Skip to main content

Advertisement

SpringerLink
Log in

A manufacturing system energy-efficient optimisation model for maintenance-production workforce size determination using integrated fuzzy logic and quality function deployment approach

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

In maintenance systems, the current approach to workforce analysis entails the utilisation of metrics that focus exclusively on workforce cost and productivity. This method omits the “green” concept, which principally hinges on energy-efficient manufacturing and also ignores the production-maintenance integration. The approach is not accurate and could not be heavily relied upon for sound maintenance decisions. Consequently, comprehensive, scientifically-motivated, cost–effective and environmentally-conscious approaches are needed. With this in view, a deviation from the traditional approach through employing a combined fuzzy, quality function deployment interacting with three meta-heuristics (colliding bodies’ optimisation, big-bang big-crunch and particle swarm optimisation) for optimisation is made in the current study. The workforce size parameters are determined by maximising workforce size’s earned-valued as well as electric power efficiency maximisation subject to various real-life constraints. The efficacy and robustness of the model is tested with data from an aluminium products manufacturing system operating in a developing country. The results obtained indicate that the proposed colliding bodies’ optimisation framework is effective in comparison with other techniques. This implies that the proposed methodology potentially displays tremendous benefit of conserving energy, thus aiding environmental preservation and cost of energy savings. The principal novelty of the paper is the uniquely new method of quantifying the energy savings contributions of the maintenance workforce.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Aghezzaf E, Khatab A, Tam PL (2016) Optimizing production and imperfect preventive maintenance planning’s integration in failure-prone manufacturing systems. Reliab Eng Syst Saf 145:190–198

    Article  Google Scholar 

  • Bagheri M, Bashiri M (2014) A hybrid genetic and imperialist competitive algorithm approach to dynamic cellular manufacturing system. Proc Inst Mech Eng Part B: J Eng Manuf 228(3):458–470

    Article  Google Scholar 

  • Belmokaddem M, Mekidiche M, Sahed AK (2009) Application of a fuzzy goal programming approach with different importance and priorities to aggregate production planning. J Appl Quant Meth 4(3):317–331

    Google Scholar 

  • Biondia M, Sanda G, Harjunkoskia I (2015) Tighter integration of maintenance and production in short-term scheduling of multi-purpose process plants. Comput Aided Chem Eng 37:1937–1942

    Article  Google Scholar 

  • Bottani E (2009) A fuzzy QFD approach to achieve agility. Int J Prod Econ 119(2):380–391

    Article  Google Scholar 

  • Chin K-S, Wang Y-M, Yang J-B, Poon KKG (2009) An evidential reasoning based approach for quality function deployment under uncertainty. Exp Syst Appl 36:5684–5694

    Article  Google Scholar 

  • Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Meth Appl Mech Eng 191:1245–1287

    Article  MATH  MathSciNet  Google Scholar 

  • Cosgrove J, Duarte M-JR, Littlewood J, Wilgreroth P (2016) An energy mapping methodology to reduce energy consumption in manufacturing operations. Proc Inst Mech Eng Part B: J Eng Manuf. doi:10.1177/0954405416673101

    Google Scholar 

  • Dai M, Tang D, Xu Y, Li W (2015) Energy-aware integrated process planning and scheduling for job shops. Proc Inst Mech Eng Part B: J Eng Manuf 229(51):13–26

    Article  Google Scholar 

  • Dat LQ, Phuong TT, Kao H-P (2015) A new integrated fuzzy QFD approach for market segments evaluation and selection. Appl Math Model 39:3653–3665

    Article  MathSciNet  Google Scholar 

  • Duflon JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Hauschild M, Kellens K (2012) Towards energy and resource efficient manufacturing: a process and system approach. CIRP Annals-Manuf Technol 61:587–609

    Article  Google Scholar 

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth int symposium on micro-machine and human science, Nagoya, Japan

  • El-Baz MA (2011) Fuzzy performance measurement of a supply chain in manufacturing companies. Exp Syst Appl 38:6681–6688

    Article  Google Scholar 

  • Engelbrecht AP (2007) Artificial intelligence: an introduction. Wiley, New York

    Google Scholar 

  • Erol OK, Eksin I (2006) New optimisation method: big-bang big-crunch. Adv Eng Soft 37:106–111

    Article  Google Scholar 

  • Faulkner W, Badurdeen F (2014) Sustainable value stream mapping (Sus-VSM): methodology to visualise and assess manufacturing sustainability performance. J Clean Prod 85:8–18

    Article  Google Scholar 

  • Felan JT, Fry TD (2001) Multi-level heterogeneous worker flexibility in a dual resource constrained (DRC) job-shop. Int J Prod Res 39(14):3041–3059

    Article  Google Scholar 

  • Fletcher SR, Baines TS, Harrison D (2008) An investigation of production workers’ performance variations and the potential impact of attitudes. ]nol 35(11–12):1113–1123

    Google Scholar 

  • Ghani et al (2012) Energy optimisation in manufacturing systems using initial engineering-driven discrete event simulation. Proc Inst Mech Eng Part B: J Eng Manuf 226(11):1914–1929

    Article  MathSciNet  Google Scholar 

  • Hasani H, Tabatabaei SA, Amiri G (2012) Grey relational analysis to determine the optimum process parameters for open-end spinning yarns. J Eng Fibers and Fabrics 7(2):81–86

    Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Ighravwe DE, Oke SA (2014) A non-zero integer non-linear programming model for maintenance workforce sizing. Int J Prod Econ 150:204–214

    Article  Google Scholar 

  • Karsak EE (2004) Fuzzy multiple objective programming framework to prioritise design requirements in quality function deployment. Comput Ind Eng 47(2–3):149–163

    Article  Google Scholar 

  • Kaveh A, Ghazaan MI (2014) Computer codes for colliding bodies optimisation and its enhanced version. Int J Opt Civil Eng 4(3):321–339

    Google Scholar 

  • Kaveh A, Mahdavi VR (2014a) Colliding bodies optimisation: a novel meta-heuristic method. Comput Struct 139:18–27

    Article  Google Scholar 

  • Kaveh A, Mahdavi VR (2014b) Colliding bodies optimisation for design of arch dams with frequency limitations. Int J Opt Civil Eng 4(4):473–490

    Google Scholar 

  • Kirkpatrick S, Gelett CD, Vecchi MP (1983) Optimisation by simulated annealing. Science 220:621–630

    Article  Google Scholar 

  • Luna-Junior FR, Carpinetti LCR (2016) A multi-criteria approach based on fuzzy QFD for choosing criteria for supplier selection. Comput Ind Eng 101:269–285

    Article  Google Scholar 

  • Maniraj M, Pakkirisamy V, Jeyapaul R (2015) an ant colony optimisation-based approach for a single-product flow-line reconfigurable manufacturing systems. Proc Inst Mech Eng Part B: J Eng Manuf. doi:10.1177/0954405415585260

    Google Scholar 

  • Mekidiche M, Belmokaddem M, Djemmaa Z (2013) Weighted additive fuzzy goal programming approach to aggregate production planning. Int J Intell Syst Appl 4:20–29

    Google Scholar 

  • Narayanan RG, Das S (2014) Sustainable and green manufacturing and materials design through computations. Proc Inst Mech Eng Part C: J Mech Eng Sci 228(9):1581–1605

    Article  Google Scholar 

  • Nourelfath M, Nahas N, Ben-Daya M (2016) Integrated preventive maintenance and production decisions for imperfect processes. Reliab Eng Syst Saf 148:21–31

    Article  Google Scholar 

  • Onar SC, Buyukozkan G, Oztaysi B, Kahraman C (2016) A new hesitant fuzzy QFD approach: an application to computer workstation selection. Appl Soft Comput 46:1–16

    Article  Google Scholar 

  • Pang Y, Xia H (2016) a hybrid modeling and optimisation approach for scheduling problems of assembly automation processes. Proc Inst Mech Eng Part I: J Syst Cont Eng 230(8):778–785

    Google Scholar 

  • Ramanathan R, Yunfeng J (2009) Incorporating cost and environmental factors in quality function deployment using data envelopment analysis. Omega 37:711–723

    Article  Google Scholar 

  • Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimisation: a novel method for constrained mechanical design optimisation problem. Comput Aided Des 43:303–315

    Article  Google Scholar 

  • Sahu SK, Datta S, Patel SK, Mahapatra SS (2013) Supply chain performance appraisement, benchmark and decision-marking: empirical study using grey theory and grey-MOORA. Int J Process Manag Benchm 3(3):233–289

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimisation over continuous spaces. J Glob Opti 11:341–359

    Article  MATH  Google Scholar 

  • Techawiboonwong A, Yenradee P, Das SK (2006) A master scheduling model with skilled and unskilled temporary workers. Int J Prod Econ 103(2):798–809

    Article  Google Scholar 

  • Vinodh S, Chintha SK (2011) Application of fuzzy QFD for enabling leanness in a manufacturing organisation. Int J Prod Res 49(6):1627–1644

    Article  Google Scholar 

  • Wang R-T (2007) Improving service quality using quality function deployment: the air cargo sector of China airlines. J Trans Manag 13:221–228

    Google Scholar 

  • Wu Z (2008) Hybrid multi-objective optimisation models for managing pavement assets. Thesis Ph.D. Virginia Polytechnic Institute and State University, Department of Civil and Environmental Engineering

  • Yan H-B, Ma T (2015) A group decision-making approach to uncertain quality deployment based on fuzzy performance relation and fuzzy majority. Eur J Oper Res 241(3):815–829

    Article  MATH  Google Scholar 

  • Yang X-S (2009) Firefly algorithm for multimodal optimisation, In: Stochastic algorithm: foundations and application, SAGA, Lecture Note in Computer Science, 5792, 169–178

  • Yue H, Slomp J, Molleman E, van Der Zee DJ (2007) Worker flexibility in a parallel dual resource constrained job shop. Int J Prod Res 46(2):1–17

    MATH  Google Scholar 

  • Zandi F, Tavana M (2011) A fuzzy group quality function deployment from e-CRM framework assessment in agile manufacturing. Comput Ind Eng 61(1):1–19

    Article  Google Scholar 

  • Zhang J-Q, Sanderson AC (2009) JADE: adaptive differential evolution with optimal external archive. IEEE Trans Evol Comp 13(5):945–958

    Article  Google Scholar 

  • Zhang X, Wang S, Yi L, Xue H, Yang S, Xiong X (2016) An integrated ant colony optimisation algorithm to solve job allocating and tool scheduling problem. Proc Inst Mech Eng Part B: J Eng Manuf. doi:10.1177/0954405416636038

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of EngineeringUniversity of Lagos, Room 10, Mezzanine Complex, Akoka-Yaba, Lagos, Nigeria

    D. E. Ighravwe & S. A. Oke

  2. Department of Mechanical Engineering, Faculty of Engineering and TechnologyLadoke Akintola University of Technology, Ogbomoso, Nigeria

    D. E. Ighravwe

  3. Industrial and Production Engineering Unit, Department of Mechanical EngineeringCovenant University, Ota, Nigeria

    S. A. Oke

Authors
  1. D. E. Ighravwe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. A. Oke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. A. Oke.

Appendix

Appendix

1.1 Section A

  1. 1.

    What is the number of products produced?

  2. 2.

    What is the average weekly production volume of each product?

figure a
  1. 3.

    Do your company outsource production activities?

  2. 4.

    If yes, what is the average weekly volume of each product that is outsourced?

figure b
  1. 5.

    What is the unit cost of outsourced product?

figure c

1.2 Section B

In this section, information on evaluation of the performance of a production system is required. The set of selected questions are presented below, while the rating system for these questions is given as follows:

Grade

Interpretation

Low

Less than 40%

Medium

41–60%

High

61–80%

Very high

Above 80%

S/n

Questions for responses

Low

Medium

High

Very high

1

How will you rate production workers idle time in your company?

2

How will you rate maintenance workers idle time in your company?

3

How will you rate machine efficiency in your company?

4

How will you rate machine utilisation in your company?

5

How will you rate machine availability in your company?

6

How will you rate machine reliability in your company?

7

How will you rate production workers efficiency in your company?

8

How will you rate maintenance workers utilisation in your company?

9

How will you rate maintenance workers efficiency in your company?

10

How will you rate production workers utilisation in your company?

11

How will you rate workers turnover rate in your company?

1.3 Section C

In this section, the range of some resources used during weekly production activities is required. Kindly assist us in providing information for the following set of questions:

S/No.

Questions for responses

1

What is the range of weekly full-time production workers?

2

What is the range of weekly full-time maintenance workers?

3

What is the range of weekly causal production workers?

4

What is the range of weekly causal maintenance workers?

5

What is the range weekly production?

6

What is the range of weekly breakdown time?

7

What is the range of weekly production cost?

8

What is the range of weekly maintenance cost?

9

What is the range of weekly cost of spares?

10

What is the range of weekly workforce cost/?

12

What is the range of weekly maintenance?

13

What is the range of weekly production time?

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ighravwe, D.E., Oke, S.A. A manufacturing system energy-efficient optimisation model for maintenance-production workforce size determination using integrated fuzzy logic and quality function deployment approach. Int J Syst Assur Eng Manag 8, 683–703 (2017). https://doi.org/10.1007/s13198-016-0555-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-016-0555-7

Keywords

Search

Navigation