Scheduling multi-component maintenance with a greedy heuristic local search algorithm

  • Seyedmohsen HosseiniEmail author
  • Sifat Kalam
  • Kash Barker
  • Jose E. Ramirez-Marquez
Methodologies and Application


As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency.


Maintenance scheduling Downtime Repair 



Department of Defense


Genetic algorithm


Government accountability office


Greedy heuristic-based improvement


Greedy heuristic local search algorithm


Greedy randomized adaptive search


Least significant difference


Maintenance, repair and overhaul


Mean time between failure


Original equipment manufacturer


Preventive maintenance scheduling problem


Reliability-centered maintenance


Relative proportion deviation


Simulated annealing



This publication results from research supported by the Naval Postgraduate School Assistance Grant/Agreement No. N00244-12-1-0069 awarded by the NAVSUP Fleet Logistics Center San Diego. The views expressed in written materials or publications do not necessarily reflect the official policies of the Naval Postgraduate School nor do mention of trade names, commercial practices, or organizations imply endorsement by the US Government.

Compliance with ethical standards

Conflict of interest

The authors do not have conflicts of interest to disclose.


  1. Abirami M, Ganesan S, Subramanian S, Anandhakumar R (2015) Source and transmission line maintenance outage scheduling in a power system using teaching learning based optimization algorithm. Appl Soft Comput 21:72–83CrossRefGoogle Scholar
  2. Al Khaled A, Hosseini S (2015) Fuzzy adaptive imperialist competitive algorithm for global optimization. Neural Comput Appl 26(4):813–825CrossRefGoogle Scholar
  3. Al-Najjar B, Alsyouf I (2003) Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. Int J Prod Econ 84(1):85–100CrossRefGoogle Scholar
  4. Arroyo JE, Leung Y-T (2017) An effective iterated greedy algorithm for scheduling unrelated parallel batch machines with non-identical capacities and unequal ready times. Comput Ind Eng 105:84–100CrossRefGoogle Scholar
  5. Behnamian J, Fatemi Ghomi SMT (2015) Minimizing cost-related objective in synchronous scheduling of parallel factories in the virtual production network. Appl Soft Comput 29:221–232CrossRefGoogle Scholar
  6. Bevilacqua M, Braglia M (2000) The analytic hierarchy process applied to maintenance strategy selection. Reliab Eng Syst Saf 70(1):71–83CrossRefGoogle Scholar
  7. Canh VuH, Barros A, Berenguer C (2014) Maintenance grouping strategy for multi component systems with dynamic context. Reliab Eng Syst Saf 132:233–249CrossRefGoogle Scholar
  8. Chen W-J (2008) Single-machine scheduling with maintenance in a manufacturing system. J Inf Optim Sci 29(3):543–556zbMATHGoogle Scholar
  9. Czapinski M (2010) Parallel simulated annealing with genetic enhancement for flowshop problem with Csum. Comput Ind Eng 59(4):778–785CrossRefGoogle Scholar
  10. Dekker R, Wildeman RE, van der Duyn Schouten FA (1997) A review of multi-component maintenance models with economic dependence. Math Methods Oper Res 45(3):411–435MathSciNetCrossRefzbMATHGoogle Scholar
  11. Do P, Vu HC, Barros A, Berenguer C (2015) Maintenance grouping for multi-component systems with availability constraints and limited teams. Reliab Eng Syst Saf 142:56–67CrossRefGoogle Scholar
  12. Eygelaar J, Lotter DP, Van Vuuren JH (2018) Generator maintenance scheduling based on the risk of power generating unit failure. Electr Power Energy Syst 95:83–95CrossRefGoogle Scholar
  13. Fan Y-P, Zhao C-L (2014) Single machine scheduling with multiple common due date assignment and aging effect under a deteriorating maintenance activity consideration. J Appl Math Comput 46(1–2):51–66MathSciNetCrossRefzbMATHGoogle Scholar
  14. Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Global Optim 6:109–133MathSciNetCrossRefzbMATHGoogle Scholar
  15. Froger A, Gendreau M, Mendoza JE, Rousseau L-M (2017) A branch-and-check approach for a wind turbine maintenance scheduling problem. Comput Oper Res 88:117–136MathSciNetCrossRefzbMATHGoogle Scholar
  16. Government Accountability Office (2007) Defense budget: trends in operation and maintenance costs and support services contracting. GAO-07-631Google Scholar
  17. Government Accountability Office (2011) Defense logistics: DOD input needed on implementing depot maintenance study recommendations. GAO-13-267Google Scholar
  18. Government Accountability Office (2013) Defense business transformation: improvements made but additional steps needed to strengthen strategic planning and assess progress. GAO-13-267Google Scholar
  19. Grigoriev A, Van de Klundert J, Spieksma FCR (2015) Modeling and solving the periodic maintenance problem. Eur J Oper Res 172:783–797MathSciNetCrossRefzbMATHGoogle Scholar
  20. Gürler Ü, Kaya A (2002) A maintenance policy for a system with multi-state components: an approximate solution. Reliab Eng Syst Saf 76(2):117–127CrossRefGoogle Scholar
  21. Gustavsson E, Pattriksson M, Stromberg A-B, Wojciechowski A, Onnheim M (2014) Preventive maintenance scheduling of multi-component systems with interval costs. Comput Ind Eng 76:390–400CrossRefGoogle Scholar
  22. Hosseini S, Al Khaled A (2014) A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl Soft Comput 24:1078–1094CrossRefGoogle Scholar
  23. Hosseini S, Al Khaled A, Vadlamani S (2014) Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem. Neural Comput Appl 25(7–8):1871–1885CrossRefGoogle Scholar
  24. Kalam S, Barker K, Ramirez-Marquez JE (2013) Improving multi-component maintenance acquisition with a greedy heuristic local search algorithm. In: Proceedings of the naval postgraduate school acquisition research symposium, Monterrey, CAGoogle Scholar
  25. Kaplanoglu V (2014) Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance. Appl Soft Comput 23:165–179CrossRefGoogle Scholar
  26. Karimi-Nasab M, Modarres M, Seyedhoseini SM (2015) A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times. Appl Soft Comput 27:137–147CrossRefGoogle Scholar
  27. Laggoune R, Chateauneuf A, Aissani D (2009) Opportunistic policy for optimal preventive maintenance of a multi-component system in continuous operating units. Comput Chem Eng 33:1499–1510CrossRefGoogle Scholar
  28. Lei D (2012) Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling. Appl Soft Comput 12(8):2237–2245CrossRefGoogle Scholar
  29. Li J-Q, Pan Q-K (2012) Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Appl Soft Comput 12(9):2896–2912CrossRefGoogle Scholar
  30. Li H, Mi S, Wen X, Qiao D, Luo G (2018) A scheduling optimization method for maintenance, repair and operations service resources of complex products. J Intell Manuf. Google Scholar
  31. Liao W, Pan E, Xi L (2010) Preventive maintenance scheduling for repairable system with deterioration. J Intell Manuf 21:875–884CrossRefGoogle Scholar
  32. Liu X, Wang W, Peng R (2015) An integrated production, inventory, and preventive maintenance model for a multi-product production system. Reliab Eng Syst Saf 137:76–86CrossRefGoogle Scholar
  33. Naderi B, Zandieh M, Aminnayeri M (2011) Incorporating periodic preventive maintenance into flexible flowshop scheduling problems. Appl Soft Comput 11:2094–2101CrossRefGoogle Scholar
  34. Pan E, Liao W, Xi L (2010) Single machine based production scheduling model integrated preventive maintenance planning. Int J Adv Manuf Technol 50(1–4):365–375CrossRefGoogle Scholar
  35. Pan E, Liao W, Xi L (2012) A joint model of production scheduling and predictive maintenance for minimizing job tardiness. Int J Adv Manuf Technol 60(9–12):1049–1061CrossRefGoogle Scholar
  36. Rau JG (1970) Optimization and probability in systems engineering. Von Nostrand Reinhold Company, New YorkzbMATHGoogle Scholar
  37. Sahu S, Pathak VK, Mehta K, Namedo A (2014) Estimation of mean time failure in two unit parallel repairable system. Int J Recent Innov Trends Comput Commun 2(10):3155–3160Google Scholar
  38. Sarker R, Omar M, Hasan K, Essam D (2013) Hybrid evolutionary algorithm for job scheduling under machine maintenance. Appl Soft Comput 13(3):1440–1447CrossRefGoogle Scholar
  39. Senra P, Lopes I, Oliveria JA (2017) Supporting maintenance scheduling: a case study. In: 27th international conference on flexible automation and intelligent manufacturing, FAIM 2017, 27–30 June 2017, Modena, ItalyGoogle Scholar
  40. Tseng L-Y, Lin Y-T (2010) A genetic local search algorithm for minimizing total flow time in the permutation flowshop scheduling problem. Int J Prod Econ 127(1):121–128CrossRefGoogle Scholar
  41. Vadlamani S, Hosseini S (2014) A novel heuristic approach for solving aircraft landing problem with single runway. J Air Transp Manag 40:144–148CrossRefGoogle Scholar
  42. Wang L, Chu J, Wu J (2007) Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. Int J Prod Econ 107(1):151–163CrossRefGoogle Scholar
  43. Yang Z, Djurdjanovic D, Ni J (2008a) Maintenance scheduling in manufacturing systems based on predicted machine degradation. J Intell Manuf 19(1):87–98CrossRefGoogle Scholar
  44. Yang Z, Djurdjanovic D, Ni J (2008b) Maintenance scheduling in manufacturing systems based on predicted machine degradation. J Intell Manuf 19(1):87–98CrossRefGoogle Scholar
  45. Yoo J, Lee IS (2016) Parallel machine scheduling with maintenance activities. Comput Ind Eng 101:361–371CrossRefGoogle Scholar
  46. Zhang X, Zeng J (2015) A general modeling method for opportunistic maintenance modeling of multi-unit systems. Reliab Eng Syst Saf 140:176–190CrossRefGoogle Scholar
  47. Zhou X, Lu Z, Xi L (2012) Preventive maintenance optimization for a multi-component system under changing job shop schedule. Reliab Eng Syst Saf 101(2012):14–20CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Seyedmohsen Hosseini
    • 1
    Email author
  • Sifat Kalam
    • 2
  • Kash Barker
    • 2
  • Jose E. Ramirez-Marquez
    • 3
  1. 1.Industrial Engineering TechnologyUniversity of Southern MississippiLong BeachUSA
  2. 2.School of Industrial and Systems EngineeringUniversity of OklahomaNormanUSA
  3. 3.School of Systems and EnterprisesStevens Institute of TechnologyHobokenUSA

Personalised recommendations