Signed distance ranking based approach for solving bounded interval‐valued fuzzy numbers linear programming problems

Enayattabr

Motameni

et al. 2021

Complex Intell. Syst.

Self Cite

In recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued fuzzy numbers using alpha cuts. Then, an extended distance function is introduced for comparing the path weights. Finally, we intend to use a modified artificial bee colony (MABC) algorithm to find the interval-valued membership function of SP in such mixed interval-valued fuzzy network. The proposed algorithm is illustrated via two applications of SP problems in wireless sensor networks and then the results are compared with those derived from genetic and particle swarm optimization (PSO) algorithms, based on three indexes convergence iteration, convergence time and run time. The obtained results confirm that the MABC algorithm has less convergence iteration, convergence time and implementation time compared to GA and PSO algorithm.

Section: Experiments and Resultsmentioning

confidence: 99%

“…The aim of this section to review some essential definitions and preliminaries of mixed interval-valued fuzzy numbers [8,16,20,24,41].…”

Section: Mixed Interval-valued Fuzzy Numbersmentioning

confidence: 99%

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Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

Enayattabr

Motameni

et al. 2021

Complex Intell. Syst.

Self Cite

“…In future work, we will study other variations of shortest path problem such as the maximum reliability path problem, shortest path with turn penalties; and the resource‐constrained shortest path problem in fuzzy environment to solve various real‐world decision problems such as crew rostering problems and tail assignment problem in aircraft scheduling. We also plan to extend our methodology to interval‐valued fuzzy networks . In addition how to modify the proposed EABC algorithm for solving robot's fuzzy routing problem concentrating simultaneously on length, speed, safety, and smoothness of the path is other new topic for research.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

Sori

Computational Intelligence

Motameni

2019

Self Cite

One of the fundamental challenges of the robotics field is robot's movement. That is, why route planning is an eminent issue of robotics research and it is used to enhance autonomy of moving robots in complex environments. The objective of route planning problem is to find the shortest route without collide from initiation point to destination point so that the amount of energy consumption by robot would not exceed a predefined amount. Because neither the amount of energy consumption nor the robot's passed distance index cannot be measured precisely due to environmental conditions, and fuzzy data is used for modeling the problem and the problem would be called “Robot Fuzzy Constrained shortest Route” problem. The main contributions of this study are fivefold: (i) The mathematical model of fuzzy constrained shortest route problem (FCSRP) is formulated; (ii) An elite artificial bees' colony (EABC) algorithm is used to solve the robot's FSCRP; (iii) The proposed EABC algorithm is simulated with two fuzzy networks; (iv) The performance of the proposed approach is compared with the performance of genetic algorithm and particle swarm optimization algorithm; and (v) The results show the convergence speed of the EABC algorithm is higher than the existing algorithms.

“…In the following subsections, preliminaries needed to introduce the proposed method to solve the FMOSPP are presented [37][38][39][40][41].…”

Section: Mathematical Backgroundmentioning

confidence: 99%

Solving fuzzy multi-objective shortest path problem based on data envelopment analysis approach

Bagheri

Razavyan

et al. 2021

Complex Intell. Syst.

Self Cite

The shortest path problem (SPP) is a special network structured linear programming problem that appears in a wide range of applications. Classical SPPs consider only one objective in the networks while some or all of the multiple, conflicting and incommensurate objectives such as optimization of cost, profit, time, distance, risk, and quality of service may arise together in real-world applications. These types of SPPs are known as the multi-objective shortest path problem (MOSPP) and can be solved with the existing various approaches. This paper develops a Data Envelopment Analysis (DEA)-based approach to solve the MOSPP with fuzzy parameters (FMOSPP) to account for real situations where input–output data include uncertainty of triangular membership form. This approach to make a connection between the MOSPP and DEA is more flexible to deal with real practical applications. To this end, each arc in a FMOSPP is considered as a decision-making unit with multiple fuzzy inputs and outputs. Then two fuzzy efficiency scores are obtained corresponding to each arc. These fuzzy efficiency scores are combined to define a unique fuzzy relative efficiency. Hence, the FMOSPP is converted into a single objective Fuzzy Shortest Path Problem (FSPP) that can be solved using existing FSPP algorithms.