A Mixed-integer Multi-objective Maintenance and Production Workforce Planning Model

Springer Science and Business Media LLC - Tập 1 - Trang 251-267 - 2017
Desmond Eseoghene Ighravwe1, Sunday Ayoola Oke2,3
1Department of Mechanical Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
2Department of Mechanical Engineering, Faculty of Engineering, University of Lagos, Lagos, Nigeria
3Industrial and Production Engineering Unit, Department of Mechanical Engineering, College of Engineering, Covenant University, Ota, Nigeria

Tóm tắt

This study tackles the dilemma of producing a cooperative workforce planning involving maintenance and production departments. Its focus is on instituting workforce equilibrium between maintenance and production departments. This is necessary in order to ensure the optimal utilisation of manufacturing resources (time, equipment and funds). This maintenance-production problem was modelled using a mixed-integer multi-objective approach. The model minimises the workforce, rework and scrap, inventory holding cost and machine usage costs while maximising the average achieved machine availability. The model accounts for expected products’ demand, the amount of expected defective products, workforce size, rest period and finished good inventory. Due to the nonlinear relationships among the maintenance-production variables, a big bang-big crunch (BB-BC) algorithm and genetic algorithm (GA) were selected as solution methods for the model. The proposed model performance was validated in a household utensils manufacturing plant under the conditions of workers’ rest and without rest periods considerations. Based on the results obtained, the BB-BC algorithm performed better than the GA in terms of fitness function and computational time. The model performance with workers’ rest period consideration generated higher achieved machine availability value than when workers’ rest period was not considered. The optimised maintenance-production variables results confirmed the model’s capability to generate acceptable solutions for the case study.

Tài liệu tham khảo

Alfares HK (1999) Aircraft maintenance workforce scheduling: a case study. J Qual Maint Eng 5(2):78–88 Ardalan A (2000) Economic and financial analysis for engineering and project management. Technomic Publishing Company, Inc, Pennsylvania, USA Azad KS, Hasançebia O, Erol OK (2011) Evaluating efficiency of big-bang big-crunch algorithm in benchmark engineering optimisation problems. Int J Optim Civil Eng 3:495–505 Barnett K, Blundell J (1981) Trade demarcations in maintenance determination of optimum crew sizes by the Monte-Carlo simulation technique. Terotechnica 2:147–155 Belmokaddem M, Mekidiche M, Sahed A (2008) Application of a fuzzy goal programming approach with different importance and priorities to aggregate production planning. J Appl Quantitative Methods 4(3):317–331 Berrichi A, Yalaoui F, Amodeo L, Mezghiche M (2010) Bi-objective ant colony optimisation approach to optimise production and maintenance scheduling. Comput Oper Res 37(9):1584–1596 Chen WJ (2007) Minimising total flow time and maximum tardiness with periodic maintenance. J Qual Maint Eng 13(3):293–303 Cheng GQ, Zhou BH, Li L (2016) Joint optimisation of production rate and preventive maintenance in machining systems. Int J Prod Res 54(21):6378–6394. https://doi.org/10.1080/00207543.2016.1174343 Duffuaa SO, Al-Sultan KS (1997) Mathematical programming approaches for the management of maintenance planning and scheduling. J Qual Maint Eng 3(3):163–176 Duffuaa SO, Ben-Daya M, Al-Sultan KS, Andijani A (2001) Generic conceptual simulation model for maintenance systems. J Qual Maint Eng 7(3):207–219 Engelbrecht AP (2007) Artificial Intelligence: An Introduction. Wiley, New York Erol O, Eksin I (2006) New optimisation method: big bang-big crunch. Adv Eng Softw 37:106–111 Fakher HB, Nourelfath M, Gendreau M (2017) A cost minimisation model for joint production and maintenance planning under quality constraints. Int J Prod Res 55(8):2163–2176. https://doi.org/10.1080/00207543.2016.1201605 Greenwood G, Gupta A (2000) Workforce constrained preventive maintenance scheduling using evolution strategies. Adv Decis Sci 31(4):833–859 Hajej Z, Turki S, Rezg N (2015) Modelling and analysis for sequentially optimising production, maintenance and delivery activities taking into account product returns. Int J Prod Res 53(15):4694–4719 Hu J, Jiang Z, Liao H (2017) Preventive maintenance of a batch production system under time-varying operational condition. Int J Prod Res 55(19):5681–5705. https://doi.org/10.1080/00207543.2017.1330565 Ighravwe DE, Oke SA (2014) A non-zero integer non-linear programming model for maintenance workforce sizing. Int J Prod Econ 150:204–214 Ighravwe DE, Oke SA, Adebiyi KA (2015) Maintenance workload optimisation with accident occurrence considerations and absenteeism from work using a genetic algorithm. Int J Manag Sci Eng Manag 11(4):294–302. https://doi.org/10.1080/17509653.2015.1065208 Ip WH, Kwong CK, Fung R (2000) Design of maintenance system in MRPII. J Qual Maint Eng 16(3):177–191 Ireson WG, Clyde FC Jr., Richard YM (1996) Handbook of reliability engineering and management, 2nd ed. McGraw-Hill, New York, United States. Jebari K, Madiafi M (2013) Selection methods for genetic algorithms. Int J Emerg Sci 3(4):333–344 Judice J, Martins P, Nunes J (2005) Workforce planning in a lot sizing mail processing problem. Comput Oper Res 32:3031–3058 Kripka M, Kripka RML (2008) Big crunch optimisation method. International Conference on Engineering Optimisation 1–5, Rio de Janeiro, Brazil, June: 1–6. Labbi Y, Attous DB (2010) Big bang-big crunch optimisation algorithm for economic dispatch with valve-point effect. Journal of Theoretical and Applied Information Technology 16:48–56. Liao G-L (2016) Optimal economic production quantity policy for a parallel system with repair, rework, free-repair warranty and maintenance. Int J Prod Res https://doi.org/10.1080/00207543.2016.1203074 Lopez P, Carteno G (2006) Integrated system to maximise efficiency in transit maintenance departments. Int J Prod Perform Manag 55(8):638–654 Lucic P, Teodorovic D (1999) Simulated annealing for the multi-objective aircrew rostering problem. Transp Res Part A: Policy Prac 33(1):19–45 Moore TD, Johnson AW, Rehg MT, Hicks MJ (2007) Methodology and theory: quality assurance staffing impacts in military aircraft maintenance units. J Qual Maint Eng 13(1):33–48 Mansour MAA-F (2011) Solving the periodic maintenance scheduling problem via genetic algorithm to balance workforce levels and maintenance cost. Am J Eng Appl Sci 4(2):223–234 Michalewicz Z (1996) Genetic algorithms + data structures = evolutionary programs, third edn. Springer, Berlin Nasr WW, Salameh M, Moussawi-Haidar L (2016) Economic production quantity with maintenance interruptions under random and correlated yields. Int J Prod Res 55(16): 4544–4556. https://doi.org/10.1080/00207543.2016.1265684. Ntuen CA, Park EU (1999) Simulation of crew size requirement in a maintained reliability system. Comput Ind Eng 37:219–222 Osman KE, Eskin I (2006) New optimisation method: big bang-big crunch. Adv Eng Softw 37:106–111 Othman M (2012) Integrating workers’ difference into workforce planning. Thesis (Ph.D.), Concordia University Montreal, Quebec Canada Pleumpirom Y, Amornsawadwatana S (2012) Multi-objective optimisation of aircraft maintenance in Thailand using goal programming: a decision-support model. Adv Decis Sci 1–17. Prudhvi P (2011) A complete copper optimisation technique using BB-BC in a smart home for a smart grid and a comparison with GA. IEEE Conference, CCECE. Ramirez-Hernandez JA, Fernandez E, O’Connor M, Patel N (2007) Conversion of non-calendar to calendar-time-based preventive maintenance schedules for semiconductor manufacturing systems. J Qual Maint Eng 13(3):259–275 Rao CVGK, Yesuratnam G (2012) Big-bang and big-crunch (BB-BC) and firefly optimisation (FFO): application and comparison to optimal power flow with continuous and discrete control variables. Int J Electr Eng Inf 4(4):575–583 Sakthivel S, Mary D (2013) Big bang-big crunch algorithm for voltage stability limits improvement by coordinated control of SVC settings. Res J Appl Sci Eng Technol 6(7):1209–1217 Sanchez A, Carles S, Martorell S, Villanuera JF (2009) Addressing imperfect maintenance modelling uncertainty in unavailability and cost-based optimisation. Reliab Eng Syst Saf 94:22–32 Santos C, Zhang A, Gonzales M, Jain S, Byde A (2009) Workforce planning and scheduling for the HP IT services business. Multi-disciplinary International Conference on Scheduling, Theory and Application. Dublin, Ireland 720–723. Sortrakul N, Cassady RC (2007) Methodology and theory: genetic algorithms for total weighted-expected tardiness integrated preventive maintenance planning and production scheduling for a single machine. J Qual Maint Eng 13(1):49–61 Tabrizian Z, Afshari E, Amiri G, Beigy MHA, Nejad SMP (2013) A new damage detection method: big-bang big-crunch (BB-BC) algorithm. Shock and Vib 20:633–648 Tse PW (2002) Maintenance practices in Hong Kong and the use of the intelligent scheduler. J Qual Maint Eng 8(4):369–380 Weil G, Heus K, Francois P, Poujade M (1995) Constraint programming for nurse scheduling. IEEE Eng Med Biol 4:417–422 Wright A (1991) Genetic algorithms for real parameter optimization. In: Rawlins GJE (ed) Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, C.A., pp 205–220 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. Virginia, USA. Wu X, Zhang K, Cheng M (2017) Computational method for optimal machine scheduling problem with maintenance and production. Int J Prod Res 55(6): 1791–1814. https://doi.org/10.1080/00207543.2016.1245451. Xia T, Jin X, Xi L, Ni J (2015) Production-driven opportunistic maintenance for batch production based on MAM-APB scheduling. Eur J Oper Res 240(3):781–790