and used a large neighborhood search algorithm (LNS) to solve the MCVRP with loading and unloading costs. Guo et al [35] included the cost of carbon emissions in the total transportation cost of the MCVRP and proposed a three-dimensional ant colony optimization algorithm (TDACO) to solve it. In addition, in studies on the MCVRP solution algorithm, the variable neighborhood search algorithm (VNS) also shows a strong optimization ability [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Multi-Compartment Vehicle Routing Problem Considering Traffic Congestion under the Mixed Carbon Policy

Fan,

Yao,

Yang

2023

Applied Sciences

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The use of multi-compartment vehicles (MCVs) in urban logistics distribution is increasing. However, urban traffic congestion causes high carbon emissions in the logistics distribution, resulting in unsustainable development in urban transportation. In addition, the application of the mixed carbon policy has gradually become the first choice for energy conservation and emission reduction in some countries and regions. The transportation industry is a major carbon-emitting industry, which needs to be constrained by carbon emission reduction policies. In this context, the research on the multi-compartment vehicle routing problem (MCVRP) considering traffic congestion under the mixed carbon policy is carried out. Firstly, a mathematical model of MCVRP considering traffic congestion under the mixed carbon policy is constructed. Secondly, a two-stage variable neighborhood threshold acceptance algorithm (VNS-TA) is proposed to solve the above mathematical model. Thirdly, 14 adapted standard examples of the MCVRP are used to verify the effectiveness and optimization ability of the two-stage VNS-TA algorithm. A simulation example of the MCVRP considering traffic congestion under the mixed carbon policy is used to conduct sensitivity analyses for different scenarios. Finally, the following conclusions are drawn: (1) the two-stage VNS-TA algorithm is effective and has strong optimization ability in solving the basic MCVRP, and (2) the two-stage VNS-TA algorithm can solve and optimize the MCVRP considering traffic congestion under the mixed carbon policy, which has the effects of cost saving and energy conservation and emission reduction.

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A three-dimensional ant colony optimization algorithm for multi-compartment vehicle routing problem considering carbon emissions

Cited by 23 publications

References 58 publications

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation

Multi-Compartment Vehicle Routing Problem Considering Traffic Congestion under the Mixed Carbon Policy

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