Propose a Fuzzy Queuing Maximal Benefit Location Problem

TL;DR

This paper introduces a fuzzy queuing location model for congested systems, using genetic algorithms and ant colony optimization to solve an NP-hard problem with uncertain variables.

Contribution

It develops a fuzzy queuing model for location problems and applies heuristic algorithms for solution, addressing real-world variability.

Findings

Genetic algorithm effectively solves the fuzzy queuing location problem.

Ant colony optimization provides comparative results and run-time analysis.

The model improves accuracy in congested system location planning.

Abstract

This paper presents a fuzzy queuing location model for congested system. In a queuing system there are different criteria that are not constant such as service rate, service rate demand, queue length, the occupancy probability of a service center and Probability of joining the queue line. In this paper with fuzzifying all of these variables, will try to reach an accurate real problem. Finally we change the problem to a single objective function and as far as this model is in NP-Hard classification we will use genetic algorithm for solving it and ant colony for comparison is used for their results and run time.