Maintenance workforce optimisation in a process industry using differential evolution

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Desmond Eseoghene Ighravwe Sunday Ayoola Oke Kazeem Adekunle Adebiyi


In the past few years, differential evolution (DE) algorithms have been applied to solve system optimisation problems. Optimisation of workforce variables is a necessary requirement to make maintenance workforce planning. Since the effective control and monitoring of major system losses is tied to maintenance, the competent historical performance and potential of DE to optimise maintenance workforce variables has strongly inspired this work. The workforce optimisation structure depends on computations involving the following performance parameter: Production line availability, workforce size changes, cost of service rate improvement, workforce bonuses as well as penalty costs and then cost of spare parts. The developed framework used DE algorithm to optimise workforce including production and maintenance variables in an integrated framework. The model incorporates nonlinear integer model and weighted additive fuzzy goal programming model. The DE algorithm was used in generating Pareto solution for maintenance and production variables. The reliability as well as the effectiveness of the presented method was verified using practical real-life data from a process industry operating in a developing country. The obtained results showed that DE algorithm can generate accurate results with a fast convergence rate and good stability as genetic algorithm and particle swarm optimisation algorithm. The study could be replicated in other process industries such as drinks manufacturing.



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Author Biography

Sunday Ayoola Oke, Department of Mechanical Engineering, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria

Oke lectures at the University of Lagos, Nigeria


[1] Ighravwe D.E., Oke S.A., Adebiyi K.A., A reliability based maintenance technicians’ model loads optimization model with stochastic consideration, J Ind Eng Int. 2016; 12: 171-183.

[2] Sitkin, S.B., See, K.E., Miller, C.C., Lawless, M.W. and Carton A.M. The paradox of stretch goals: Organisations in pursuit of the seemingly impossible. Acad Manag Rev. 2011; 36(3): 544-566.

[3] Sethanan, K. and Pitakaso, R. Differential evolution algorithms for scheduling raw milk transportation. Comp Electr Agric. 2016; 121: 245-259.

[4] Noktehdan, A., Karimi, B. and Kashan, A.H., A differential evolution algorithm for the manufacturing cell formation problem using group based operators. Exp Syst Appl. 2010; 37(7): 4822-4829.

[5] Zeng, X., Wong, W-K. and Leung, Y.-S., An operator allocation optimisation model for balancing control of the hybrid assembly lines using Pareto utility discrete differential evolution algorithm. Comp Operat Res. 2012; 39(5): 1145-1159.

[6] Yildiz, A.R. Hybrid evolutionary systems for manufacturing processes. Appl Soft Comp. 2013; 13(3): 1433-1439.

[7] Atif, M. and Al-Sulaiman, Optimisation of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm. Ener Conser Manag. 2015; 95: 1-9.

[8] Le-Anh L., Nguyen-Thoi, T., Hottw, U., Dang-Trung H., and Bui-uan T., Static and frequency optimisation of folded laminated composite plates using an adjusted differential evolution algorithm and a smoothed triangular plate element. Comp Struct. 2015; 127: 382-394.

[9] De Falco, I., Differential evolution for automatic rule extraction from medical databases. Appl Soft Comp. 2013; 13(2): 1265-1283.

[10] Ghosh, S., A differential evolution based approach for estimating minimal model parameters from IVGTT data. Comp Bio Med. 2014; 46: 57-60.

[11] Raza S.A. and Al-Turki U.M. A comparative study of heuristic algorithms to solve maintenance scheduling problem. J Qual Maint Eng. 2007; 13(4): 398-410.

[12] Fetanat A. and Shafipour G., Generation maintenance scheduling in power systems using ant colony optimization for continuous domains based 0–1 integer programming, Exp Syst Appl. 2011; 38: 9729-9735.

[13] Li, J-Q., and Pan, Q-K., Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Appl Soft Comp. 2012; 12: 2896–2912.

[14] Li, J-Q., Pan, Q-K. and Tasgetiren, M.F., A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. App Math Mod. 2014; 38: 1111-1132.

[15] Nourelfath M., Chatelet E. and Nahas N. Joint redundancy and imperfect preventive maintenance optimization for series–parallel multi-state degraded systems. Reliab Eng Sys Saf.2012; 103: 51-60.

[16] Chaoqui M., Benhra J. and Zakari A. Agile Approach for Joint Scheduling of Production and Maintenance in Flow Shop. Int J Comp Appl. 2014; 59(11): 29-36.

[17] Alba, E., Luque, G., and Luna, F., Parallel meta-heuristic for workforce planning. J Mathem Alg. 2007; 6(3): 509-528.

[18] Hargaden, V., and Ryan, S., Workforce analytics: Matching supply and demand in professional service firms. In: Manufacturing and Service Operations Management (MSOM) Annual Conference; 2011.

[19] Haque, N., Virginas, B., Kern, M., and Owusu, G., An auction-based system for workforce resource allocation. New Frontiers in App Artificial Intel -Lecture Note in Comp Sci. 2008; 5027: 845-854

[20] Alfares, H.K., Aircraft maintenance workforce scheduling: A case study. J Qua Maint Eng. 1999; 5(2): 78-88.

[21] April, J., Better, M., Glover, F., Kelly, J.P., and Kochenberger, G., Strategic workforce optimisation: Ensuring workforce readiness with optforce [Internet].2010 [cited 2015 April 1]. Available from pdf

[22] Starkey, A., Hagrasa, H., Shakya S. and Owusu G., A multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimisation. Inform Sci. 2016; 329: 390-411.

[23] Monra, M. Azevedo, R.V., Droguett, E.L., Chaves, L.R. Lins, I.D., Vilela, R.F., Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model, Reliab Eng Sys Saf.2016; 150: 136-146

[24] Firat, M., Briskron, D., and Laugier, A., A branch and price algorithm for stable workforce
assignment with hierarchical skills. Euro J Oper Res. 2016; 251(2): 679-685.

[25] Ighravwe, D.E., Oke S.A., and Adebiyi, K.A., An enhanced reliability-oriented workforce
planning model for process industry using combined fuzzy goal programming and differential
evolution approach. J Ind Eng Int. 2017b; doi: 10.1007/s40092-017-0223-9.

[26] Markovic, D., Petrovic, G., Cojbasic, Z. and Marinkovic, D., A comparative analysis of
metaheuristic maintenance optimization of refuse collection vehicles using the Taguchi
experimental design. Trans Famena. 2012;36(4): 25-38.

[27] Ighravwe D.E and Oke S.A., A mixed-integer multi-objective maintenance and production
workforce planning model, Process Integr Optim and Sus. 2017c; 1: 251-267.

[28] Ighravwe D.E., Oke S.A., Adebiyi K.A., A weighted goal programming model for maintenance
workforce optimization for a process industry, Asia-Pacific J Sci Tech. 2017d; 22(4): 1-18

[29] Ighravwe D.E, Oke S.A., Adebiyi K.A., A surrogate model for optimal maintenance workforce
cost determination in a process industry, Eng Appl Sci Res. 2017e; 44(4): 202-207.

[30] Yare, Y. and Venayagamoorthy, G.K., Optimal scheduling of generator maintenance using
modified discrete particle swarm optimisation. Symposium-Bulk Power System Dynamics and
Control-VII, Revitalising Operational Reliability 2007 August 19-24; Charleston, SC. USA; 2007.

[31] Engelbrencht, A.P., Artificial Intelligence: An Introduction. England: John Wiley and Sons
Limited; 2007.

[32] Pereira, C.M.N.A., Lapa, C.M.F., Antonio, C.A., Mol, A.C.A. and da Luz, A.F., A particle swarm
optimisation (PSO) approach for non-periodic preventive maintenance scheduling programming.
Prog Nuclear Ener. 2010; 52: 710-714.

[33] Heo, J-H., Lyu, J-K., Kim, M-K. and Park, J-K., Application of particle swarm optimisation to the
reliability centred maintenance method for transmission Systems. J Electrical Eng Tech. 2012;
7(6): 814-823.

[34] Wang, J., Lu, J., Bie, Z., You, S. and Cao X., Long-term maintenance scheduling of smart
distribution system through a PSO-TS algorithm. J Appl Math. 2014; 1-12.

[35] Samuel, G.G. and Rajan, C.C.A., Hybrid particle swarm optimisation: Evolutionary programming
approach for solving generation maintenance scheduling problem. Scien Res and Ess. 2013;
8(35): 1701-1713.

[36] Joines, J.A., and Houck, C.R., On the use of non-stationary penalty functions to solve nonlinear
constrained optimisation problems with genetic algorithms. In: Proceedings of the IEEE Congress
on Evolutionary Computation. 1994; 579-584.

[37] Deb, K. Agrawal, S. Pratap A. and Meyarivan, T., A Fast Elitist Nondominated sorting genetic
algorithm for multi-objective optimization: NSGA-II, Proceedings of the Parallel Problem
Solving from Nature VINSGA-II," Proceedings of the Parallel Problem Solving from Nature VI
Conference;2000 September 16-20; Paris. France; p. 849-58.

[38] Ighravwe, D.E., Oke, S.A., and Adebiyi, K.A., Preventive maintenance task balancing with spare
parts optimisation via big-bang big-crunch algorithm. Int J Sys Assu Eng Manag. 2017a; 8(2):

[39] Mekidiche, M., Belmokaddem, M., and Djemmaa, Z., Weighted additive fuzzy goal programming
approach to aggregate production planning. Int J Intell Sys Appl. 2013; 4: 20-29.