A Decision Support System for a Long-distance Routing Problem based on the Ant Colony Optimization Metaheuristic

Sicilia, Juan Antonio; Royo, Beatriz; Larrodé, Emilio; Fraile, Alberto

doi:10.1016/j.sbspro.2014.01.138

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…The requirements exist not only in the city, but also between cities and between countries. Researchers [13][14][15][16] worked on case studies in long-haul transportation and published their research works. They announce that their works can reduce the costs of transportation for logistics operators and companies significantly.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Kantawong

Pravesjit

2020

ECTI-CIT

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This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

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Section: Literature Reviewmentioning

confidence: 99%

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Kantawong

Pravesjit

2020

ECTI-CIT

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“…e experimental results showed that the above model could fully utilise the load and volume of the loading tools in a balanced manner. Sicilia et al [21] used an ant colony algorithm to solve a biobjective bulk cargo loading model and verified that the proposed model and algorithm were feasible through experiments.…”

Section: Introductionmentioning

confidence: 96%

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Wei

2022

Scientific Programming

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In logistics distribution systems, the constrained optimisation of the cargo dispensing problem has been the focus of research in related fields. At present, many scholars try to solve the problem by introducing swarm intelligence algorithms, including genetic algorithm, particle swarm algorithm, bee swarm algorithm, fish swarm algorithm, etc. Each swarm intelligence algorithm has different characteristics, but they all have certain advantages for the optimisation of complex problems. In recent years, the Wolf Pack algorithm, an emerging swarm intelligence algorithm, has shown good global convergence and computational robustness in solving complex high-dimensional functions. Therefore, this article chooses to use the Wolf Pack algorithm to solve a multi-vehicle and multi-goods dispensing problem model. First, the principle and process of the Wolf Pack algorithm are introduced, and two improvements are proposed for the way of location update and the way of step update. Then, a mathematical model of the multi-vehicle and multi-goods dispensing problem is developed. Next, the mathematical model is solved using the proposed improved Wolf Pack algorithm. The experimental results show that the proposed improved Wolf Pack algorithm effectively solves the cargo dispatching problem. In addition, the proposed improved Wolf Pack algorithm can effectively reduce the number of vehicles to be dispatched compared with other swarm intelligence algorithms.

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“…Some of these techniques are the genetic algorithm (Jassbi and Makvandi 2010 ), mixed-integer linear programming (MILP) approach (Tiwari et al 2013 ), artificial neural network (ANN) theory (Qu and Chen, 2008), and the Grey Relational Analysis method (Yu et al 2005 ). Some papers also examined the route selection problems without using a mathematical model (Huynh and Fotuhi 2013 ; Bookbinder and Fox 1998 ; Marín-Tordera et al 2006 ; Kaewfak and Ammarapala 2018 ; Boardman et al 1997 ; Pham et al 2018 ; Sicilia et al 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets

2022

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The selection of a proper international freight transport route is one of the crucial tasks for decision-makers since it can affect costs, efficiency, and transportation performance. Besides, the selection of suitable and appropriate freight routes can also reduce external costs of transportation such as emissions, noise, traffic congestions, accidents, and so on. Route selection in international transportation is a complicated decision-making problem as many conflicting factors and criteria affect the assessment process. It has been observed that there is no mathematical model and methodological frame used for solving these selection problems, and decision-makers make decisions on this issue based on their own experiences and verbal judgments in the research process. Therefore, a methodological frame is required to make rational, realistic, and optimal decisions on route selection. From this perspective, the current paper proposes using the IVAIF CODAS, an extended version of the traditional CODAS techniques, and using the Atanassov interval-valued intuitionistic fuzzy sets (IVAIFS) for processing better the existing uncertainties. The proposed model is applied to solve the route selection, a real-life decision-making problem encountered in international transportation between EU countries and Turkey. According to the results of the analysis, option A6 (i.e., Route-6 (Bursa–Istanbul–Pendik–Trieste (Ro-Ro)–Austria–Frankfurt/Germany) has been determined as the best alternative. These obtained results have been approved by a comprehensive sensitivity analysis performed by using different MCDM techniques based on interval-valued intuitionistic fuzzy sets. Hence, it can be accepted that the proposed model is an applicable, robust, and powerful mathematical tool; also, it can provide very reliable, accurate, and reasonable results. As a result, the proposed model can provide a more flexible and effective decision-making environment as well as it can provide valuable advantages to the logistics and transport companies for carrying out practical, productive, and lower cost logistics operations.

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A Decision Support System for a Long-distance Routing Problem based on the Ant Colony Optimization Metaheuristic

Cited by 11 publications

References 9 publications

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets

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