20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis
Abstract
:1. Introduction
2. Methodology
3. Results and Discussion
3.1. Data Level: Sources
3.2. Data Level: Authors
3.3. Data Level: Documents
Authors | Publication Title | Local Citations | Link Strength |
---|---|---|---|
Kennedy and Eberhart, 1995 [15] | Particle swarm optimization | 22 | 29 |
Dantzig and Ramser, 1959 [3] | The truck dispatching problem | 27 | 25 |
Gendreau et al., 1994 [48] | A Tabu search heuristic for the vehicle routing problem | 5 | 13 |
Lin, 1965 [49] | Computer solutions of the traveling salesman problem | 5 | 12 |
Rochat and Taillard, 1995 [50] | Probabilistic diversification and intensification in local search for vehicle routing | 5 | 11 |
Solomon, 1987 [47] | Algorithms for the vehicle routing and scheduling problems with time window constraints | 14 | 11 |
Fisher and Jaikumar, 1981 [46] | A generalized assignment heuristic for vehicle routing | 6 | 10 |
Clerc, 2000 [45] | Discrete particle swarm optimization illustrated by the traveling salesman problem | 10 | 9 |
Ai and Kachitvichyanukul, 2009 [22] | A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery | 17 | 8 |
Chen et al., 2006 [43] | Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem | 8 | 7 |
Norouzi et al., 2015 [33] | Evaluating of the particle swarm optimization in a periodic vehicle routing problem | 7 | 3 |
Dethloff, 2001 [51] | Vehicle routing and reverse logistics: the vehicle routing problem with simultaneous delivery and pick-up | 5 | 2 |
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Author | PSO-Exclusive | Literature Survey Type | Database Analysis | Bibliometric Study | Main Contribution |
---|---|---|---|---|---|
Ferruci [8] | Selective | VRP problem classification | |||
Vidal et al. [10] | Selective | √ | Heuristics survey | ||
Lahyani et al. [9] | Selective | √ | √ | VRP variant classification | |
Marinakis et al. [7] | √ | Selective | Novel variants survey | ||
This survey | √ | Comprehensive | √ | √ | Metadata-focused survey |
Database | Query | Publications |
---|---|---|
Scopus | AUTHKEY((“particle swarm optimization” OR “PSO”) AND (“vehicle routing” OR “vehicle routing problem” OR “vehicle route problem” OR “vehicle route” OR “VRP” OR “inventory routing” OR “inventory routing problem” OR “inventory route problem” OR “inventory route” OR “IRP”)) | 263 |
Web of Science | AK = ((“particle swarm optimization” OR “PSO”) AND (“vehicle routing” OR “vehicle routing problem” OR “vehicle route problem” OR “vehicle route” OR “VRP” OR “inventory routing” OR “inventory routing problem” OR “inventory route problem” OR “inventory route” OR “IRP”)) | 81 |
Level of Analysis | Metrics |
---|---|
Sources | Bradford’s Law, H-index, source dynamics, most relevant sources |
Authors | Lotka’s Law, fractional authorship, most relevant affiliations, countries |
Documents | Most cited documents (global and local), bibliographic coupling, co-citation, keywords evolution |
Description | Results | |
---|---|---|
Scopus Database | Web of Science Database | |
Main information about the data | ||
Timespan | 2004–2022 | 2004–2022 |
Sources | 181 | 49 |
Documents | 263 | 82 |
Average years since publishing | 8.13 | 6.53 |
Average citations per document | 17.54 | 36.94 |
References | 6463 | 2521 |
Annual Growth Rate | 6.29% | 0% |
Document types | ||
Article | 139 | 80 |
Review | 0 | 1 |
Book chapter | 4 | 0 |
Conference paper | 120 | 0 |
Document contents | ||
Keywords Plus (ID) | 1302 | 146 |
Author’s Keywords | 546 | 249 |
Authors | ||
Authors | 574 | 227 |
Authors of single-authored documents | 16 | 2 |
Authors’ collaboration | ||
Single-authored documents | 19 | 2 |
Co-authors per document | 3.06 | 3.32 |
Journal Source | Rank | Frequency |
---|---|---|
Computers & Industrial Engineering | 1 | 8 |
Expert Systems with Applications | 2 | 8 |
Applied Soft Computing | 3 | 5 |
Engineering Applications of Artificial Intelligence | 4 | 3 |
Journal of Intelligent Manufacturing | 5 | 3 |
Transportation Research Part E-Logistics and Transportation Review | 6 | 3 |
Annals of Operations Research | 7 | 2 |
IEEE Transactions on Intelligent Transportation Systems | 8 | 2 |
International Journal of Advanced Manufacturing Technology | 9 | 2 |
International Journal of Production Research | 10 | 2 |
Authors | Publication Title | Local Citations | Link Strength |
---|---|---|---|
Marinakis and Marinaki, 2010b [30] | A hybrid genetic—Particle Swarm Optimization Algorithm for the vehicle routing problem | 178 | 199 |
Marinakis et al., 2010a [31] | A hybrid particle swarm optimization algorithm for the vehicle routing problem | 141 | 191 |
Potvin, 2009 [36] | State-of-the art review: Evolutionary algorithms for vehicle routing | 71 | 145 |
Ahmed et al., 2018 [21] | Bilayer local search enhanced particle swarm optimization for the capacitated vehicle routing problem | 11 | 131 |
Marinakis et al., 2013b [32] | Particle Swarm Optimization for the vehicle routing problem with stochastic demands | 134 | 102 |
Marinakis et al., 2019 [39] | A Multi-Adaptive Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows | 71 | 97 |
Ozsoydan and Sipaphiogu, 2013 [35] | Heuristic solution approaches for the cumulative capacitated vehicle routing problem | 22 | 93 |
Jia et al., 2018 [26] | A Dynamic Logistic Dispatching System with Set-Based Particle Swarm Optimization | 47 | 91 |
Goksal et al., 2013 [40] | A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery | 187 | 91 |
Alinaghian et al., 2017 [23] | A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization | 17 | 88 |
Khouadjia et al., 2012 [27] | A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests | 102 | 83 |
Marinakis et al., 2013a [29] | Combinatorial neighborhood topology particle swarm optimization algorithm for the vehicle routing problem | 11 | 82 |
Xu et al., 2015 [38] | A Combination of genetic algorithm and particle swarm optimization for vehicle routing problem with time windows | 38 | 78 |
Norouzi et al., 2015 [33] | Evaluating of the particle swarm optimization in a periodic vehicle routing problem | 39 | 77 |
Kim and Son, 2012 [28] | A probability matrix based particle swarm optimization for the capacitated vehicle routing problem | 35 | 75 |
Wu et al., 2016 [41] | Vehicle routing problem with time windows using multi-objective co-evolutionary approach | 14 | 74 |
Kanthavel et al., 2011 [42] | Optimization of capacitated vehicle routing problem by Nested Particle Swarm Optimization | 22 | 74 |
Ai and Kachitvichyanukul, 2009 [22] | Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem | 156 | 74 |
Chen et al., 2016 [24] | The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks | 34 | 69 |
Gong et al., 2012 [25] | Optimizing the vehicle routing problem with time windows: A discrete particle swarm optimization approach | 145 | 69 |
Khouadjia et al., 2010 [27] | Adaptive particle swarm for solving the dynamic vehicle routing problem | 15 | 69 |
Chen and Shi, 2019 [43] | Neural-like encoding particle swarm optimization for periodic vehicle routing problems | 17 | 65 |
Shi et al., 2018 [37] | Particle swarm optimization for split delivery vehicle routing problem | 13 | 59 |
Okulewicz and Mandziuk, 2017 [34] | The impact of particular components of the PSO-based algorithm solving the Dynamic Vehicle Routing Problem | 55 | 59 |
Norouzi et al., 2012 [44] | A New Multi-objective Competitive Open Vehicle Routing Problem Solved by Particle Swarm Optimization | 51 | 16 |
Authors | Publication Title | Local Citations | Link Strength |
---|---|---|---|
Marinakis and Marinaki, 2010a [30] | A hybrid genetic—Particle Swarm Optimization Algorithm for the vehicle routing problem | 144 | 190 |
Marinakis et al., 2010b [31] | A hybrid particle swarm optimization algorithm for the vehicle routing problem | 121 | 181 |
Potvin, 2009 [36] | State-of-the Art Review Evolutionary Algorithms for Vehicle Routing | 60 | 159 |
Alinaghian et al., 2017 [23] | A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization | 12 | 113 |
Marinakis et al., 2013 [32] | Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands | 114 | 111 |
Okulewicz and Mandziuk, 2017 [34] | The impact of particular components of the PSO-based algorithm solving the Dynamic Vehicle Routing Problem | 46 | 101 |
Ozsoydan and Sipahioglu, 2013 [35] | Heuristic solution approaches for the cumulative capacitated vehicle routing problem | 20 | 101 |
Okulewicz and Mandziuk, 2019 [56] | A metaheuristic approach to solve Dynamic Vehicle Routing Problem in continuous search space | 20 | 97 |
Goksal et al., 2013 [40] | A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery | 154 | 94 |
Wu et al., 2016 [41] | Vehicle routing problem with time windows using multi-objective co-evolutionary approach | 15 | 94 |
Marinakis et al., 2019 [39] | A multi-adaptive particle swarm optimization for the vehicle routing problem with time windows | 62 | 93 |
Khouadjia et al., 2012 [27] | A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests | 83 | 90 |
Jia et al., 2018 [26] | A Dynamic Logistic Dispatching System With Set-Based Particle Swarm Optimization | 43 | 87 |
Moghaddam et al., 2012 [57] | Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm | 63 | 83 |
Xu et al., 2015 [38] | A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows | 25 | 82 |
Chen et al., 2016 [53] | The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks | 27 | 79 |
Norouzi et al., 2012 [44] | A New Multi-objective Competitive Open Vehicle Routing Problem Solved by Particle Swarm Optimization | 42 | 79 |
Belmecheri et al., 2013 [52] | Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows | 58 | 79 |
Norouzi et al., 2015 [33] | Evaluating of the particle swarm optimization in a periodic vehicle routing problem | 32 | 78 |
Kim and Son, 2012 [28] | A probability matrix based particle swarm optimization for the capacitated vehicle routing problem | 25 | 76 |
Chen and Shi, 2019 [53] | Neural-like encoding particle swarm optimization for periodic vehicle routing problems | 15 | 75 |
Ai and Kachitvichyanukul, 2009 [58] | Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem | 110 | 74 |
Gong et al., 2012 [25] | Optimizing the Vehicle Routing Problem With Time Windows: A Discrete Particle Swarm Optimization Approach | 112 | 70 |
Naderipour and Alinaghian, 2016 [54] | Measurement, evaluation and minimization of CO2, NOx, and CO emissions in the open time dependent vehicle routing problem | 32 | 65 |
Norouzi et al., 2017 [55] | Modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption | 43 | 61 |
Authors | Publication Title | Local Citations | Link Strength |
---|---|---|---|
Kennedy and Eberhart, 1995 [15] | Particle swarm optimization | 49 | 137 |
Dantzig and Ramzer, 1959 [3] | The Truck Dispatching Problem | 33 | 101 |
Ai and Kachitvichanukul, 2009 [22] | A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery | 30 | 99 |
Solomon, 1987 [47] | Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints | 18 | 52 |
Kennedy et al., 2001 [15] | Swarm Intelligence, ISBN 9781558605954 | 17 | 51 |
Ai and Kachitvichyanukul, 2009 [58] | Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem | 14 | 49 |
Marinakis and Marinaki, 2010 [31] | A hybrid particle swarm optimization algorithm for the vehicle routing problem | 13 | 49 |
Clarke, 1964 [59] | Scheduling of Vehicles from a Central Depot to a Number of Delivery Points | 11 | 42 |
Marinakis and Marinaki, 2010 [30] | A hybrid genetic—Particle Swarm Optimization Algorithm for the vehicle routing problem | 12 | 39 |
Marinakis et al., 2013 [32] | Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands | 10 | 33 |
Shi and Eberhart, 1998 [60] | A modified particle swarm optimizer | 10 | 33 |
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Reong, S.; Wee, H.-M.; Hsiao, Y.-L. 20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis. Mathematics 2022, 10, 3669. https://doi.org/10.3390/math10193669
Reong S, Wee H-M, Hsiao Y-L. 20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis. Mathematics. 2022; 10(19):3669. https://doi.org/10.3390/math10193669
Chicago/Turabian StyleReong, Samuel, Hui-Ming Wee, and Yu-Lin Hsiao. 2022. "20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis" Mathematics 10, no. 19: 3669. https://doi.org/10.3390/math10193669
APA StyleReong, S., Wee, H.-M., & Hsiao, Y.-L. (2022). 20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis. Mathematics, 10(19), 3669. https://doi.org/10.3390/math10193669