Login | Register

Enterprise, project and workforce selection models for industry 4.0.

Title:

Enterprise, project and workforce selection models for industry 4.0.

Kaur, Rupinder (2018) Enterprise, project and workforce selection models for industry 4.0. Masters thesis, Concordia University.

[img]
Preview
Text (application/pdf)
Kaur_MAsc_S2018.pdf - Accepted Version
Available under License Spectrum Terms of Access.
2MB

Abstract

Abstract
Enterprise, project, and workforce selection models for Industry 4.0.
Rupinder Kaur
The German federal government first coined industry 4.0 in 2011. Industry 4.0 involves the use of
advanced technologies such as cyber-physical system, internet of things, cloud computing, and
cognitive computing with the aim to revolutionize the current manufacturing practices.
Automation and exchange of big data and key characteristics of Industry 4.0. Due to its numerous
benefits, industries are readily investing in Industry 4.0, but this implementation is an uphill
struggle.
In this thesis, we address three key problems related to Industry 4.0 implementation namely
Enterprise selection, Project selection and Workforce selection. The first problem involves
identification of enterprises suitable for Industry 4.0 implementation. The second problem involves
prioritization and selection of Industry 4.0 projects for the chosen digital enterprises. The third and
last problem involves workforce selection and assignment for execution of the identified Industry
4.0 projects. Multicriteria solution approaches based on TOPSIS and Genetic Algorithms are
proposed to address these problems. Industry experts are involved to prioritize the criteria used for
enterprise, project and workforce selection. Numerical applications are provided.
The proposed work is innovative and can be useful to manufacturing and service organizations
interested in implementing Industry 4.0 projects for performance improvement.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Kaur, Rupinder
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:6 August 2018
Thesis Supervisor(s):awasthi, Dr. anjali
ID Code:984481
Deposited By: Rupinder Kaur
Deposited On:16 Nov 2018 16:47
Last Modified:16 Nov 2018 16:47

References:

References
[1] Alaba F., Othman M., Hashem I., Alotaibi F. (2017). Internet of things security: A
survey. Journal of Network and Computer Applications, 88, 10-28.
[2] Alba, E., Laguna, M., Luque, G. Workforce planning with parallel genetic algorithm.
Retrieved February 2018 from
https://pdfs.semanticscholar.org/4b4e/aae134c26f9e530d7b768410d1aad5c819ea.pdf
[3] Abotaleb, I., Moussa, M., Hussain, S. (2014). Optimization of Allocating Multi-Skilled
Labor Resources Using Genetic Algorithms. CSCE General Conference, 175, 1-9.
[4] Afshari, A., Mojahed, M., & Yusuff, R. M. (2010). Simple additive weighting approach
to personnel selection problem. International Journal of Innovation, Management and
Technology, 1(5), 511.
[5] Albach, H., Meffert, H., Pinkwart, A., Reichwald, R. Management of permanent
change. Retrieved from https://link.springer.com/book/10.1007/978-3-658-05014-6.
[6] Algethami, H., Pinheiro, R.L., Landa-Silva, D. (2016). A Genetic Algorithm for a
Workforce Scheduling and Routing Problem. IEEE Congress on Evolutionary
Computation(CEC),927-934. Retrieved February 2018 from
https://ieeexplore.ieee.org/document/7743889/.
[7] Alguliyev, R. M., Aliguliyev, R. M., & Mahmudova, R. S. (2015). Multicriteria
personnel selection by the modified fuzzy VIKOR method. The Scientific World
Journal, Retrieved December 2018,
https://www.hindawi.com/journals/tswj/2015/612767/abs/.
[8] Askin, R.G., Huang, Y. (1997). Employee training and assignment for facility
reconfiguration. Proceedings of Industrial Engineering Research Conference,426.
Retrieved March 2018 from https://asu.pure.elsevier.com/en/publications/employeetraining-
and-assignment-for-facility-reconfiguration.
[9] Asl, M.B., Khalolzadeh, A., Youshanlouei, H.R., Mood, M.M. (2012). Identifying and
ranking the effective factors on selecting Enterprise Resource Planning(ERP) system
using the combined Delphi and Shannon Entripy approach. Procedia- Social and
Behavioural Sciences, 41,513-520.
[10] Baležentis, A., Baležentis, T., & Brauers, W. K. (2012). Personnel selection
based on computing with words and fuzzy MULTIMOORA. Expert Systems with
applications, 39(9), 7961-7967.
[11] Barlatt, A.Y. (2009). Models and algorithms for workforce allocation and
utilization (Doctoral dissertation), University of Michigan. Retrieved January 2018
from https://deepblue.lib.umich.edu/handle/2027.42/63864.
[12] Benesova, A., and Tupa, J. (2017). Requirements for education and
qualification of people in industry 4.0. 27th International Conference on Flexible
Automation and Intelligent Manufacturing, FAIM2017, 11, 2195-2202.
[13] Bhadury, J., Mighty, EJ., Damar, H. (2000). Maximizing workforce diversity in
project teams: A network flow approach. International Journal of Management
Science, 28,143–153.
[14] Bley, K., Leyh, C. (2016). Digitization of German enterprises in the production
sector- do they know how digitized they are.22nd Americas Conference on Information
System,1-10. Retrieved November 2017 from
https://www.researchgate.net/publication/305661673_Digitization_of_German_Enter
prises_in_the_Production_Sector_-_Do_they_know_how_digitized_they_are.
[15] Brettel M., Friederichsen N., Keller M., Rosenberg M. (2014). How
virtualization, decentralization and network building change the manufacturing
landscape: an industry 4.0 perspective. World Academy of Science, engineering and
79
Technology International journal of information and communication engineering, 8(1),
37-44.
[16] Bruecker P., Bergh J., Belien J., Demeulemeester E. (2015). Workforce
planning incorporating skills: State of the art. European Journal of operational
Research, 243(1), 1-16.
[17] Bohanec, M., Rajkovi, V. Knowledge acquisition and explanation for multiattribute
decision making, Yugoslavia. Retrieved from
http://kt.ijs.si/MarkoBohanec/pub/Avignon88.pdf.
[18] Boran, F. E., Genç, S., & Akay, D. (2011). Personnel selection based on
intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service
Industries, 21(5), 493-503.
[19] Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria
intuitionistic fuzzy group decision making for supplier selection with TOPSIS method.
Expert Systems with Applications, 36(8), 11363-11368.
[20] Bouajaja, S., Dridi, N. (2017). A survey on human resource allocation problem
and its applications. Operational Research, 17(2), 339-369.
[21] Buer, S. V., Strandhagen, J. O., & Chan, F. T. (2018). The link between Industry
4.0 and lean manufacturing: mapping current research and establishing a research
agenda. International Journal of Production Research, 56(8), 2924-2940.
[22] Cai, X., Li, K.N. (2000). A genetic algorithm for scheduling staff of mixed skills
under multi-criteria. European Journal of Operational Research, 125(2), 359-369.
[23] Celaschi, F. (2017). Advanced design-driven approaches for an industry 4.0
framework: The human-centred dimension of the digital industrial revolution. Strategic
Design Research Journal, 10, 97-104.
[24] Celik, N., Lee, S., Mazhari, E., Son, YJ., Lemaire, R., Provan, K. (2011),
Simulation-based workforce assignment in a multi-organizational social network for
alliance-based software development. Simulation Modelling Practice and Theory, 19,
2169–2188.
[25] Cert, A., Enea, M., Galante, G., Manuela, C. (2009). Multi-objective human
resources allocation in R & D projects planning. International Journal of Production
Research, 47(13), 3503-3523.
[26] Cesani, V.I., Steudel, H.J. (2005). A study of labor assignment flexibility in
cellular manufacturing systems. Computers and Industrial Engineering, 48,571-591.
[27] Chen, J.Y., Tai, K.C., Chen, G.C. (2017). Application of programmable logic
controller to build-up an intelligent industry 4.0 platform. Procedia CIRP, 63,150-155.
[28] Chryssolouris, G., Mavrikios, D., Papakostas, N., Mourtzis, D., Michalos, G.,
Georgoulias, K. (2009). Digital manufacturing: History, perspectives, and outlook.
Proceedings of Institution of Mechanical Engineers, 223,451-462.
[29] Colarelli, S.M., Boos, A.L. (1992). AL. Sociometric and ability-based
assignment to work groups: Some implications for personnel selection. Journal of
Organizational Behaviour, 13,187–196.
[30] Collard, P., Gaspar. A., Clergue, M., Escazut, C. (1998). Fitness Distance
Correlation, as statistical measure of Genetic Algorithm difficulty, revisited. European
conference on Artificial Intelligence.927-93. Retreived March 2018from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.28.2481&rep=rep1&type=p
df.
[31] Crispim, J. A., & Pinho de Sousa, J. (2009). Partner selection in virtual
enterprises: a multi-criteria decision support approach. International Journal of
Production Research, 47(17), 4791-4812.
80
[32] Cuevas, R., Ferrer, J. C., Klapp, M., Muñoz, J. C. (2016). A mixed integer
programming approach to multi-skilled workforce scheduling. Journal of Scheduling,
19(1), 91-106.
[33] Dağdeviren, M. (2010). A hybrid multi-criteria decision-making model for
personnel selection in manufacturing systems. Journal of Intelligent Manufacturing,
21(4), 451-460.
[34] Dağdeviren, M., & Yüksel, İ. (2007). Personnel selection using analytic
network process. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 6(11), 99-118.
[35] Daghouri, A., Mansouri, K., Qbadou, M. (2018). Information system
performance evaluation and optimization using AHP and TOPSIS: Construction
industry case. 4th International Conference on Optimization and Applications(ICOA).
Retrieved June 2018 from https://ieeexplore.ieee.org/document/8370589/.
[36] Dilberoglu, U.M., Gharehpapagh, B., Yaman, U., Dolen, M.et al. (2017). The
role of additive manufacturing in the era of Industry 4.0. 27th International
Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017,
11,545-554.
[37] Dulmin, R., Mininno, V. (2003). Supplier selection using a multi-criteria
deciaion aid method. Journal of Purchasing and Supply Management .9(4), 177-187.
[38] Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel
selection. Expert Systems with applications, 37(6), 4324-4330.
[39] Efe, B. (2016). An integrated fuzzy multi criteria group decision making
approach for ERP system selection. Applied Soft Computing. 38, 106-117.
[40] Eitzen, G, Panton, D, Mills, G. (2004). Multi-Skilled Workforce Optimization.
Annals of Operations Research, 217, 359–372.
[41] Erol, S., Schumacher, A., Sihn, W. (2015). Strategic guidance towards Industry
4.0 – a three-stage process model. International Conference on Competitive
Manufacturing, Retrieved September 2017 from
http://publica.fraunhofer.de/documents/N-382579.html.
[42] Farahani et al. (2016). Car sales forecasting using neural networks and
analytical hierarchy process. The fifth international conference on Data analytics,
USA, 57-62.
[43] Fettermann, D. C., Cavalcante, C. G. S., Almeida, T. D. D., & Tortorella, G. L.
(2018). How does Industry 4.0 contribute to operations management?. Journal of
Industrial and Production Engineering, 35(4), 255-268.
[44] Flatt, H., Schriegel, S., Jasperneite, J., Trsek, H., Adamczyk, H. (2016).
Analysis of the Cyber-Security of industry 4.0 technologies based on RAMI 4.0 and
identification of requirements. IEEE 21st International Conference on Emerging
Technologies and Factory Automation (ETFA). Retrieved March 2018 from
https://ieeexplore.ieee.org/document/7733634/.
[45] Forman, E., Peniwati, K., (1998). Aggregating individual judgements and
priorities with the analytical hierarchy process. European Journal of Operational
Research, 108(1), 165-169.
[46] Fini, A.A.F., Rashidi, T.H., Akbarnezhad, A., Waller S.T. (2015). Incorporating
multiskilling and learning in the optimization of crew composition. Journal of
Construction Engineering and Management, 142(5), 1-14.
[47] FitzHugh, K.L.M., Piercy, N.F. (2013). Does collaboration between sales and
marketing affect business performance? 207-220. Retrieved May 2018 from
https://www.tandfonline.com/doi/abs/10.2753/PSS0885-3134270301.
81
[48] Fowler, J.F., Wirojanagud, P., Gel, E.S. (2008), Heuristics for workforce
planning with worker differences. European Journal of Operational Research, 190,
724–740.
[49] Geissbauer, R., Schrauf, S. (2018). Industry 4.0: building the digital enterprise.
Global Digital Operations Study, Retrieved August 2017 from
https://www.pwc.com/gx/en/industries/industry-4-0.html.
[50] Gerbert, P., Lorenz, M., Rubmann, M. (2015). Industry 4.0: the future of
productivity and growth in manufacturing industries. The Boston Consulting Group.
Retrieved August 2017 from
https://www.bcg.com/enca/publications/2015/engineered_products_project_business_
industry_4_future_productivity_growth_manufacturing_industries.aspx.
[51] Gokalp, M.O., Kayabay, K., Akyol, M.A., Eren, P.E., Kocyigit, A. (2016). Big
data for industry 4.0: A conceptual framework. International Conference on
Computational Science and Computational Intelligence(CSCI), 431-434.
[52] Gomar, JE., Haas, CT., Morton, DP. (2002). Assignment and allocation
optimization of partially multiskilled workforce. Journal of Construction Engineering
and Management, 128, 103-109.
[53] Gorecky, D., Schmitt, M., Loskyll, M., Zuhlke, D. (2014). Human-machineinteraction
in the industry 4.0 era. 12th IEEE International Conference on Industrial
Informatics(INDIN). Retreived June 2018 from from
https://www.researchgate.net/publication/286571545_Human-machineinteraction%
20in%20the%20industry%2040%20era.
[54] Gunasekaran A., Subramanian N., Papadopoulos T. (2017). Information
technology for competitive advantage within logistics and supply chains: A review.
Transportation Research Part E: Logistics and Transportation Review, 99, 14-33.
[55] Guoping, L.,Yun, H., Aizhi, W. (2017). Fourth industrial revolution:
Technological drivers, impacts and coping methods. Published in www.Springer.com.
27(4), 626-637.
[56] Haddara, M. (2014). ERP selection: The smart way. Procedia Technology .16,
394-403.
[57] Han, D., Han, I. (2004). Prioritization and selection of intellectual capital
measurement indicators using analytic hierarchy process for the mobile
telecommunications industry. Expert Systems with Applications. 26, 519-527.
[58] Haas, C. T., Rodriguez, A. M., Glover, R., Goodrum, P. M. (2001).
Implementing a multiskilled workforce. Construction Management & Economics,
19(6), 633-641.
[59] Harbi, K.M. (2001). Application of the AHP in project management,
International Journal of Project Management, 19, 19-27.
[60] Hasan, M. A., Shankar, R., & Sarkis, J. (2008). Supplier selection in an agile
manufacturing environment using data envelopment analysis and analytical network
process. International Journal of Logistics Systems and Management, 4(5), 523-550.
[61] Hecklau F., Galeitzkea M., Flachsa S., Kohlb H. (2016). Holistic approach for
human resource management in industry 4.0. Procedia CIRP, 54, 1-6.
[62] Hermann, M., Pentek, T., Otto, B. (2016). Design principles for industry 4.0
scenarios. 49th Hawaii International Conference on System Sciences (HICSS), 3928-
3937. Retrieved March 2018 from https://ieeexplore.ieee.org/document/7427673/.
[63] Hopp, WJ., Van, MP. (2004). Agile workforce evaluation: A framework for
cross-training and coordination. IIE Transaction, 36,919.
82
[64] Hyari, K., El-Mashaleh, M., Kandil, A. (2010). Optimal assignment of
multiskilled labor in building construction projects. International Journal of
construction education and research. 6(1), 70-80.
[65] Hyldegard, J. (2006). Collaborative information behaviour exploring
Kuhlthau’s information search process model in a group-based educational setting.
Information Processing and Management, 42,276–298.
[66] Joshi, A., Jackson, S.E. (2003). Managing workforce diversity to enhance
cooperation in organizations. International Handbook of Organizational Teamwork
and Cooperative Working, 277–296. Retrieved April 2018 from
https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470696712.ch14.
[67] Ji, P., Zhang, H. Y., & Wang, J. Q. (2018). A projection-based TODIM method
under multi-valued neuromorphic environments and its application in personnel
selection. Neural Computing and Applications, 29(1), 221-234.
[68] Jun (2010). Multi-objective optimization for resource driven scheduling in
construction projects. University of Illinois at Urbana-Champaign. Retrieved April
2018 from https://core.ac.uk/download/pdf/4825454.pdf.
[69] Kabak, M. (2013). A Fuzzy DEMATEL-ANP Based Multi Criteria Decision
Making Approach For Personnel Selection. Journal of Multiple-Valued Logic & Soft
Computing, 20(5), 571-593.
[70] Karre H., Hammera M., Kleindiensta M., Ramsauera, C. (2017). Transition
towards an Industry 4.0 state of the Lean Lab at Graz University of Technology.7th
Conference on Learning Factories, 9,206-213.
[71] Karsak, E. E. (2001). Personnel selection using a fuzzy MCDM approach based
on ideal and anti-ideal solutions. In Multiple criteria decision making in the new
millennium ,393-402. Retrieved November 2018 from
https://link.springer.com/chapter/10.1007/978-3-642-56680-6_36.
[72] Ketabchi, S., Moosaei, H., Fallahi, S. (2013). Optimal error correction of the
absolute value equation using a genetic algorithm. Mathematical and Computer
Modelling, 57, 2339-2342.
[73] Khan M., Salah K. (2018). IoT security: Review, block chain solutions, and
open challenges. Future Generation Computer Systems, 82,395-411.
[74] Killic, H.S., Zaim, S., Delen, D. (2015). Selecting “The Best” ERP system for
SMEs using a combination of ANP and PROMETHEE methods. Expert Systems with
applications.42(5), 2343-2352.
[75] Kim, H., Hwang, Y., Kim, K. (2014). Study on the application of multi-skilled
labors to factory production process for securing economic feasibility of modular unit.
Korean Journal of Construction Engineering and Management, 15(1), 11-19.
[76] Kinzel, H. Industry 4.0- Where does this leave the human factor. Retrieved
October 2017 from https://tci-thaijo.org/index.php/JUCR/article/view/108399/85751.
[77] Konak, A., Coit, D.W., Smith, A.E. (2006). Multi-objective
optimization using genetic algorithms: A tutorial. Reliability Engineering & System
Safety, 91,992-1007.
[78] Koutra, G., Barbounaki, S., Kardaras, D., & Stalidis, G. (2017, July). A
multicriteria model for personnel selection in maritime industry in Greece. In Business
Informatics (CBI), 2017 IEEE 19th Conference, Vol. 1, pp. 287-294.
[79] Kundakcı, N. (2016). Personnel selection with grey relational analysis.
Management Science Letters, 6(5), 351-360.
[80] Kusumawardani, R.P., Agintiara, M. (2015). Application of Fuzzy AHPTOPSIS
Method for Decision Making in Human Resource Manager Selection Process.
Procedia Computer Science, 72,638-646.
83
[81] Lee, J.W., Kim, S.H. (2001). An integrated approach for interdependent
information system project selection. International Journal of Project Management.
19(2), 111-118.
[82] Leyh, C., Schaffer, T., Bley, K., Forstenhausler, S. (2016). SIMMI 4.0- A
maturity model for classifying the enterprise-wide IT and software landscape focussing
on industry 4.0. Federated Conference on Computer Science and Information Systems
(FedCSIS), 8, 1297-1302.
[83] Liao, C.N., Kao, H.P. (2011). An integrated fuzzy TOPSIS and MCGP
approach to supplier selection in supply chain management. Expert Systems with
Applications. 38(9). 10803-10811.
[84] Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past,
present and future of Industry 4.0-a systematic literature review and research agenda
proposal. International journal of production research, 55(12), 3609-3629.
[85] Lill, I. (2009). Multiskilling in construction‐a strategy for stable employment.
Technological and Economic Development of Economy, 15(4), 540-560.
[86] LimaJunior, F.R., Osiro, L., Carpinetti, L.C.R. (2014). A comparison between
Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Journal of Applied Soft
Computing, 21, 194-209.
[87] Lin, C.M., Gen, M. (2008). Multi-criteria human resource allocation for solving
multistage combinatorial optimization problems using multi-objective hybrid genetic
algorithm. International Journal of Expert Systems with Applications, 34, 2480-2490.
[88] Lin, C.H. (2013). A rough penalty genetic algorithm for constrained
optimization. Journal of Information Sciences, 241, 119-137.
[89] Liu, C., Xu, X. (2017). Cyber-physical machine tool- the era of machine tool
4.0. The 50th CIRP conference on manufacturing system, 63, 70-75.
[90] Lin, H. T. (2010). Personnel selection using analytic network process and fuzzy
data envelopment analysis approaches. Computers & Industrial Engineering, 59(4),
937-944.
[91] Longo, F., Nicoletti, L., Padovano, A. (2017). Smart operators in industry 4.0:
A human-centred approach to enhance operators’ capabilities and competencies within
the new smart factory context. Computers and Industrial Engineering. 113,144-159.
[92] Lorenz, M., Gerbert, P., Rubmann, M. (2015). Industry 4.0: the future of
productivity and growth in manufacturing industries. Retrieved January 2018 from
https://www.bcg.com/enca/
publications/2015/engineered_products_project_business_industry_4_future_produ
ctivity_growth_manufacturing_industries.aspx.
[93] Luque, G.,Alba, E. (2005). Workforce Planning with a Parallel Genetic
Algorithm. Studies in Computational Intelligence, 367, 115-134.
[94] Masdefiol, R., del Mar, M., & Stävmo, F. (2016). Industry 4.0–Only designed
to fit the German automotive industry: A multiple case study on the feasibility of
Industry 4.0 to Swedish SMEs. Retrieved February 2018 from http://hj.divaportal.
org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=186&af=%5B%5D&search
Type=SIMPLE&query=&language=sv&pid=diva2%3A933316&aq=%5B%5B%7B
%22organisationId%22%3A%22139%22%7D%5D%5D&sf=all&aqe=%5B%5D&so
rtOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=689.
[95] Masoni, R., Ferrise, F., Bordegoni, M., Gattullo, M., Uva A.E., Fiorentino, M.,
Carrabba, E., Donato, M.D. (2017). Supporting remote maintenance in industry 4.0
through augmented reality. 27th International Conference on Flexible Automation and
Intelligent Manufacturing, FAIM201, 11, 1296-1302.
84
[96] Maturana, S., Alarcón, L. F., Deprez, M. (2003). Modelling the impact of
multiskilling and concrete batch size in multi-storey buildings. In Proceedings of the
XI Conference on Lean Construction, IGLC, 11,553-566.
[97] McCall, J. (2004). Genetic algorithms and modelling. Journal of Computational
and Applied Mathematics, 184,205-222.
[98] Meade, L.M., Presley, A. (2002). R & D project selection using the analytic
network process. IEEE Transactions on Engineering Management, 49(1), 59-66.
[99] Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press
Cambridge,205. Retrieved February 2018 from
https://mitpress.mit.edu/books/introduction-genetic-algorithms.
[100] Motyl B., Baronio G., Uberti S., Speranza D. (2017). How will change the future
engineer’s skills in the industry 4.0 framework? A questionnaire surveys. Procedia
Manufacturing, 11, 1501-1509.
[101] Naveh, Y, Richter, Y, Altshuler, Y, Gresh, DL, Connors, DP. (2007) Workforce
optimization: Identification and assignment of professional workers using constraint
programming. IBM Journal of Research and Development, 51,263–279.
[102] Nie, H., Liu. B. (2013). Combining MILP with Memetic Algorithm for
Scheduling and Staffing Construction Project with a Multi-skilled Workforce.
International Conference on Computational and Information Sciences (ICCIS), 1150-
1153. Retrieved October 2018 from https://ieeexplore.ieee.org/document/6643222/.
[103] Nowotarski, P., & Paslawski, J. (2017, October). Industry 4.0 concept
introduction into construction SMEs. In IOP Conference Series: Materials Science and
Engineering, 245(5). Retrieved December 2018 from
http://iopscience.iop.org/article/10.1088/1757-899X/245/5/052043.
[104] Nurcin, C., Xi, H., Xu, D., Son Y.J., Lemaire, R., Provan, K. (2011).
Simulation-based workforce assignment considering position in a social network,
Simulation, 88(1), 72 – 96. Retrieved February 2018 from
https://dl.acm.org/citation.cfm?id=2433909.
[105] Hester, P.T., Velasquez, M. (2013). An analysis of multi-criteria decisionmaking
methods. International Journal of Operations Research. 10(2). 56-66.
[106] Pereshybkina, A., Conde, M. E. C., & Kalyesubula, T. (2017). How will the
industry 4.0 transformations affect SMEs in Germany by 2030? Retrieved January 2018
from
https://opus.hsfurtwangen.de/.../Project+A_Scenario+Industry+4.0+and+SMEs_EK2.
[107] Pitic, L., Popescu, S., Pitic, D. (2014). Roadmap for ERP evaluation and
selection. Procedia Economics and Finance. 15, 1374-1382.
[108] Radziwon, A., Bilberg, A., Bogersa, M., Madsen, E.S. (2014). The Smart
Factory: Exploring Adaptive and Flexible Manufacturing. Procedia Engineering, 69,
1184-1190.
[109] Reid, D.J. (1996). Genetic Algorithms in Constrained Optimization. Journal of
Mathematical and Computer Modelling, 23, 87-111.
[110] Roblek, V., Meško, M., & Krapež, A. (2016). A complex view of industry 4.0.
Sage Open, 6(2). Retrieved January 2018 from
http://journals.sagepub.com/doi/abs/10.1177/2158244016653987.
[111] Ross, E. (2017). Cross-trained workforce planning models (Doctoral
dissertation, Lancaster University). Retrieved June 2018 from
http://www.research.lancs.ac.uk/portal/en/publications/crosstrained-workforceplanning-
models(bcb8d06c-be3f-4517-81e8-699953bd62a0).html.
[112] Russoa, F.S.M., Camanho, R. (2015). Criteria in AHP: A Systematic Review of
Literature, Procedia Computer Science, 55, 1123-1132.
85
[113] Saaty, T.L., Peniwati, K., Shang, J.S. (2007). The analytic hierarchy process
and human resource allocation: Half the story. Journal of Mathematical and Computer
Modelling, 46, 1041-1053.
[114] Sadeghzadeh K., Salehi M. (2011). Mathematical analysis of fuel cell strategic
technologies development solutions in the automotive industry by the TOPSIS multicriteria
decision-making method. International journal of Hydrogen Energy, 36(20),
13272-13280.
[115] Samaranyake, P., Ramanathan, K., Laosirihongthong, T. (2017). Implementing
Industry 4.0 - A Technological Readiness Perspective. International conference on
Industrial Engineering and Engineering Management (IEEM),529-533. Retrieved
October 2017 from https://ieeexplore.ieee.org/document/8289947/.
[116] Sampson, SE. (2006). Optimization of volunteer labor assignments. Journal of
Operations Management, 24, 363–377.
[117] Santos, M. Y., e Sá, J. O., Andrade, C., Lima, F. V., Costa, E., Costa, C., ... &
Galvão, J. (2017). A Big Data system supporting Bosch Braga Industry 4.0 strategy.
International Journal of Information Management, 37(6), 750-760.
[118] Sari, B., Sen, T., & Kilic, S. E. (2008). Ahp model for the selection of partner
companies in virtual enterprises. The International Journal of Advanced Manufacturing
Technology, 38(3-4), 367-376.
[119] Sarkis, J., Talluri, S., & Gunasekaran, A. (2007). A strategic model for agile
virtual enterprise partner selection. International Journal of Operations & Production
Management, 27(11), 1213-1234.
[120] Sepehr, A., Zucca, C. (2012). Ranking desertification indicators using TOPSIS
Algorithm. Natural Hazards: Journal of International Society for the Prevention and
Mitigation of Natural Hazards, 62, 1137-1153.
[121] Schumacher A., Erol S., Sihn W. (2016). A maturity model for assessing
industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP,
52,161-166.
[122] Sha, D. Y., & Che, Z. H. (2005). Virtual integration with a multi-criteria partner
selection model for the multi-echelon manufacturing system. The International Journal
of Advanced Manufacturing Technology, 25(7-8), 793-802.
[123] Shahin A., Mahbod M. (2007). Prioritization of key performance indicators.
International Journal of Productivity and Performance Management. 56(3), 226-240.
[124] Shih H-S, Shyur H-J, Stanley Lee E.(2007), An extension of TOPSIS for group
decision making, Mathematical and Computer Modelling, 45, 801–813.
[125] Simons, S., Abe, P., Neser, S. (2017). Learning in the AutFab- the fully
automated industrie 4.0 learning factory of the University of Applied Science
Darmstadt. 7th conference on learning factories, 9, 81-88.
[126] Sniderman, B., Mahto, M., Cotteleer, M. (2016). Industry 4.0 and
manufacturing ecosystems. Exploring the world of connected enterprises. Retrieved
September 2017 from https://www2.deloitte.com/insights/us/en/focus/industry-4-
0/manufacturing-ecosystems-exploring-world-connected-enterprises.html.
[127] Sureeyatanapas, P., Sriwattananusart, K., Niyamosoth, T., Sessomboon, W.,
Arunyanart, S. (2018). Supplier selection towards uncertain and unavailable
information: An extension of TOPSIS method. Journal Operations Research
Perspectives, 5, 69-79.
[128] Su, X., Hu, Y., Huang, S., Mahadevan, S., Deng, Y. (2014). A new decisionmaking
method by incomplete preferences based on evidence distance. Knowledge-
Based System, 56,264-272.
86
[129] Subramanian, P., Ramanathan, K., Laosirihongthong, T. (2012). A review of
applications of Analytic Hierarchy Process in operations management. International
Journal of Production Economics, 138, 215-241.
[130] Sundar, D., Umadevi, B., Alagarsamy, K. (2010). Multi Objective Genetic
Algorithm for the Optimized Resource Usage and the Prioritization of the Constraints
in the Software Project Planning. International journal of Computer Applications, 3, 1-
4.
[131] Sung, T.K. (2018). Industry 4.0: A Korea perspective. Journal of Technological
Forecasting and Social Change, 132, 40-45.
[132] Thannimalai, P., Kadhum, M.M., Feng, C.J., Ramadass, S. (2013). A glimpse
of cross training models and workforce scheduling optimization. 2013 IEEE
Symposium on Computers & Informatics, 98-103. Retrieved May 2018 from
https://ieeexplore.ieee.org/abstract/document/6612383/.
[133] Tsai, C.W., Lai, C.F., Chiang, M.C., Yang, L.T. (2013). Data mining for internet
of things: a survey. IEEE Communications Surveys & Tutorials. 16(1), 77-97.
[134] Vaidya, O.S., Kumar, S. (2006). Analytic hierarchy process: An overview of
applications. European Journal of Operational Research, 169, 1-29.
[135] Vafaeipour, M., Zolfani, S.H., Varzandeh, M.H.M., Derakhti, A., Eshkalag,
M.K. (2014). Assessment of regions priority for implementation of solar projects in
Iran: New application of a hybrid multi-criteria decision-making approach. Journal of
Energy Conversion and Management, 86, 653-663.
[136] Verdecho, M. J., Alfaro-Saiz, J. J., Rodriguez-Rodriguez, R., & Ortiz-Bas, A.
(2012). A multi-criteria approach for managing inter-enterprise collaborative
relationships. Omega, 40(3), 249-263.
[137] Vidic, N.S. (2008). Developing methods to solve the workforce assignment
problem considering worker heterogeneity and learning and forgetting, PhD
Dissertation, University of Pittsburgh. Retrieved May 2018 from
https://www.researchgate.net/publication/282259842_developing_methods_to_solve_
the_workforce_assignment
.problem_considering_worker_heterogeneity_and_learning_and_forgettingdeveloping
_methods_to_solve_the_workforce_assignment_problem_considering_worker .
[138] Wan, J., Cai, H., Zhou, K. (2015). Industrie 4.0: Enabling Technologies.
International conference on intelligent computing and internet of things(ICIT).
Retrieved March 2018 from https://ieeexplore.ieee.org/document/7111555/.
[139] Walter, M., Zimmermann, J. (2016). Minimizing average project team size
given multi-skilled workers with heterogeneous skill levels. Computers & Operations
Research, 70, 163-179.
[140] Wei, C.C., Chien, C.F., Wang, M.J.J. (2005). An AHP-based approach to ERP
system selection. International Journal of Production Economics. 96(1), 47-62.
[141] Wittenberg, C. (2016). Human-CPS interaction-requirements and humanmachine
interaction methods for the industry 4.0. IFAC, 49, 420-425.
[142] Wright, J., Alajmi, A. (2016). Efficient Genetic Algorithm sets for optimizing
constrained building design problem. International Journal of Sustainable Built
Environment, 5, 123-131.
[143] Wong C., Sancha C., Thomson, C. (2017). A national culture perspective in the
efficacy of supply chain integration practices. International Journal of production
economics, 193,554-565.
[144] Wongwai, N., Malaikrisanachalee, S. (2011). Augmented heuristic algorithm
for multi-skilled resource scheduling. Automation in Construction, 20, 429–445.
87
[145] Wu, C., & Barnes, D. (2011). A literature review of decision-making models
and approaches for partner selection in agile supply chains. Journal of Purchasing and
Supply Management, 17(4), 256-274.
[146] Wu, C., & Barnes, D. (2012). A dynamic feedback model for partner selection
in agile supply chains. International Journal of Operations & Production Management,
32(1), 79-103.
[147] Wu, C., Barnes, D., Rosenberg, D., & Luo, X. (2009). An analytic network
process-mixed integer multi-objective programming model for partner selection in
agile supply chains. Production Planning and Control, 20(3), 254-275.
[148] Wu, C., & Barnes, D. (2010). Formulating partner selection criteria for agile
supply chains: A Dempster–Shafer belief acceptability optimization approach.
International Journal of Production Economics, 125(2), 284-293.
[149] Xu X., Zhong R., Klotz E., Newman S., Thames L., Schaefer D. (2017).
Intelligent manufacturing in the context of industry 4.0: A review. Software -defined
cloud manufacturing for industry 4.0, 3(5), 616-630.
[150] Yan, H., Chai, H. (2017). Evaluation of influencing factors on general aviation
tourism industry of xi’an based on AHP and fuzzy comprehensive evaluation method.
IEEE International Conference on Industrial Engineering and Engineering
Management. Retrieved from https://ieeexplore.ieee.org/document/8290198/.
[151] Zhang, S. F., & Liu, S. Y. (2011). A GRA-based intuitionistic fuzzy multicriteria
group decision making method for personnel selection. Expert Systems with
Applications, 38(9), 11401-11405.
[152] Zhang, M., Guo, H., Huo, B., Zhao, X., Huang, J. (2017). Linking supply chain
quality integration with mass customization and product modularity. International
Journal of production economics. Retrieved June 2018 from
https://www.sciencedirect.com/science/article/pii/S0925527317300117.
[153] Zhang, Y. (2010). Research on human resource optimization based on genetic
algorithm from perspective of two-way choice model. International Conference on
Educational and Information Technology. Retrieved February 2018 from
https://ieeexplore.ieee.org/document/5607688/.
[154] Zhiting, S., Yanming, S., Jiafu, W., Peipei, L. (2017). Data quality management
for service-oriented manufacturing cyber-physical systems. Computer and Electrical
Engineering, 64, 34-44.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top