The impact of particular components of the PSO-based algorithm solving the Dynamic Vehicle Routing Problem

Okulewicz, Michał; Mańdziuk, Jacek

doi:10.1016/j.asoc.2017.04.070

Cited by 79 publications

(30 citation statements)

References 25 publications

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“…In the literature, there are several kinds of assignment problems that are examined. For example, according to the constraints capacitated vehicle routing problem (VRP), distance constrained VRP, time window VRP, multi depot VRP, pick-up and delivery VRP, periodic VRP, stochastic VRP, time dependent VRP, according to the state of the roads symmetric or asymmetric VRP, according to routing situation open or close VRP and according to environment static and dynamic VRP [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Also, there are vehicle routing problems that contain more than one of these features.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-step Meta-heuristic Approach for a Vehicle Assignment Problem – Case from İstanbul/Turkey

Yücenur

2020

PROMET

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In this paper, a two-step meta-heuristic approach is proposed for vehicle assignment problem with geometric shape-based clustering and genetic algorithm. First, the geometric shape-based clustering method is used and then the solution of this method is given to the genetic algorithm as initial solution. The solution process is continued by genetic algorithm. There are 282 bus lines in İstanbul European side. Those buses should be assigned to six bus garages. The proposed method is used to determine the minimum distance between the bus lines and garages by assigning buses to garages. According to the computational results, the proposed algorithm has better clustering performance in terms of the distance from each bus-line start point to each bus garage in the cluster. The crossover rate changing method is also applied as a trial in order to improve the algorithm performance. Finally, the outputs that are generated by different crossover rates are compared with the results of the k-Nearest Neighbour algorithm to prove the effectiveness of the study.

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Section: Literature Reviewmentioning

confidence: 99%

Two-step Meta-heuristic Approach for a Vehicle Assignment Problem – Case from İstanbul/Turkey

Yücenur

2020

PROMET

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“…This leads to the increase in the complexity of VRP. To address such challenges, a comprehensive review of real-world applications and the state-of-the-art solution methods for dynamic VRP was studied (Wang et al, 2012;Pillac et al, 2013;Okulewicz and Mańdziuk, 2017). Many algorithms of VRP focused more on the shortest delivery distance and one-way routing optimization with the consideration of cost, energy consumption, and delivery time (Yu et al, 2009;Marinakis and Marinaki, 2010;Pavone et al, 2011).…”

Section: Logistics Transportation and Routing Optimizationmentioning

confidence: 99%

An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks

Liu

Zhang

Liu

et al. 2019

Journal of Cleaner Production

103

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Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an 'Internet of Things'-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles' utilization rate, and achieving real-time logistics services with high efficiency.

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“…Common heuristic algorithm includes ant algorithm, genetic algorithm, simulated annealing algorithm, and particle swarm optimization. [3][4][5][6] These heuristic algorithms are the natural calculation of simulating the phenomenon principle in the natural world, which seeks for the best solution or similar best solution rapidly based on the priori knowledge, and the best solution is sought within the acceptable time. Recently, there are more algorithms about the neural network, especially, the rising of deep learning provides a kind of new support in theory for solving the logistics distribution path optimization.…”

Section: Relevant Researchmentioning

confidence: 99%

“…Step 4: Use the output layer a 5 n of the DBN model as the input of the SoftMax classifier and complete the SoftMax classifier solution according to Formulas (2) and 3, and the labeled data queue (x (i) , y (j) ) is the output;…”

Section: Road Condition Prediction Algorithmmentioning

confidence: 99%

Road condition prediction and logistics distribution path optimization algorithm based on traffic big data

Liang

Jiang

et al. 2019

Journal of Algorithms & Computational Technology

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In view of the complex road conditions in today's cities, the traditional prediction methods for road conditions are not so accurate, and the optimization algorithm for the logistics distribution path is not sensitive to changes in the road conditions so that its application in an actual logistics distribution system is not effective. This article proposes a road condition prediction and logistics distribution path optimization algorithm based on traffic big data. First, it analyses the characteristics of the road condition information of traffic big data. By combining the powerful feature extraction and self-learning ability of a deep belief network, it establishes a road condition prediction model based on a deep belief network and completes the model training and verification through the learning of traffic big data. Then, it combines the road condition prediction (result) information, traffic network information, and logistics distribution information to construct the time-share weighted traffic network. It then modifies the access set and pheromone variables of the ant algorithm based on the time-share traffic network to establish the road condition prediction and logistics distribution path optimization algorithm based on traffic big data. Finally, it conducts comparative experiments with other logistics distribution path optimization algorithms. The experimental results show that the proposed algorithm is superior to other logistics distribution optimization algorithms. Therefore, this algorithm is an effective method for optimizing logistics distribution.

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The impact of particular components of the PSO-based algorithm solving the Dynamic Vehicle Routing Problem

Cited by 79 publications

References 25 publications

Two-step Meta-heuristic Approach for a Vehicle Assignment Problem – Case from İstanbul/Turkey

Two-step Meta-heuristic Approach for a Vehicle Assignment Problem – Case from İstanbul/Turkey

An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks

Road condition prediction and logistics distribution path optimization algorithm based on traffic big data

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