Optimizing Fresh Logistics Distribution Route Based on Improved Ant Colony燗lgorithm

Wu, Daqing; Zhu, Ziwei; Hu, Dong; Mansour, Romany F.

doi:10.32604/cmc.2022.027794

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…(2021) adopted above approach, where the environment cost was obtained through the unit price and carbon emission determined based on emission factor. At present, this method for calculating carbon emission cost has become the research mainstream (Chen et al ., 2019; Wu et al ., 2022). Although considering carbon emission cost in the objective function reduces carbon emission, it does not address the carbon emission at the source (Li et al ., 2020c).…”

Section: Literature Reviewmentioning

confidence: 99%

An electric vehicle routing model with charging stations consideration for sustainable logistics

Li,

Lim,

Xiong

et al. 2023

IMDS

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PurposeRecently, 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.Design/methodology/approachThis 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.FindingsThe 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.Research limitations/implicationsThe 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.Originality/valueIn 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.

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Section: Literature Reviewmentioning

confidence: 99%

An electric vehicle routing model with charging stations consideration for sustainable logistics

Li,

Lim,

Xiong

et al. 2023

IMDS

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“…This is consistent with the focus of this study, which is to use IoT technology to optimize logistics distribution routes. In terms of using specific algorithms for route optimization, Wu et al, [5] and Liu [6] used improved ant colony algorithm and particle swarm optimization algorithm respectively to prove the effectiveness of these algorithms in optimizing logistics distribution routes. These studies show that the efficiency and accuracy of logistics distribution routes can be significantly improved by adopting different algorithms and technologies.…”

Section: Introductionmentioning

confidence: 99%

Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment

Liu

2024

Decis. Mak. Appl. Manag. Eng.

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With the increasing challenges facing the logistics industry, especially in meeting the growing demand for distribution efficiency and accuracy, the use of modern technology to optimize logistics distribution routes has become a key issue. This study explores the application of artificial intelligence (AI) and Internet of Things (IoT) technologies in the optimization of logistics distribution routes. The research first focused on collecting and processing logistics-related data, including historical delivery records, real-time traffic data, and cargo tracking information. Then, by building optimization models based on AI and IoT technologies, the study explores the potential of these technologies to improve logistics distribution efficiency and reduce costs. In addition, through cost-benefit analysis and discussion of challenge-coping strategies, this study not only verified the effectiveness of the proposed scheme in theory but also demonstrated its feasibility in practical application. Finally, the study presents implications for industry practice and recommendations for future research, emphasizing the importance of continuous technology evaluation and adaptation to market changes.

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“…Logistics distribution methods based on exact optimisation algorithms can fall into dimensional explosion, making it difficult for the algorithm to be met with a satisfactory solution in a short period of time [8]. Logistics path optimisation methods based on heuristic algorithms converge quickly and are easy to implement, but they are also prone to the local optimum problem [9]. Literature [10] combines cuckoo algorithm and intelligent water droplet algorithm to improve the ability to solve the logistics vehicle path planning problem; Literature [11] proposes an improved ant colony algorithm for the multiwarehouse green vehicle planning problem by comprehensively considering the cost of economy and environmental pollution; Literature [12] introduces a pheromone oscillation process to transform the firefly algorithm and applies it to the logistics vehicle planning problem; Literature [13] proposes a logistics vehicle planning problem with time windows based on improved particle swarm algorithm considering distribution and recovery costs; literature [14] designed a joint adaptive large-scale optimization algorithm and solved the timedependent logistics vehicle problem under fuzzy demand; literature [15] proposed a stochastic logistics and distribution vehicle method for multi-centre demand based on neighbourhood searcher strategy to improve the cultural gene algorithm; literature [16] combined the scanning algorithm and improved particle swarm algorithm to propose a time-uncertain logistics distribution vehicle path optimisation method.…”

Section: Introductionmentioning

confidence: 99%

Cold Chain Low-carbon Logistics Path Optimisation Method Based on Improved Hummingbird Optimisation Algorithm

Long

2024

EAI Endorsed Trans Energy Web

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INTRODUCTION:The research of scientific and reasonable logistics and distribution programme time the pursuit of each logistics enterprise, not only can improve customer satisfaction and corporate image, but also help to reduce distribution costs. OBJECTIVES: For the current cold chain low-carbon logistics distribution path optimisation methods there are problems such as easy to fall into the local optimum, optimisation time-consuming. METHODS: This paper proposes a cold chain low-carbon logistics distribution path optimisation method based on the improved Hummingbird optimisation algorithm. Firstly, by analyzing the characteristics of the cold chain low-carbon logistics distribution path optimization problem, designing the cold chain low-carbon logistics path optimization objective function and constraints, and constructing a cold chain low-carbon logistics distribution path optimization model based on a soft time window; then, the hummingbird optimization algorithm is improved by using the initialization strategy of the set of good points and the cardinality leap strategy, to overcome the defects of the hummingbird optimization algorithm; secondly, a method based on intelligent optimization algorithm is proposed by designing the double-layer array coding and the adaptive function, combined with the improved hummingbird optimization algorithm. A cold chain low-carbon logistics path optimization method based on intelligent optimization algorithm is proposed; finally, the superiority and robustness of the proposed method are verified by simulation experimental analysis. RESULTS: The results show that the proposed method not only improves the optimisation time, but also increases the optimisation fitness value. CONCLUSION: This paper solves the problem that the optimisation of the green low-carbon logistics path optimisation problem is time-consuming and prone to falling into local optimum.

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Optimizing Fresh Logistics Distribution Route Based on Improved Ant Colony燗lgorithm

Cited by 13 publications

References 22 publications

An electric vehicle routing model with charging stations consideration for sustainable logistics

An electric vehicle routing model with charging stations consideration for sustainable logistics

Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment

Cold Chain Low-carbon Logistics Path Optimisation Method Based on Improved Hummingbird Optimisation Algorithm

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