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Publications (10 of 18) Show all publications
Nourmohammadi, A., Fathi, M., Ruiz Zúñiga, E. & Ng, A. H. C. (2019). A Genetic Algorithm for Bi-Objective Assembly Line Balancing Problem. In: Yan Jin, Mark Price (Ed.), Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK. Paper presented at 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK (pp. 519-524). Amsterdam: IOS Press, 9
Open this publication in new window or tab >>A Genetic Algorithm for Bi-Objective Assembly Line Balancing Problem
2019 (English)In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 519-524Conference paper, Published paper (Refereed)
Abstract [en]

Assembly line designs in manufacturing commonly face the key problem of dividing the assembly tasks among the working stations so that the efficiency of the line is optimized. This problem is known as the assembly line balancing problem which is known to be NP-hard. This study, proposes a bi-objective genetic algorithm to cope with the assembly line balancing problem where the considered objectives are the utilization of the assembly line and the workload smoothness measured as the line efficiency and the variation of workload, respectively. The performance of the proposed genetic algorithm is tested through solving a set of standard problems existing in the literature. The computational results show that the genetic algorithm is promising in providing good solutions to the assembly line balancing problem.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2019
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 9
Keywords
Assembly line balancing, bi-objectives, Genetic Algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17679 (URN)10.3233/ATDE190091 (DOI)978-1-64368-008-8 (ISBN)978-1-64368-009-5 (ISBN)
Conference
17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK
Projects
This study is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project.
Funder
EU, Horizon 2020, 723711
Available from: 2019-09-13 Created: 2019-09-13 Last updated: 2019-11-05Bibliographically approved
Beheshtinia, M. A., Ahmadi, B. & Fathi, M. (2019). A Genetic Algorithm with Multiple Populations to Reduce Fuel Consumption in Supply Chain. International Journal of Transportation Engineering
Open this publication in new window or tab >>A Genetic Algorithm with Multiple Populations to Reduce Fuel Consumption in Supply Chain
2019 (English)In: International Journal of Transportation Engineering, ISSN 2322-259XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Reducing fuel consumption by transportation fleet in a supply chain, reduces transportation costs and consequently, the product final cost. Moreover, it reduces environmental pollution, and in some cases, it helps governments constitute less subsidies for fuels. In this paper, a supply chain scheduling is studied, with the two objective functions of minimizing the total fuel consumption, and the total order delivery time. After presenting the mathematical model of the problem, a genetic algorithm, named Social Genetic Algorithm (SGA) is proposed to solve it. The proposed algorithm helps decision makers determine the allocation of orders to the suppliers and vehicles and production and transportation scheduling to minimize total order delivery time and fuel consumption. In order for SGA performance evaluation, its results are compared with another genetic algorithm in the literature and optimal solution. Finally, a sensitivity analysis is performed on SGA. The results of comparisons also show the high performance of SGA. Moreover, by increasing the number of suppliers and vehicles and decreasing the number of orders, the value of the objective function is reduced.

Place, publisher, year, edition, pages
Tarahan Parseh Transportation Research Institute, 2019
Keywords
Transportation, Fuel consumption, Supply chain management, Routing, Genetic algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17494 (URN)10.22119/IJTE.2019.134126.1410 (DOI)
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-11-13Bibliographically approved
Nourmohammadi, A., Fathi, M., Zandieh, M. & Ghobakhloo, M. (2019). A water-flow like algorithm for solving U-shaped assembly line balancing problems. IEEE Access, 7, 129824-129833
Open this publication in new window or tab >>A water-flow like algorithm for solving U-shaped assembly line balancing problems
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 129824-129833Article in journal (Refereed) Published
Abstract [en]

The problem of assigning assembly tasks to the stations arranged along a material handling device is known as assembly line balancing. This paper aims to address the U-shaped assembly line balancing problem (UALBP) which arises when a U-shaped assembly line has to be configured. It is widely known that this problem is NP-hard. Accordingly, different meta-heuristics based on a single solution (such as Simulated Annealing) or a population of solutions (such as Genetic Algorithms) have been proposed in the literature. Meanwhile, it has been argued that either of these meta-heuristics with a fixed number of solutions cannot maintain efficient search progress and thus can lead to premature convergence. Thus, this study aims at adopting a novel meta-heuristic algorithm with dynamic population sizes, namely Water Flow-like Algorithm (WFA), inspired by the behaviour of water flows in nature, to address the UALBP. The line efficiency and variation of workload are considered as the primary and the secondary objective, to be optimized, respectively. To verify the efficiency and robustness of the proposed WFA, a real case study taken from an automobile manufacturer as well as a set of standard problems are solved and the results compared with the existing approaches in the literature. The computational results demonstrate the superiority of the WFA, particularly in addressing medium to large-sized problems.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
U-shaped, assembly line balancing, water flow-like algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17656 (URN)10.1109/ACCESS.2019.2939724 (DOI)000487236000016 ()
Available from: 2019-09-07 Created: 2019-09-07 Last updated: 2019-10-11Bibliographically approved
Fathi, M., Nourmohammadi, A., Ng, A. H. C., Syberfeldt, A. & Eskandari, H. (2019). An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem. Engineering computations
Open this publication in new window or tab >>An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem
Show others...
2019 (English)In: Engineering computations, ISSN 0264-4401, E-ISSN 1758-7077Article in journal (Refereed) Epub ahead of print
Abstract [en]
  • Purpose – This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision makers aim to design an efficient assembly line while satisfying a set of constraints.
  • Design/methodology/approach – An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP in order to optimize the number of stations and the workload smoothness.
  • Findings – To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.
  • Originality/value – The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is employed in the IGA to enhance its local search capability.
Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019
Keywords
assembly line balancing, genetic algorithm, variable neighborhood search, generation transfer
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17157 (URN)10.1108/EC-02-2019-0053 (DOI)2-s2.0-85071617279 (Scopus ID)
Projects
This study is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project.
Funder
EU, Horizon 2020, 723711
Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-09-24Bibliographically approved
Fathi, M., Ghobakhloo, M. & Syberfeldt, A. (2019). An Interpretive Structural Modeling of Teamwork Training in Higher Education. Education Sciences, 9(1), 1-20
Open this publication in new window or tab >>An Interpretive Structural Modeling of Teamwork Training in Higher Education
2019 (English)In: Education Sciences, E-ISSN 2227-7102, Vol. 9, no 1, p. 1-20Article in journal (Refereed) Published
Abstract [en]

In the past decade, the importance of teamwork training in higher education and employers’ enthusiasm for recruiting team players have been widely discussed in the literature. Yet, the process through which effective teamwork training is developed in a higher education setting has not yet been properly discussed. The present study aims to map the precedence relationships among the key determinants of teamwork training effectiveness and explain the process through which an effective teamwork training program can be developed. The study first conducted an extensive review of the literature to highlight the key determinants of effective teamwork training. Next, the study benefitted from an interpretive structural modeling technique and captured the opinions of a group of teamwork training experts to further map the interrelationships among the potential determinants that were identified. By listing the key determinants of effective teamwork training, mapping their interrelationships, and identifying their driving and dependence power, the present study is expected to help practitioners and academicians through providing a detailed understanding of the process through which an effective teamwork training program can be developed in a higher education context.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2019
Keywords
teamwork, higher education, determinants, interpretive structural modeling
National Category
Pedagogy
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16568 (URN)10.3390/educsci9010016 (DOI)000464170900001 ()2-s2.0-85061216921 (Scopus ID)
Available from: 2019-01-16 Created: 2019-01-16 Last updated: 2019-05-09Bibliographically approved
Fathi, M., Nourmohammadi, A., Ng, A. H. C. & Syberfeldt, A. (2019). An optimization model for balancing assembly lines with stochastic task times and zoning constraints. IEEE Access, 7, 32537-32550, Article ID 8663269.
Open this publication in new window or tab >>An optimization model for balancing assembly lines with stochastic task times and zoning constraints
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 32537-32550, article id 8663269Article in journal (Refereed) Published
Abstract [en]

This study aims to bridge the gap between theory and practice by addressing a real-world assembly line balancing problem (ALBP) where task times are stochastic and there are zoning constraints in addition to the commonly known ALBP constraints. A mixed integer programming (MIP) model is proposed for each of the straight and U-shaped assembly line configurations. The primary objective in both cases is to minimize the number of stations; minimizing the maximum of stations’ mean time and the stations’ time variance are considered secondary objectives. Four different scenarios are discussed for each model, with differences in the objective function. The models are validated by solving a real case taken from an automobile manufacturing company and some standard test problems available in the literature. The results indicate that both models are able to provide optimum solutions for problems of different sizes. The technique for order preference by similarity to ideal solution (TOPSIS) is used to create reliable comparisons of the different scenarios and valid analysis of the results. Finally, some insights regarding the selection of straight and U-shaped layouts are provided.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
assembly line balancing, mathematical programming, stochastic, zoning constraints
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16689 (URN)10.1109/ACCESS.2019.2903738 (DOI)000463040400001 ()2-s2.0-85063577558 (Scopus ID)
Projects
This study is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project.
Funder
EU, Horizon 2020, 723711
Available from: 2019-03-09 Created: 2019-03-09 Last updated: 2019-05-09Bibliographically approved
Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2019). Choosing efficient meta-heuristics to solve the assembly line balancing problem: A landscape analysis approach. Paper presented at 52nd CIRP Conference on Manufacturing Systems, Ljubljana, Slovenia, June 12-14, 2019. Procedia CIRP, 81, 1248-1253
Open this publication in new window or tab >>Choosing efficient meta-heuristics to solve the assembly line balancing problem: A landscape analysis approach
2019 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 81, p. 1248-1253Article in journal (Refereed) Published
Abstract [en]

It is widely known that the assembly line balancing problem (ALBP) is an NP-hard optimization problem. Although different meta-heuristics have been proposed for solving this problem so far, there is no convincing support that what type of algorithms can perform more efficiently than the others. Thus, using some statistical measures, the landscape of the simple ALBP is studied for the first time in the literature. The results indicate a flat landscape for the problem where the local optima are uniformly scattered over the search space. Accordingly, the efficiency of population-based algorithms in addressing the considered problem is statistically validated.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Assembly line balancing, fitness landscape analysis, meta-heuristic algorithms
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17280 (URN)10.1016/j.procir.2019.03.302 (DOI)2-s2.0-85068480053 (Scopus ID)
Conference
52nd CIRP Conference on Manufacturing Systems, Ljubljana, Slovenia, June 12-14, 2019
Projects
This study is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project
Funder
EU, Horizon 2020, 723711
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-08-19Bibliographically approved
Ghobakhloo, M. & Fathi, M. (2019). Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management
Open this publication in new window or tab >>Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing
2019 (English)In: Journal of Manufacturing Technology Management, ISSN 1741-038X, E-ISSN 1758-7786Article in journal (Refereed) Epub ahead of print
Abstract [en]

purpose– The study demonstrates how small manufacturing firms can leverage their Information Technology (IT) resources to develop the lean-digitized manufacturing system that offers sustained competitiveness in the Industry 4.0 era.

Design/methodology/approach – The study performs an in-depth 5-years case study of a manufacturing firm, and reports its journey from failure in the implementation of enterprise resource planning to its success in integrating IT-based technology trends of Industry 4.0 with the firm’s core capabilities and competencies while pursuing manufacturing digitization.

Findings – Industry 4.0 transition requires the organizational integration of many IT-based modern technologies and the digitization of entire value chains. However, Industry 4.0 transition for smaller manufacturers can begin with digitization of certain areas of operations in support of organizational core strategies. Development of leandigitized manufacturing system is a viable business strategy for corporate survivability in the Industry 4.0 setting.

Research limitations/implications – Although the implementation of lean-digitized manufacturing system is costly and challenging, this manufacturing strategy offers superior corporate competitiveness in the long run. Since this finding is rather limited to the present case study, assessing the business value of lean-digitized manufacturing system in a larger-scale research context would be an interesting avenue for future research.

Practical implications – Industry 4.0 transition for typical manufacturers should commensurate with their organizational, operational, and technical particularities. Digitization of certain operations and processes, when aligned with the firm’s core strategies, capabilities, and procedures, can offer superior competitiveness even in Industry 4.0 era, meaning that the strategic plan for successful Industry 4.0 transition is idiosyncratic to each particular manufacturer.

Social implications – Manufacturing digitization can have deep social implications as it alters inter and intra organizational relationships, causes unemployment among low-skilled workforce, and raises data security and privacy concerns. Manufacturers should take responsibility for their digitization process and steer it in a direction that simultaneously safeguards economic, social, and environmental sustainability.

Originality/value – The strategic roadmap devised and employed by the case company for managing its digitization process can better reveal what manufacturing digitization, mandated by Industry 4.0, might require of typical manufacturers, and further enable them to better facilitate their digital transformation process.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019
Keywords
Industry 4.0, Information Technology, Lean manufacturing, Digitization, Manufacturing performance
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17158 (URN)10.1108/JMTM-11-2018-0417 (DOI)2-s2.0-85070289468 (Scopus ID)
Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-08-23Bibliographically approved
Nourmohammadi, A., Eskandari, H. & Fathi, M. (2019). Design of stochastic assembly lines considering line balancing and part feeding with supermarkets. Engineering optimization (Print), 51(1), 63-83
Open this publication in new window or tab >>Design of stochastic assembly lines considering line balancing and part feeding with supermarkets
2019 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 51, no 1, p. 63-83Article in journal (Refereed) Published
Abstract [en]

This article aims to address the assembly line balancing problem (ALBP) and supermarket location problem (SLP) as two long-term interrelated decision problems considering the stochastic nature of the task times and demands. These problems arise in real-world assembly lines during the strategic decision-making phase of configuring new assembly lines from both line balancing and part-feeding (PF) aspects. A hierarchical mathematical programming model is developed, in which the first level resolves the stochastic ALBP by minimizing the workstation numbers and the second level deals with the stochastic SLP while optimizing the PF shipment, inventory and installation costs. The results of case data from an automotive parts manufacturer and a set of standard test problems verified that the proposed model can optimize the configuration of assembly lines considering both ALBP and SLP performance measures. This study also validates the effect of the stochastic ALBP on the resulting SLP solutions.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2019
Keywords
Assembly line balancing, stochastic, part feeding, supermarket, mathematical programming
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15106 (URN)10.1080/0305215X.2018.1439944 (DOI)000450649100004 ()2-s2.0-85044034117 (Scopus ID)
Available from: 2018-04-27 Created: 2018-04-27 Last updated: 2019-03-29Bibliographically approved
Yousefi, M., Yousefi, M., Fathi, M. & Fogliatto, F. (2019). Patient visit forecasting in an emergency department using a deep neural network approach. Kybernetes
Open this publication in new window or tab >>Patient visit forecasting in an emergency department using a deep neural network approach
2019 (English)In: Kybernetes, ISSN 0368-492X, E-ISSN 1758-7883Article in journal (Refereed) Epub ahead of print
Abstract [en]

This study aims to investigate factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to 7 days.In this study, first the important factors to influence the demand in EDs were extracted from literature then the relevant factors to our study are selected. Then a deep neural network is applied for constructing a reliable predictor.Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable through employing the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), support vector regression (SVR), generalized linear models (GLM), generalized estimating equations (GEE), seasonal ARIMA (SARIMA) and combined ARIMA and linear regression (LR) (ARIMA-LR).We applied this study in a single ED to forecast the patient visits. Applying the same method in different EDs may give us a better understanding of the performance of the model. The same approach can be applied in any other demand forecasting after some minor modifications.To the best of our knowledge, this is the first study to propose the use of long short-term memory (LSTM) for constructing a predictor of the number of patient visits in EDs.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019
Keywords
Patient Visit Forecasting, Deep Neural Networks, Long Short-term Memory, Emergency Department
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17635 (URN)10.1108/K-10-2018-0520 (DOI)
Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-11-06Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5530-3517

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