Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

2012 ◽  
Vol 62 (2) ◽  
pp. 469-478 ◽  
Author(s):  
Amir Musa Abazari ◽  
Maghsud Solimanpur ◽  
Hossein Sattari
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Programming Model ◽  
Mixed Integer ◽  
Mathematical Programming Model

A Genetic Algorithm Approach for Multi-Product Multi-Machine CONWIP Production System

2011 ◽  
Vol 110-116 ◽  
pp. 3624-3630
Author(s):  
Saeede Ajorlou ◽  
Issac Shams ◽  
Mirbahador G. Aryanezhad
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Programming Model ◽  
Search Algorithms ◽  
Mixed Integer ◽  
Simplified Model ◽  
Mathematical Programming Model ◽  
Production Lines ◽  
Work In Process ◽  
Genetic Algorithm Approach

In this paper, a new mathematical programming model is developed to address common issues relating to single-stage CONstant-Work-In-Process based production lines. A Ge-netic Algorithm (GA) approach is then proposed to directly solve the model in order to simultaneously determines the optimal job sequence and WIP level. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, our proposed method does not rely on a linearized or simplified model of the system. results from a comprehensive numerical example indicate computational efficiency and validation of our method.

OPINION-AWARE INFLUENCE MAXIMIZATION: HOW TO MAXIMIZE A FAVORITE OPINION IN A SOCIAL NETWORK?

2018 ◽  
Vol 21 (06n07) ◽  
pp. 1850022 ◽  
Author(s):  
MEHRDAD AGHA MOHAMMAD ALI KERMANI ◽  
REZA GHESMATI ◽  
MASOUD JALAYER
Keyword(s):  
Genetic Algorithm ◽  
Social Network ◽  
Mathematical Programming ◽  
Real World ◽  
Programming Model ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Message Content ◽  
Mathematical Programming Model ◽  
The Real

Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes’ opinions have not been considered. In this paper, a modified version of influence maximization which is named as “opinion-aware influence maximization” (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms.

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