Genetic Algorithm Applications to Optimization Modeling

2011 ◽  
pp. 748-754 ◽  
Author(s):  
Pi-Sheng Deng
Keyword(s):  
Portfolio Management ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Solution Space ◽  
Function Optimization ◽  
Hill Climbing ◽  
Local Optima ◽  
Dynamic Memory ◽  
Promising Area ◽  
Improved Performance

Genetic algorithms (GAs) are stochastic search techniques based on the concepts of natural population genetics for exploring a huge solution space in identifying optimal or near optimal solutions (Davis, 1991)(Holland, 1992)(Reeves & Rowe, 2003), and are more likely able to avoid the local optima problem than traditional gradient based hill-climbing optimization techniques when solving complex problems. In essence, GAs are a type of reinforcement learning technique (Grefenstette, 1993), which are able to improve solutions gradually on the basis of the previous solutions. GAs are characterized by their abilities to combine candidate solutions to exploit efficiently a promising area in the solution space while stochastically exploring new search regions with expected improved performance. Many successful applications of this technique are frequently reported across various kinds of industries and businesses, including function optimization (Ballester & Carter, 2004)(Richter & Paxton, 2005), financial risk and portfolio management (Shin & Han, 1999), market trading (Kean, 1995), machine vision and pattern recognition (Vafaie & De Jong, 1998), document retrieval (Gordon, 1988), network topological design (Pierre & Legault, 1998)(Arabas & Kozdrowski, 2001), job shop scheduling (Özdamar, 1999), and optimization for operating system’s dynamic memory configuration (Del Rosso, 2006), among others. In this research we introduce the concept and components of GAs, and then apply the GA technique to the modeling of the batch selection problem of flexible manufacturing systems (FMSs). The model developed in this paper serves as the basis for the experiment in Deng (2007).

An efficient algorithm for function optimization: modified stem cells algorithm

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammad Taherdangkoo ◽  
Mahsa Paziresh ◽  
Mehran Yazdi ◽  
Mohammad Bagheri
Keyword(s):  
Stem Cells ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Natural Behavior ◽  
Local Optima ◽  
Bee Colony ◽  
Intelligent Behavior ◽  
Improved Performance

AbstractIn this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

DECISION SUPPORT FOR A SUSTAINABLE PRODUCTION IN JOB SHOP MANUFACTURING SYSTEMS

10.6036/9917 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 455-459
Author(s):  
MAHDI NADERI ◽  
ANTONIO FERNÁNDEZ ULLOA ◽  
JOSÉ ENRIQUE ARES GÓMEZ ◽  
GUSTAVO PELÁEZ LOURIDO
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Job Shop ◽  
Sustainable Production ◽  
Ease Of Use ◽  
Manufacturing Companies ◽  
Strategic Decisions ◽  
Production Environments

Despite the growing importance that is being given to the concepts of sustainability in many areas, not only in industry but also in the economy and public opinion in general, until now, most research has focused, practically, on the analysis of the concepts, but has not addressed, in a comprehensive way, its impact in decision making probably due to the complex relations of interdependence between its different aspects. In this context, MAPSAM (Methodology for the Assessment of Sustainability in Manufacturing Processes and Systems) was created to help the decision-making process, allowing a conscious and transparent assessment by administrators and managers at the different levels of the structure of companies and organisations. This article explains its development and application in a "job shop" type manufacturing system with an approach that allows the integration of economic, environmental and social criteria. MAPSAM is based on the use of various techniques and tools to quantify the importance of each aspect of sustainability and it has been applied in other production environments, being implemented in different systems, analysing their ease of use and evaluating their behaviour. The objective is to show how it helps to make operational, tactical and strategic decisions in the management on these type of manufacturing companies and, specifically, in this contribution we want to highlight its versatility and applicability, by validating it in a certain type of layout. With this new application, MAPSAM increases its possibilities as an innovative instrument that allows companies to make conscious and sustainable decisions in order to be more efficient, fair, supportive and respectful of the environment. Keywords: Manufacturing System, Simulation, Decision Support, Sustainable Production, Decision-Making

Systematische Navigation für die Reihenfolgeplanung/Systematic solution space navigation for job shop scheduling problems

2020 ◽  
Vol 110 (07-08) ◽  
pp. 563-571
Author(s):  
Edzard Weber ◽  
Eduard Schenke ◽  
Luka Dorotic ◽  
Norbert Gronau
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Solution Space ◽  
Comparative Test ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Space Navigation ◽  
Systematic Solution

Dieser Beitrag stellt einen Algorithmus für das Job-shop-Scheduling-Problem vor, welcher den Lösungsraum indexiert und eine systematische Navigation zur Lösungssuche durchführt. Durch diese problemadäquate Aufbereitung wird der Lösungsraum nach bestimmten Kriterien vorzustrukturiert. Diese Problemrepräsentation wird formal beschrieben, sodass ihre Anwendung als Grundlage für ein navigationsorientiertes Suchverfahren dienen kann. Ein vergleichender Test mit anderen Optimierungsansätzen zeigt die Effizienz dieser Lösungsraumnavigation.   This paper presents an algorithm for the job-shop scheduling problem indexing the solution space and performing systematic navigation to find good solutions. By this problem-adequate preparation of the solution space, the solution space is pre-structured according to certain criteria. This problem representation is formally described so that its application can serve as a basis for a navigation-oriented search procedure. A comparative test with other optimization approaches shows the efficiency of this solution space navigation.

PRACTICAL PRODUCTION LAYOUT DESIGN FOR MULTI-PRODUCT AND SMALL-LOT-SIZE PRODUCTION: A CASE STUDY

2016 ◽  
Vol 78 (7) ◽  
Author(s):  
Cheng Ying, Nyeoh ◽  
Hasnida Ab-Samat ◽  
Shahrul Kamaruddin
Keyword(s):  
Manufacturing Systems ◽  
Production Efficiency ◽  
Job Shop ◽  
Flow Line ◽  
Facility Layout ◽  
Layout Design ◽  
Simulation Software ◽  
Utilization Rate ◽  
Lot Size ◽  
Related Data

Facility layout design has an important function in manufacturing systems because it affects manufacturing costs, work in progress, lead times, and production output. This study presents facility layout designs for multi-product and small-lot-sized production lines. This research focuses on the development and analysis of layout alternatives on the basis of performance measures and aims to improve production efficiency. Thus, the tools and techniques available for the layout designed were investigated. Related data were collected, and alternative layouts were developed using the WITNESS simulation software. Finally, the alternative layouts were ana1yzed and evaluated using the analytic hierarchy process to identify the best possible layout. Two important parameters observed in the alternative layouts: the ability to produce a desired output and the flexibility of each layout coherent with the fluctuation of product demands in the industry. Results from analysis shows that suggested Model 3 with the combination of flow line and job shop configurations is the most suitable layout. This model has the highest machine utilization rate and the highest labour utilization rate yet requires only 21 operators, the lowest number of workers. For future work, this type of layout should be tested with different variation in lot sizes.

An RFID-Driven Graphical Formalized Deduction for Describing the Time-Sensitive State and Position Changes of Work-in-Progress Material Flows in a Job-Shop Floor

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Pingyu Jiang ◽  
Wei Cao
Keyword(s):  
Radio Frequency Identification ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Numerical Control ◽  
Shop Floor ◽  
Cnc Milling ◽  
Rfid Technology ◽  
Material Flows ◽  
Work In Progress ◽  
Frequency Identification

As a key advanced manufacturing technology in next generation manufacturing systems, radio frequency identification (RFID) technology is considered to be one of the most promising technological innovations with the potential to increase visibility and improve efficiency. Therefore, research about RFID and its applications are increasing by blasting with all kinds of RFID models in various fields, especially in manufacturing. By introducing RFID technology into the job-shop floor, this paper proposes a systematic RFID-driven graphical formalized deduction model (rfid-GFDM) for describing the time-sensitive state and position changes of work-in-progress (WIP) material flows and guiding where to deploy RFID devices and how to use them for collecting real-time on-site data. Four steps including RFID configuration based on the process flow model, state blocks model, automatic event generation, and extended event-driven model are proposed one by one to support the implementation of rfid-GFDM. The nature of RFID technology is revealed, too. A use case about a computer numerical control (CNC) milling system is studied, and it demonstrates the feasibility of the proposed model. Finally, the possibility of popularizing the model to other field is discussed, too. It is expected to establish a normative RFID modeling method that will facilitate the convenience of RFID applications in a broad scope.

Fuzziness in Ant Colony Optimization and Their Applications

2018 ◽  
pp. 363-376
Author(s):  
Deepthi P. Hudedagaddi ◽  
B. K. Tripathy
Keyword(s):  
Ant Colony Optimization ◽  
Early Stage ◽  
Solution Space ◽  
Fuzzy Linear Programming ◽  
Ant Colony ◽  
Local Optima ◽  
Local Improvement ◽  
Nature Inspired Algorithm ◽  
Solution Methods ◽  
Nature Inspired Algorithms

Nature-inspired algorithms are still at a very early stage with a relatively short history, comparing with many traditional, well-established methods. Metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. One major algorithm is Ant Colony Optimization which has been applied in varied domains to better the performance. Fuzzy Linear Programming models and methods has been one of the most and well-studied topics inside the broad area of Soft Computing. Its applications as well as practical realizations can be found in all the real-world areas. Here we wish to introduce how fuzziness can be included in a nature inspired algorithm like ant colony optimization and thereby enhance its functionality. Several applications of ACO with fuzzy concepts will be introduced in the chapter.

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