Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—A neuro-fuzzy and artificial bee colony approach

Pamučar, Dragan; Ljubojević, Srđan; Kostadinović, Dragan; Đorović, Boban

doi:10.1016/j.eswa.2016.08.024

Cited by 71 publications

(36 citation statements)

References 52 publications

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“…Many algorithms related to this subject have been developed [14]. Neuro-Fuzzy architecture [15][16][17][18][19], A* [20,21], D* [22] (the dynamic version of the A*), Genetic Algorithm [23,24], Dijksta's Algorithm [25], BFS [26], and DFS [27] are the methods used for path planning which consider the cost efficiency. Another method for generating a path plan using potential field functions is the APF [28,29] method.…”

Section: Literature Reviewmentioning

confidence: 99%

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Dirik

Castillo

Kocamaz

2019

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Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the desired two points. In this paper, we propose a mobile robot path planning method in the visualize plane using an overhead camera based on interval type-2 fuzzy logic (IT2FIS). We deal with a visual-servoing based technique for obstacle-free path planning. It is necessary to determine the location of a mobile robot in an environment surrounding the robot. To reach the target and for avoiding obstacles efficiently under different shapes of obstacle in an environment, an IT2FIS is designed to generate a path. A simulation of the path planning technique compared with other methods is performed. We tested the algorithm within various scenarios. Experiment results showed the efficiency of the generated path using an overhead camera for a mobile robot.

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Section: Literature Reviewmentioning

confidence: 99%

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Dirik

Castillo

Kocamaz

2019

Axioms

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“…The ith neuron has the output of the form: = ( ) × ( ). The output of the second layer is equal to the minimum value of the two inputs [47].…”

Section: Adaptive Neuro-fuzzy Inference Modelmentioning

confidence: 99%

Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses

Stojčić

Pamučar

Mahmutagić³

et al. 2018

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Queuing systems (QS) represent everyday life in all business and economic systems. On the one hand, and there is a tendency for their time and cost optimization, but on the other hand, they have not been sufficiently explored. This especially applies to logistics systems, where a large number of transportation and storage units appear. Therefore, the aim of this paper is to develop an ANFIS (Adaptive neuro-fuzzy inference system) model in a warehouse system with two servers for defining QS optimization parameters. The research was conducted in a company for the manufacturing of brown paper located in the territory of Bosnia and Herzegovina, which represents a significant share of the total export production of the country. In this paper, the optimization criterion is the time spent in the system, which is important both from the aspect of all customers of the system, and from that of the owner of the company. The time criterion directly affects the efficiency of the system, but also the overall costs that this system causes. The developed ANFIS model was compared with a mathematical model through a sensitivity analysis. The mathematical model showed outstanding results, which justifies its development and application.

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“…Assadipour et al (2016) proposed a toll-based dual-level programming method for the hazmat transportation and designed hybrid speed constraints for multiobjective particle swarm optimization algorithm [15]. Pamucar et al (2016) proposed a multiobjective route planning method for hazmat transportation and designed a solution algorithm combining neurofuzzy and artificial bee colony approach [16]. Mohammadi et al (2017) studied the hazmat transportation under uncertain conditions using a mixed integer nonlinear programming model and the metaheuristic algorithm was utilized to solve this model [17].…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Route Robust Optimization Model and Algorithm for Hazmat Transportation

Hao

et al. 2018

Discrete Dynamics in Nature and Society

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Aiming at route optimization problem of hazardous materials transportation in uncertain environment, this paper presents a multiobjective robust optimization model by taking robust control parameters into consideration. The objective of the model is to minimize not only transportation risk but also transportation time, and a robust counterpart of the model is introduced through applying the Bertsimas-Sim robust optimization theory. Moreover, a fuzzy C-means clustering-particle swarm optimization (FCMC-PSO) algorithm is designed, and the FCMC algorithm is used to cluster the demand points. In addition the PSO algorithm with the adaptive archives grid is used to calculate the robust optimization route of hazmat transportation. Finally, the computational results show the multiobjective route robust optimization model with 3 centers and 20 demand points’ sample studied and FCMC-PSO algorithm for hazmat transportation can obtain different robustness Pareto solution sets. As a result, this study will provide basic theory support for hazmat transportation safeguarding.

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Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—A neuro-fuzzy and artificial bee colony approach

Cited by 71 publications

References 52 publications

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses

A Multiobjective Route Robust Optimization Model and Algorithm for Hazmat Transportation

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