A multi-objective two-stage approach to the design of communication networks subject to production constraints for computerized manufacturing systems

1989 ◽  
Vol 27 (4) ◽  
pp. 587-598 ◽  
Author(s):  
TARUN GUPTA ◽  
BIMAN K. GHOSH
Keyword(s):  
Communication Networks ◽  
Manufacturing Systems ◽  
Two Stage ◽  
Multi Objective ◽  
Production Constraints ◽  
Computerized Manufacturing

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

A two-stage evolutionary strategy based MOEA/D to multi-objective problems

2021 ◽  
pp. 115654
Author(s):  
Jie Cao ◽  
Jianlin Zhang ◽  
Fuqing Zhao ◽  
Zuohan Chen
Keyword(s):  
Evolutionary Strategy ◽  
Two Stage ◽  
Multi Objective

Hub Location Allocation Problems and Solution Algorithms

2018 ◽  
pp. 77-106
Author(s):  
Peiman A. Sarvari ◽  
Fatma Betül Yeni ◽  
Emre Çevikcan
Keyword(s):  
Engineering Design ◽  
Communication Networks ◽  
Manufacturing Systems ◽  
Location Problem ◽  
Computer Engineering ◽  
Hub Location ◽  
Hub Location Problem ◽  
Location Allocation ◽  
Solution Algorithms ◽  
Location Allocation Problem

The Hub Location-Allocation Problem is one of the most important topics in industrial engineering and operations research, which aims to find a form of distribution strategy for goods, services, and information. There are plenty of applications for hub location problem, such as Transportation Management, Urban Management, locating service centers, Instrumentation Engineering, design of sensor networks, Computer Engineering, design of computer networks, Communication Networks Design, Power Engineering, localization of repair centers, maintenance and monitoring power lines, and Design of Manufacturing Systems. In order to define the hub location problem, the present chapter offers two different metaheuristic algorithms, namely Particle Swarm Optimization or PSO and Differential Evolution. The presented algorithms, then, are applied to one of the hub location problems. Finally, the performances of the given algorithms are compared in term of benchmarking.

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