Optimal location of logistics distribution centres with swarm intelligent clustering algorithms

Lin, Tsung-Xian; Wu, Zhong-Huan; Pan, Wen-Tsao

doi:10.1371/journal.pone.0271928

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…If we make c ¼ TÀ EðTÞ ffi ffi ffi ffi ffi ffiffi We assume that the probability density function of ψ is F(ψ). If constraint ( 14) holds at confidence level α, it follows from Eqs ( 18) and ( 19) that constraint (14) can be transformed into constraint (20) when and only when a = b. If constraint (14) holds at confidence level a, from constraints (18) and (19), when and only when FðcÞ 14) can be transformed into constraint (20).…”

Section: Stochastic Constraint Transformationmentioning

confidence: 99%

“…Mathematical planners, including LINGO, CPLEX software, etc., have significant advantages in solving small-scale optimization problems; however, when the optimization problem is large in size, the mathematical planners are unable to obtain the appropriate solution in the specified time. On the other hand, metaheuristic algorithms such as genetic algorithm (GA) [ 18 ], PSO algorithm [ 19 ], hybrid clustered ant colony algorithm (ACO K-means) [ 20 ], and sorting-based heuristics [ 21 ] are prone to local optimality.…”

Section: Introductionmentioning

confidence: 99%

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Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty

Zhu,

Fan,

Yin

2024

PLoS ONE

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As China’s "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0–1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency.

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Section: Stochastic Constraint Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty

Zhu,

Fan,

Yin

2024

PLoS ONE

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“…Results verified the superiority of this approach over fuzzy C-means, and K-means methods using GPS datasets from Aracaju, Beijing, Chongqing, Rome, and San Francisco. Additionally, Lin, Wu, and Pan used Fruit-Fly optimized K-means algorithm to boost distribution efficiency and the location solution for logistics distribution centers [34]. Tabianan, Velu, and Ravi used K-Means clustering to boost corporate profits and avoid customer churn based on shared behavioral traits [35].…”

Section: Related Workmentioning

confidence: 99%

Identification of Location for Police Headquarters to Deal with Crime Against Women in India Using Clustering Based on K-Means Algorithm

Indu,

Dimri,

Kumar

2023

IJCDS

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During 2020-21, the rate of crime against women has considerably increased, especially the cases of domestic violence, kidnapping, and rape. Considering the fact that a woman better understands the problem of another woman, and to ensure women's safety in this era of technology, we proposed opening Police Headquarters with lady staff only. For this, we suggest the use of the latitude, longitude, and average weighted crime as clustering parameters for the K-means method to recognize the most probable location at which such headquarters can be opened. The parameter average weighted crime signifies the magnitude or intensity of nine different possible crimes against women and thus identifies the location where the maximum number of crimes against women are reported. Therefore, such locations are ideal for setting up the Lady Police Headquarters to further curtail any such crimes, and deliver swift protection as well as justice to the victim women. It is somehow similar to identifying clusters (locality) from clusters (blocks). Thence, using the suggested approach, the Indian law enforcing agencies including police can prevent and control crimes at the micro level and can also increase their reach.

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“…Artificial bee colony [16] was also adopted as a clustering algorithm to minimize the execution time and to optimize the clustering according to dataset size. Lin et al [17] proposed a new algorithm for the clustering problem, the fruit-fly optimization k-means algorithm and designed a distribution centre location problem and three clustering indicators to evaluate the performance of the algorithm. Tawhid et al [18] developed a new hybrid swarm intelligence optimization algorithm called monarch butterfly optimization algorithm with cuckoo search algorithm for optimization problems.…”

Section: ) Clustering Algorithmsmentioning

confidence: 99%

A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine

Shi,

Zhang,

et al. 2023

IEEE Access

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Syndrome types are important for diagnosis and treatment in traditional Chinese medicine. Syndrome types can be summarized by domain experts as formula clusters. In this paper, we propose seven feature models for the formula clustering problem based on categories, subcategories, functional tendencies and names of Chinese materia medica. A novel multi-objective clustering hyper-heuristic algorithm is obtained. In our proposed algorithm, 12 low-level heuristics are used for clustering solution perturbation by merging clusters, dividing clusters or moving points between clusters based on received solutions from the high-level heuristic. The high-level heuristic evaluates the received solutions from low-level heuristics, updates the solution pool, and selects initial solutions for the next iteration via roulette wheel selection on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms other clustering algorithms in most datasets. The initial number of clusters has less influence on the final clustering solutions for our proposed algorithm than for other clustering algorithms. For most datasets, the roulette wheel selection mechanism on the Pareto front shows higher convergence rates and accuracy than a random selection mechanism. Accuracy was higher for feature models based on functional tendencies than for the other feature models.

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Optimal location of logistics distribution centres with swarm intelligent clustering algorithms

Cited by 6 publications

References 37 publications

Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty

Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty

Identification of Location for Police Headquarters to Deal with Crime Against Women in India Using Clustering Based on K-Means Algorithm

A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine

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