Technical Sessions A6 - G6
SESSION A6: Healthcare engineering and management
 QUANTITATIVE MODELLING OF ELDERLY PEOPLE FLOW WITHIN FRENCH HEATHCARE SYSTEM BEHAVIOUR
Fatima Zahra Hamdani, Lamya Jdaini, Malek Masmoudi and Jean Roche
The Health care system is encountering a high pressure in France due to population ageing that causes an increasing demand for elderly healthcare services. In this paper the elderly people flow in the French healthcare system is analyzed and two quantitative techniques; Markov model and Queueing Network, are used to model the pathway in hospital for elderly patient. The Markov chain model is used to understand the dynamics of elderly people flow and the Queueing Network is used to master the System behavior.
 MULTI CRITERIA DECISION MAKING APPROACH FOR HOME CARE TEAM SELECTION
Ikram Khatrouch and Malek Masmoudi
Team building is one of the important issues in human resources management. In Home care service, selecting best team aims at improving the quality of service at patient’s home. There are various methods regarding team selection in literature. This paper proposes a new approach for home care team selection within home care domain. The proposed model integrates TOPSIS system with Analytic Hierarchy Process (AHP) and provides for several real cases, the best team satisfying several criteria. We test the effectiveness of the model on data from a real Home Support System from Roanne, in France.
 MAINTENANCE STRATEGY SELECTION FOR MEDICAL EQUIPMENTS USING FUZZY MULTIPLE CRITERIA DECISION MAKING APPROACH
Zeineb Ben Houria, Mariem Besbes, Bouthaina El Aoud, Malek Masmoudi and Faouzi Masmoudi
Maintenance strategy plays a key role in increasing availability and enhancing reliability levels of medical equipments. Selecting the optimal maintenance strategy for each device is a complex multidisciplinary task requiring experts’ judgments, considering associated uncertainties. In this paper, we present a fuzzy Multiple Criteria Decision Making (MCDM) methodology to select optimal maintenance strategies for medical equipment according to their criticalities. The methodology is based on Fuzzy Analytical Hierarchy Approach (FAHP) coupled with fuzzy TOPSIS. The effectiveness evaluation of criteria is determined by FAHP. Furthermore, fuzzy TOPSIS is used for ranking different maintenance strategies. Criticality is calculated and thus optimal maintenance strategy is selected.
 HYBRID HEURISTIC FOR PATIENTS TASKS SCHEDULING ON MEDICAL RESOURCES WITH BED ALLOCATION
Nour Al Houda Saadani, Maroua Helioui and Zied Bahroun
The health care industry is more and more facing big challenges to increase its efficiency and at the same time deliver good quality services. To ensure the good management of financial budgets, hospitals try to improve their productivity by bringing significant gains in terms of efficiency. In this context, we propose a hybrid heuristic that uses a mixed-integer model to solve at the same time the scheduling of patients on the different necessary medical equipment and the management of beds during their stays in the hospital. The objective is to minimize the patients stay in the hospital. A lower bound is defined to evaluate the performance of the proposed heuristic. The results showed that our heuristic gives good solutions in reasonable computational times.
SESSION B6: System Simulation and Forecasting
 SIMULATING PERFORMANCE FOR ONE-DEDICATED-LANE BUS RAPID TRANSIT/LIGHT RAIL SYSTEMS
Rising vehicular traffic on urban roadways can create severe delays which cost governments and people both time and money. Given that narrow downtown urban corridors have little room for widening roadways, other alternative methods must be analyzed to help ease congestion. In this paper, we consider one-dedicated-lane bus rapid transit/light rail systems. Specifically, we focus on speed control rules to absorb the impact of the stochastic demand (and the different distances between pairs of neighbouring stops) on the performance of a closed system, i.e., with the surrounding traffic ignored or its effect negated. We develop a simulation model based on the commercial simulation software ProModel for such a closed system. We also use the simulation model to study the performance of speed control as an operating rule, including the relationship between headway and the required average transit-vehicle speed. A five-stop single-track train system was created in ProModel simulating a light-rail line where trains can only cross at the passenger stops. The model was analyzed by comparison to a standard common dual-track train system. It was found that trains in the single-track model must increase their speed nonlinearly between two stations to compensate for increased stress in the system.
 Prediction of CPU Availability in Volunteer Computing Systems using Multivariate Time Series Modeling
Nahla Chabbah Sekma, Ahmed Elleuch and Najoua Dridi
Computing resources in volunteer computing grid represent a big under-used reserve of processing capacity. Predicting CPU availability can help to better exploit these resources and make effective scheduling decisions. Autoregressive models are among the simplest prediction techniques that may perform better than the most complex competitors. To address their limitations, we propose an automated approach to check whether time series meet the model assumptions and to construct a prediction model by identifying its appropriate order value. At each prediction, we consider autoregressive models over three different past analyses: first over the recent hours, second during the same hours of the previous days and third during the same weekly hours of the previous weeks. We compare multivariate vector autoregressive models (VAR) and pure autoregressive models (AR), constructed according to our approach, against the tendency prediction technique using traces of a large-scale Internet-distributed computing system, termed seti@home.
 Implementing PID Control for Adjustable Service-Rate Queueing System Simulations
Paul Babin and Allen Greenwood
Adjustable service-rate queueing simulations are used to model the behavior of a variety of systems of interest to industrial engineers where the service rate is adjusted based on the number of items in queue. Previous papers have studied the performance of systems using linear feedback based on the current number of items in queue compared to a target value – which performs well for a stationary arrival rate. This paper looks at applying a proportional integral derivative (PID) controller to provide a more realistic representation of the system when non-stationary arrivals are included. These closed-loop models better represent system performance when management intervenes with rate adjustments in response to changes in the number in queue. The feedback signal to adjust the service rate is based on the error – the difference between the target and current number in queue. One controller term is proportional to the current error, one term is an accumulation of the area under the error-time curve, and one term is related to the rate of change of the error. Performance measures related to the response of the system to a step change in the average arrival rate value are included. The stochastic nature of queueing system is also considered. The application of familiar PID control to represent management feedback process to an adjustable service-rate queueing system is demonstrated in a discrete event simulation model.
 Internet Prospects' flows forcasting for a multi-period optimisation model of offer/demand assignment problem
Manel Maamar, Vincent Mousseau, Wassila Ouerdane and Alexandre Aubry
In this paper, we propose a forecasting system for a multi-period assignment model, where Internet prospects' flows evolve continuously over time. In particular, we show how we build a forecasting model of Internet prospects' flows while considering some characteristics of a multi-period assignment model in order to optimize and improve the marketplace's system of Place des leads (PdL). A numerical investigation is conducted based on real data of PdL application to show the benefits of introducing forecasting information to the multi-period optimization model.
SESSION C6: Recent Metaheuristics & Scheduling
 RECENT HYBRID METAHEURISTICS FOR MULTIOBJECTIVE SCHEDULING
Mitsuo Gen, Lin Lin, Wenqiang Zhang and Youngsu Yun
In real manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues with multiple objectives. However it is very difficult for solving the COP problems intractable by the traditional approaches because of NP-hard problems. For developing effective and efficient algorithms that are in a sense ""good,"" i.e., whose computational time is small as within 3 minutes, we have to consider three issues: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP)."
In this paper, we focus on recent hybrid metaheuristics to solve a variety of multiobjective scheduling problems in manufacturing systems: hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MoEA) and hybrid multiobjective genetic algorithm and particle swarm optimization with Cauchy distribution (HMoGA.PSO+CD). We also demonstrate their applications to AGV dispatching model by random key-based hybrid evolutionary algorithm, assembly line balancing model with worker capability by HSS-MoEA and two-stage re-entrant flexible flowshop scheduling with blocking by Hybrid PSO with FLC.
 HYBRIDIZATION OF MODIFIED CUCKOO SEARCH AND GENETIC ALGORITHM FOR RELIABILITY OPTIMIZATION PROBLEMS
Youngsu Yun, Jung-Bok Jo and Mitsuo Gen
In this paper, a hybrid genetic algorithm with modified cuckoo search (MCS-HGA) is proposed for solving reliability optimization problems. For the proposed MCS-HGA, a modified cuckoo search (MCS) which improves a weakness of conventional cuckoo search (CS) is adapted. Combining the MCS with genetic algorithm (GA) can reinforce search quality and speed toward global optimal solution rather than combining conventional CS and GA does. In numerical experiments, three types of reliability optimization problems are used for comparing the performance of the proposed MCS-HGA with those of various conventional approaches including CS and GA. The experimental result shows that the proposed MCS-HGA outperforms the conventional algorithms.
 MODELING AND ANALYSIS OF THE CHARGING INFRASTRUCTURE ALLOCATION FOR A MULTIPLE-ROUTE SERVICE DYNAMIC WIRELESS CHARGING TRANSPORTATION SYSTEM USING A GENETIC ALGORITHM
Min Seok Lee, Seungmin Jeong and Young Jae Jang
We present an innovative electric vehicle known as the online electric vehicle (OLEV). The OLEV uses advanced wireless power transfer technology that enables it to charge its battery while in motion using charging infrastructure installed under roads. The OLEV system is considered for application in a city’s mass public transportation system because it can be effectively used for buses that make frequent stops on predetermined routes. In this paper, we present the system design issues involved in deploying the OLEV system on multiple routes. In particular, we present a mixed integer programming (MIP) model to minimize the initial investment cost by determining the optimal allocation of the charging infrastructure. We propose a genetic algorithm (GA) as a solution approach and compare its efficiency with that of a branch and cut algorithm. We also describe the characteristics of the GA as they apply to the multiple-route problem facing the OLEV system. Finally, we conducted numerical experiments on the multiple-route problem to verify the performance of the GA.
 EVALUATION OF ISLAND GENETIC ALGORITHM BASED HIERARCHICAL SCHEDULER ORGANIZATION FOR MULTI-OBJECTIVE SCHEDULING
Gursel Suer and Arkopaul Sarkar
This study proposes a hierarchical scheduler organization based on island based genetic algorithm (GA), as a prototype for future experiments on human profiles in decision-making process in optimization algorithms. In this proposed framework, group of single objective schedulers configure different GA engines applying different fuzzy membership and forms teams aiming to satisfy partial objective. Best candidates chosen from different scheduler teams are migrated to a multi objective scheduler GA engine periodically as seeds. The performance of the framework is evaluated against a control multi-objective GA engine with no seeding from single objective schedulers and it is found that this framework improves the quality of solution and convergence time. A separate experiment is performed by varying the frequency of the migration and also the percentage of seed from the single objective schedulers to multi objective scheduler. It is observed that, higher percentage of seeding improves quality of solution but this effect is more prominent interval of seeding is longer.
SESSION D6: Manufacturing
 A NEW FLEXIBILITY INDEX FOR MACHINES SELECTION IN RECONFIGURABLE MANUFACTURING SYSTEM DESIGN PROBLEM: MULTIOBJECTIVE APPROACH
Hichem Haddou Benderbal, Mohammed Dahane and Lyes Benyoucef
Reconfigurable Manufacturing System (RMS) is a recent manufacturing paradigm driven by responsiveness and high performance, where machine components, machines or material handling units can be added, removed, modified or interchanged as needed. Thus, the design of such systems is based on reconfigurable machines capabilities and product specification. This paper deals with the reconfigurable Manufacturing System (RMS) design problem. Here we propose a RMS design approach based on an adapted non-dominated sorting genetic algorithm (NSGA-II) for selecting the best set of machines to ensure the best process plan according to the customized flexibility required for producing all parts of a given product. This approach will allow us to get the more adequate machines from a set of available (candidate) ones. The machines selection is based on two main objectives, namely the minimization of the total completion time and the maximization of the flexibility index. In order to demonstrate the application of our approach, we present a numerical example and discuss the obtained results.
 HYBRID DIFFERENTIAL EVOLUTION-DATA MINING (HDEDM) ALGORITHM FOR UNCERTAIN DYNAMIC CMS PROBLEM
Fariborz Jolai, Mona Koushan and Ata Allah Taleizadeh
In this paper we consider a DCMS with uncertain demand considering different Inter and intra cell costs and money time value consideration. The proposed model considering inter/intra- cell material handling cost, machine cost, operation cost, amortization cost. We propose a hybrid DE and GA algorithm to solve considered DCMS problem. To enhance solution quality and reduce computation time of DE and GA, we employ data mining (DM) technique. Experimental results demonstrate that the incorporation of DM and DE can improve the solution quality and calculation time produced by this method also it represents better results than other algorithms.
TOWARDS A DYNAMIC RECONFIGURATION FOR MANUFACTURING SYSTEM
Wiem Mrabet and Talel Ladhari
Dynamic reconfiguration problem is an important issue in modern manufacturing system with the feature of combinatorial computation complexity. A dynamic reconfiguration model, which is based on Multi-Agent System (MAS), was proposed. Communication and negotiation mechanism between agents were addressed to support autonomic decision for each individual agent. Furthermore, the performance of our approach is evaluated by a simulation that shows its effectiveness in dynamic reconfiguration resolution.
A BI-OBJECTIVE MODEL FOR A CELLULAR MANUFACTURING SYSTEM INTEGRATING INTRA-CELL LAYOUT AND PROCESSING ROUTE RELIABILITY
Yousef Nejatbakhsh, Shima Shirzadi, Reza Tavakkoli-Moghaddam, Ahmad Ebrahimi and Reza Kia
This paper presents a new integer bi-objective model to integrate reliability and intra-cell layout design in a cellular manufacturing system (CMS). The first objective is to minimize the total costs (e.g., inter and intra-cell material handling, setting up routes, machine overhead and operation) considering intra-cell layout, operation sequence, operation time, alternative process routes, route selection, machine capacity, demand and part movements in a batch. The second objective is to maximize the processing routes reliability. The model presented in this paper is capable of modelling different failure characteristics (e.g., an increasing, decreasing or constant machine failure rate). To represent the capability of the presented model, a numerical example is solved using the _-constraint method as an optimization tool.
SESSION E6: Heuristics and Approximation Algorithms for Combinatorial Problems
 Mathematical Formulations for the Unrelated Parallel Machines with Precedence Constraints
Mohammed-Albarra Hassan Abdel-Jabbar, Imed Kacem, Sébastien Martin and Izzeldin M. Osman
In this paper we studied an unrelated parallel machine scheduling problem of minimizing makespan subject to precedence constraints Rm|prec|Cmax. The environment of this problem is common in electronic manufacturing, service industries and computing, etc. The problem is NP-hard in the strong sense. This research proposes several mathematical formulations. We generate sets of benchmark instances and compare the performance of the mathematical formulations is compared with extensive computational testing.
 A GRASPXELS APPROACH FOR THE RESOLUTION OF THE INTEGRATED PRODUCTION AND TRANSPORTATION SCHEDULING PROBLEM
Philippe Lacomme, Aziz Moukrim, Alain Quilliot, Daniele Vigo and Marina Vinot
The integrated production and transportation scheduling problem (PTSP) reflects a real concern in the industry world. However, for many years companies and researchers considered the production and transportation sub-problems separately and sequentially. Unfortunately, this kind of approach will not lead to a global optimal solution. The problem considered in this paper is the integrated production and transportation scheduling problem with capacity constraints and a short shelf life product. In this problem, given a subset of customers, all products must be produced before being delivered directly to the clients by complying with lifespan. To solve this problem, an efficient greedy randomized adaptive search procedure (GRASP), with an evolutionary local search (ELS) is introduced and benchmarked on classical instances. The methods have proven to be more efficient than previous published ones, providing new best solutions in shorter computing time.
 Hybrid metaheuristic based on adaptive memory programming for the vehicle routing problem with two-dimensional loading constraints
Lei Wu, Mhand Hifi and Moudher Khalid Abdal-Hammed
In this paper we investigate the use of the adaptive memory programming for solving the vehicle routing problem with two-dimensional loading constraints, an NP-hard combinatorial optimization problem. Such a problem may be viewed as the combination of two complementary well-known problems: two-dimensional bin-packing problem and capacitated vehicle routing problem. The proposed method considers a two-phase solution procedure: a first phase generates a placement order and applies a bottom-left strategy to this order for packing the items in the vehicle and, a second phase is applied for reaching a feasible routing. Both phases are combined by using a diversification strategy. The proposed method has been evaluated on Gendreau's benchmark instances. The obtained results are compared to those reached by the best methods taken from the literature. Encouraging results have been obtained.
 A genetic algorithm approach to the single-vendor multi-customer integrateddelivery-inventory problem
Zakaria Hammoudan, Benoit Beroule, Olivier Grunder, Oussama Barakat and Abdellah El Moudni
Considerable attention has previously been given to the single-vendor single-customer integrated inventory problem. In this work, we tackle the problem of single-vendor multi-customer with multi-product consideration supply chain model with one transporter available to deliver the products from the sup-plier to the customers. A mathematical model incorporating the costs of both the delivery and then storage costs is developed as a Mixed Integer Programming model (MIP). Then, we propose a Genetic Algorithm (GA) to solve this problem concurrent with the CPLEX solver. Extensive experiments are conducted to evaluate the efficiency of the GA in terms of time of resolution and solution quality.Both procedures are then compared through computational experiments which show that the GA is capable of generating optimal solutions within a short amount of CPU time.
SESSION F6: Multi-Criteria Decision Making and Decision Analysis
IDENTIFYING AN ASSESSABLE SET OF SOFT SKILLS ENGINEERS NEED TO SUCCEED
Dyah Ariningtyas Hening and David Koonce
Soft skills are, by definition, skills necessary for a work environment and which are outside of the teachable, technical skills traditionally found in engineering education. There is currently no study that provides a method that effectively assesses soft skills development for engineers. This paper presents two approaches to assess the perceptions of a select group of undergraduate and early-career graduate students regarding important soft skills needed to succeed in a work environment. The methods used in this study are the Q-Methodology and a Conventional Survey method. Kantrowitz’s Soft Skills Performance Measurement (SSPM) tool serves as a basis for the soft skills activities, which are written out as statements . The Q-Methodology reduced the number of statements to a total of 19 statements that participants deemed important. The conventional survey reduced the total number of statements to 25. There are 11 statements identified by both Q-Methodology and Conventional Survey participants as important statements. 8 additional statements were selected from the Q-Methodology and 11 additional statements were selected in the Conventional Survey method. Thus, by using the union of statements, which was reduced from the 106 statements in the SSPM, a total of 30 statements were identified for the final assessment tool.
From a Literature Review to a Research Direction: Integrative Supply Chain Network Optimization Models
Canser Bilir, Sule Onsel Ekici and Donald C. Sweeney
The number of studies addressing supply chain (SC) network optimization and modeling has grown substantially over the last decade. Here we review the strategic SC optimization literature and discuss several model features, including decision variables, model objectives, model nature, solution algorithms, SC risk, competition modeling and the multi-objective solution method. Based on this literature review, we propose a novel approach in which SC network optimization modeling and competitive facility location are simultaneously utilized.
PRIORITIZATION OF IMPROVEMENT PROJECTS IN ENERGY MANAGEMENT SYSTEM
Mohamed Salaheldin, Ahmed Farouk Abdul Moneim and M. Nashat Fors
Energy saving became increasingly in-focus to overcome the decreasing of available energy sources and to reduce its environmental impact regarding the climate change scenarios. Energy management system (EnMS) is one of the effective approaches to contribute in energy savings by increase the energy efficiency especially in industry. Within any organization, one of the crucial elements during implementation of an EnMS is the selection and prioritization of energy improvement opportunities or energy improvement projects. The prioritization process needs a systematic decision support system that handles such multi-criteria decision problem. This paper proposes the merging of fuzzy analytic hierarchy process (FAHP) with value-focused thinking (VFT) to develop a new methodology for decision support suitable for energy improvement projects prioritization. The methodology is introduced and validated through a real case in Egyptian petrochemical industry.
Selecting The Contractor using Multi Criteria Decision Making in National Gas Company of Lorestan province of Iran
Fatemeh Jaferi, Moslem Parsa, Hadi Shirouyezad and Heshmatolah Shams Khorramabadi
In this modern fluctuating world, organizations need to outsource some parts of their activities (project) to providers in order to show a quick response to their changing requirements. In fact, a number of companies and institutes have contractors do their projects and have some specific criteria in contractor selection. Therefore a set of scientific tools is needed to select the best contractors to execute the project according to appropriate criteria. Multi criteria decision making (MCDM) has been employed in the present study as a powerful tool in ranking and selecting the appropriate contractor. In this study, devolving second-source (civil) project to contractors in the National Gas Company of Lorestan Province (Iran) has been found and therefore, 5 civil companies have been evaluated. Evaluation criteria include executive experience, qualification of technical staff, good experience and company's rate, technical interview, affordability, equipment and machinery. Criteria's weights are found through experts' opinions along with AHP and contractors ranked through TOPSIS and AHP. The order of ranking contractors based on MCDM methods differs by changing the formula in the study. In the next phase, the number of criteria and their weights have been sensitivity analyzed through using AHP. Adding each criteria changed contractors' ranking. Similarly, changing weights resulted in a change in ranking. Adopting the stated strategy resulted in the facts that not only is an appropriate scientific method available to select the most qualified contractors to execute gas project, but also a great attention is paid to picking needed criteria for selecting contractors. Consequently, executing such project is undertaken by most qualified contractors resulted in optimum use of limited resource, accelerating the implementation of project, increasing quality and finally boosting organizational efficiency.
SESSION G6: Industrial Big-data Instrumentation and Infrastructure Integrity
AWCC-TECHNOLOGY OF SPACE-TIME BIFURCATION ANALYSIS TO RISK ASSESSMENT IN WEATHER-DEPENDENT INDUSTRIES
Yury Kolokolov, Anna Monovskaya and Kondo Adjallah
Climatology remains one of the traditional applications of the advanced computer technologies to big-data acquisition, processing, and analysis. At the same time, the problem of influences of climatic changes on different technological processes becomes more and more pressing. Thus, the interdisciplinary engineering problem of risk assessment appears. To get the well-grounded answer concerning the qualitative changes in the climate dynamics the bifurcation analysis should be used. The reliability of the answer depends on the data: are there simulated or measured data? In the last case, the result is the most reliable, but it is very hard to realize it in practice. We present the computer technology to space-time bifurcation analysis of the temperature observations, where an annual warming-cooling cycle (AWCC) is the unit to analyze. The proposed AWCC-technology is based on the use of both the conceptual model of the hysteresis regulator with double synchronization (HDS-model) to describe the local climate dynamics and the approach to build the modified bifurcation diagrams. The possibility to build the diagrams is provided by SUC-logic to make the synthesis of experimental bifurcation analysis, symbolical analysis, and multidimensional data visualization by use of special sections (S) and units (U) in certain consecutions (C). The AWCC-technology provides the intelligent tool to determine the climatic norms, taking into account the effects of local and regional temperature oscillations (LTO- and RTO-effects correspondingly): each LTO-effect exhibits the latent dynamics of local climatic changes; each RTO-effect exhibits such blocking weather pattern that leads to a persistent temperature behavior lasting longer than it usually occurs. So, the AWCC-technology could be useful for design and planning exploitation of the units used in weather-dependent industries, taking into account the increased reliability of risk estimations.
Integer Linear Programming Based Scheduling Method for Wireless Sensors Network Lifespan Optimization
Yousif Elhadi Elsideeg Ahmed, Kondo H. Adjallah, Imed Kacem and Sharief F. Babiker
In this paper, the wireless sensors network (WSN) for data collection on a given area, with a limited energy source lifetime maximization problem is addressed. We developed a linear mathematical model for WSNs scheduling and lifetime optimization. For a randomly deployed set S of sensors to monitor a set T of targets, we found a set of q non-disjoint set covers. Then, the proposed method aims to find the optimal scheduling for the WSN lifetime extension. The method is applied using eclipse supported with the linear optimization libraries of Cplex and Java Development Kit (JDK) to obtain the optimal solution for this problem on different operation situations as experimental results.
Wind data Collection for potential analysis and electricity generation Case study in northwestern coast of Senegal
Boudy Ould Bilal, Kondo H. Adjallah, Fadel Kebe, Papa Alioune Sarr Ndiaye, Vincent Sambou and Alexandre Sava
The exploration of the wind energy has become one of the most signiﬁcant subjects for countries all around the world. It became very important to develop effective and scientific approaches. That allows to evaluate the wind resources potential in order to choice the suitable wind turbines. The target of this paper is a data analysis to determine the wind energy potential of five sites along the northwestern coast of Senegal and to assess the electricity generation from fifteen wind turbines available on the market in order to choose the suitable technologies. The wind characteristics and the wind energy potential were analyzed using wind speeds data collected during one year over each site at 12 m and 7 m of height. Using commercial wind turbines, which size is between 250 W and 2 MW, one has made a technical assessment of electricity generation. Calculations from Wind turbines characteristics enable us to conclude about the most adapted wind turbine for electricity generation on all sites for a remote and a grid connected application.