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Ahmadzadeh, FarzanehORCID iD iconorcid.org/0000-0002-8524-3321
Publications (10 of 18) Show all publications
Ahmadzadeh, F., Jederström, K., Plahn, M., Olsson, A. & Foyer, I. (2017). AN INVESTIGATION OF THE MOST IMPORTANT FACTORS FOR SUSTAINABLE PRODUCT DEVELOPMENT USING EVIDENTIAL REASONING. Numerical Algebra, Control and Optimization, 7(4), 435-455
Open this publication in new window or tab >>AN INVESTIGATION OF THE MOST IMPORTANT FACTORS FOR SUSTAINABLE PRODUCT DEVELOPMENT USING EVIDENTIAL REASONING
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2017 (English)In: Numerical Algebra, Control and Optimization, ISSN 2155-3289, E-ISSN 2155-3297, Vol. 7, no 4, p. 435-455Article in journal (Refereed) Published
Abstract [en]

Those working in product development need to consider sustain ability, being careful not to compromise the future generations ability to satisfy its needs. Several strategies guide companies towards sustainability. This paper studies six of these strategies: eco-design, green design, cradle-to-cradle, design for environment, zero waste, and life cycle approaches. Based on a literature review and semi-structured interviews, it identifies 22 factors of sustainability from the perspective of manufacturers. The purpose is to determine which are the most important and to use them as a foundation for a new design strategy. A survey based on the 22 factors was given to people working with product development; they graded each factor by importance. The resulting qualitative data were analyzed using evidential reasoning. The analysis found the factors minimize use of toxic substances, increase competitiveness, economic benefits, reduce material usage, material selection, reduce emissions, and increase product functionality are more important and should serve as the foundation for a new approach to sustainable product development.

Place, publisher, year, edition, pages
AMER INST MATHEMATICAL SCIENCES-AIMS, 2017
Keywords
Sustainable design, product development, evidential reasoning, sustainable product development strategy, Multi Criteria Decision Making
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-38737 (URN)10.3934/naco.2017027 (DOI)000424328600004 ()2-s2.0-85044257748 (Scopus ID)
Available from: 2018-02-23 Created: 2018-02-23 Last updated: 2018-04-05Bibliographically approved
Ahmadzadeh, F. & Bengtsson, M. (2017). Using evidential reasoning approach for prioritization of maintenance-related waste caused by human factors-a case study. The International Journal of Advanced Manufacturing Technology, 90(9-12), 2761-2775
Open this publication in new window or tab >>Using evidential reasoning approach for prioritization of maintenance-related waste caused by human factors-a case study
2017 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 90, no 9-12, p. 2761-2775Article in journal (Refereed) Published
Abstract [en]

The reduction and elimination of maintenance-related waste is receiving increasing attention because of the negative effect of such waste on production costs. The overall goal of this research is to identify and prioritize factors that can be considered maintenance-related waste within the automotive manufacturing industry. Five manufacturing companies participated in a workshop to identify root causes of maintenance-related waste; 16 categories were found. The identified factors were heavily reliant on human factors as a root or major contributory cause at different levels affecting performance and productivity. For prioritization, the evidential reasoning (ER) approach which is one of the latest developments in multi-criteria decision-making is applied. A basic tree structure necessary for ER assessment is developed based on the workshop results as well as literature on human factors. Then, a survey on basic attributes at the lowest level of this tree is designed and performed at one of the companies participating in the workshop. The application of ER shows that, on an overall level, "management condition" is in first order and "maintainer condition" and "working condition" are in second and third order respectively as the worst cases for creating maintenance-related waste. On the most delimited level "inadequate resources" and "weather/indoor climate" have the highest and lowest average scores respectively in ER ranking or prioritization. This methodology with its resulting ranking can be used as a tool to create awareness for managers seeking to reduce or eliminate maintenance-related waste.

Place, publisher, year, edition, pages
SPRINGER LONDON LTD, 2017
Keywords
Multiple criteria analysis, Maintenance, Human factors, Evidential reasoning, Prioritization
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-35657 (URN)10.1007/s00170-016-9377-7 (DOI)000401505000030 ()2-s2.0-84992153874 (Scopus ID)
Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2018-01-24Bibliographically approved
Ahmadzadeh, F. (2016). Multi criteria decision making with Evidential Reasoning under uncertainty. In: IEEE International Conference on Industrial Engineering and Engineering Management: . Paper presented at 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016, 4 December 2016 through 7 December 2016 (pp. 1534-1538).
Open this publication in new window or tab >>Multi criteria decision making with Evidential Reasoning under uncertainty
2016 (English)In: IEEE International Conference on Industrial Engineering and Engineering Management, 2016, p. 1534-1538Conference paper, Published paper (Refereed)
Abstract [en]

Many decision problems have more than one objective that need to be dealt with simultaneously. Moreover, because of the qualitative nature of the most of real world problem it is an inevitable activity and very important to interpret and present the uncertain information for making effective decision. The Evidential Reasoning (ER) approach which is one of the latest development within multi criteria decision making (MCDM) seems to be the best fit to synthesize both qualitative and quantitative data under uncertainty. To support this claim, two case studies were tested to illustrate the application of ER for prioritization and ranking of decision alternative to support decision process even with uncertain information. The importance of having a better structured decision process is essential for the success of any organization, so it can be applied widely in most of real world problem dealing with making effective decision.

Keywords
decision criteria, Evidential reasoning, prioritization, uncertainty, Engineering, Industrial engineering, Decision criterions, Evidential reasoning approaches, Multi-criteria decision making, nocv1, Real-world problem, Uncertain informations, Decision making
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-34768 (URN)10.1109/IEEM.2016.7798134 (DOI)000392208100311 ()2-s2.0-85009895145 (Scopus ID)9781509036653 (ISBN)
Conference
2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016, 4 December 2016 through 7 December 2016
Available from: 2017-02-08 Created: 2017-02-02 Last updated: 2017-02-16Bibliographically approved
Mostafa Orand, S., Mirzazadeh, A., Ahmadzadeh, F. & Talebloo, F. (2015). Optimisation of the Inflationary Inventory Control Model under Stochastic Conditions with Simpson Approximation: Particle Swarm Optimisation Approach. Iranian Journal of Management Studies, 8(2), 203-220
Open this publication in new window or tab >>Optimisation of the Inflationary Inventory Control Model under Stochastic Conditions with Simpson Approximation: Particle Swarm Optimisation Approach
2015 (English)In: Iranian Journal of Management Studies, ISSN 2008-7055, Vol. 8, no 2, p. 203-220Article in journal (Refereed) Published
National Category
Control Engineering Probability Theory and Statistics
Identifiers
urn:nbn:se:mdh:diva-36986 (URN)2345-3745 (ISRN)10.22059/ijms.2015.52631 (DOI)
Projects
XPRES - Excellence in Production Research
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2019-09-20Bibliographically approved
Fataneh, J., Ahmadzadeh, F. & Mirzazadeh, A. (2014). Attribute Control Chart Development by Evidential Reasoning. In: Proceedings of The 3rd international workshop and congress on eMaintenance: . Paper presented at The 3rd International Workshop & Congress on eMaintenance, Luleå University of Technology,Sweden 17-18 June 2014 (pp. 37-40).
Open this publication in new window or tab >>Attribute Control Chart Development by Evidential Reasoning
2014 (English)In: Proceedings of The 3rd international workshop and congress on eMaintenance, 2014, p. 37-40Conference paper, Published paper (Refereed)
Abstract [en]

Attributes charts are commonly used in monitoring quality characteristics of the proportion type and these charts assume that the monitored characteristics are binomially distributed. Classical control charts need to certain and precise data. However, in practice, quality experts express their opinion in imprecise form, which in turn, add more uncertainty and ambiguity. It is essential to properly represent and interpret uncertain information to evaluate product items. In this paper, the evidential reasoning (ER) based approach has been developed for supporting this uncertainty. So the belief multinomial p-control chart is introduced for monitoring the production process in the uncertainty condition, using evidence theory and the belief structure. A numerical example showing production process evaluation is examined by using the ER approach. The results show the proposed approach, is effective not only for reducing production defective but also for increasing the certainty in interpreting of quality variables (data).

National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-26438 (URN)978-91-7439-972-1 (ISBN)
Conference
The 3rd International Workshop & Congress on eMaintenance, Luleå University of Technology,Sweden 17-18 June 2014
Projects
XPRES
Available from: 2014-11-02 Created: 2014-10-31 Last updated: 2014-11-03Bibliographically approved
Al-Chalabi, H., Ahmadzadeh, F., Lundberg, J. & Ghodrati, B. (2014). Economic lifetime prediction of a mining drilling machine using an artificial neural network. International Journal of Mining, Reclamation and Environment, 28(5), 311-322
Open this publication in new window or tab >>Economic lifetime prediction of a mining drilling machine using an artificial neural network
2014 (English)In: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 28, no 5, p. 311-322Article in journal (Refereed) Published
Abstract [en]

This study develops models for predicting the economic lifetime of drilling machines used in mining. It uses three cases, each represented by a MATLAB code, to develop an optimisation model. The resulting ORT is fed as input to an artificial neural network (ANN) and the results translated into a relatively simple equation. The study finds that increasing the purchase price and decreasing the operating and maintenance costs will increase a machine's ORT linearly. Decreased maintenance cost has the largest impact on ORT, followed by increased purchase price and decreased operating cost. The ANN method gives a series of basic weight and response functions which can be made available to any engineer without the use of complicated software. It also helps decision-makers determine the best time economically to replace an old machine with a new one; thus, it can be extended to more general applications in the mining industry.

National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-26439 (URN)10.1080/17480930.2014.942519 (DOI)000343814000006 ()2-s2.0-84908509788 (Scopus ID)
Projects
XPRES
Available from: 2014-11-02 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved
Strömberg, T. & Ahmadzadeh, F. (2014). Excess action and broken characteristics for Hamilton-Jacobi equations. Nonlinear Analysis, 110, 113-129
Open this publication in new window or tab >>Excess action and broken characteristics for Hamilton-Jacobi equations
2014 (English)In: Nonlinear Analysis, ISSN 0362-546X, E-ISSN 1873-5215, Vol. 110, p. 113-129Article in journal (Refereed) Published
Abstract [en]

We study propagation of singularities for Hamilton-Jacobi equations S t+H(t,x,λS)=0,(t,x)∈(0,∞)×ℝn, by means of the excess Lagrangian action and a related class of characteristics. In a sense, the excess action gauges how far a curve X(t) is from being action minimizing for a given viscosity solution S(t,x) of the Hamilton-Jacobi equation. Broken characteristics are defined as curves along which the excess action grows at the slowest pace possible. In particular, we demonstrate that broken characteristics carry the singularities of the viscosity solution.

Keywords
Generalized characteristic, Hamilton-Jacobi equation, Propagation of singularities, Mathematical techniques, Nonlinear analysis, Hamilton Jacobi equations, Lagrangian, Viscosity solutions, Mechanics
National Category
Mathematical Analysis
Identifiers
urn:nbn:se:mdh:diva-25906 (URN)10.1016/j.na.2014.08.001 (DOI)000342386300009 ()2-s2.0-84906675945 (Scopus ID)
Available from: 2014-09-12 Created: 2014-09-12 Last updated: 2017-12-05Bibliographically approved
Ahmadzadeh, F. & Lundberg, J. (2014). Remaining useful life estimation: Review [Review]. International Journal of Systems Assurance Engineering and Management, 5(4), 461-474
Open this publication in new window or tab >>Remaining useful life estimation: Review
2014 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 5, no 4, p. 461-474Article, book review (Other academic) Published
Abstract [en]

This paper reviews the recent modelling developments in estimating the remaining useful life (RUL) of industrial systems. The RUL estimation models are categorized into experimental, data driven, physics based and hybrid approaches. The paper reviews some typical approaches and discusses their advantages and disadvantages. According to the literature, the selection of the best model depends on the level of accuracy and availability of data. In cases of quick estimations which are less accurate, the data driven method is preferred, while the physics based approach is applied when the accuracy of estimation is important.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-26430 (URN)10.1007/s13198-013-0195-0 (DOI)
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved
Ahmadzadeh, F. & Lundberg, J. (2013). Application of multi regressive linear model and neural network for wear prediction of grinding mill liners. International Journal of Advanced Computer Sciences and Applications, 4(5), 52-58
Open this publication in new window or tab >>Application of multi regressive linear model and neural network for wear prediction of grinding mill liners
2013 (English)In: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 4, no 5, p. 52-58Article in journal (Refereed) Published
Abstract [en]

The liner of an ore grinding mill is a critical component in the grinding process, necessary for both high metal recovery and shell protection. From an economic point of view, it is important to keep mill liners in operation as long as possible, minimising the downtime for maintenance or repair. Therefore, predicting their wear is crucial. This paper tests different methods of predicting wear in the context of remaining height and remaining life of the liners. The key concern is to make decisions on replacement and maintenance without stopping the mill for extra inspection as this leads to financial savings. The paper applies linear multiple regression and artificial neural networks (ANN) techniques to determine the most suitable methodology for predicting wear. The advantages of the ANN model over the traditional approach of multiple regression analysis include its high accuracy.

National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-26429 (URN)10.14569/IJACSA.2013.040509 (DOI)
Available from: 2014-11-03 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved
Ahmadzadeh, F., Lundberg, J. & Strömberg, T. (2013). Multivariate process parameter change identification by neural network. The International Journal of Advanced Manufacturing Technology, 69(9-12), 2261-2268
Open this publication in new window or tab >>Multivariate process parameter change identification by neural network
2013 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 69, no 9-12, p. 2261-2268Article in journal (Refereed) Published
Abstract [en]

Whenever there is an out-of-control signal in process parameter control charts, maintenance engineers try to diagnose the cause near the time of the signal which does not always lead to prompt identification of the source(s) of the out-of-control condition, and this in some cases yields to extremely high monetary loses for the manufacturer owner. This paper applies multivariate exponentially weighted moving average (MEWMA) control charts and neural networks to make the signal identification more effective. The simulation of this procedure shows that this new control chart can be very effective in detecting the actual change point for all process dimension and all shift magnitudes considered. This methodology can be used in manufacturing and process industries to predict change points and expedite the search for failure causing parameters, resulting in improved quality at reduced overall cost. This research shows development of MEWMA by usage of neural network for identifying the step change-point and the variable responsible for the change in the process mean vector.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-26431 (URN)10.1007/s00170-013-5200-x (DOI)000327095900030 ()2-s2.0-84892371787 (Scopus ID)
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-8524-3321

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