Flexible resource management and its effect on project cost and duration

Journal of Industrial Engineering International

دوره 16، شماره 1، خرداد 2020، صفحه 119-133   

نویسندگان

Desta A. Hailemariam^{* 1، 2}؛ Xiaojun Shan¹؛ Sung H. Chung¹؛ Mohammad T. Khasawneh¹؛ William Lukesh²؛ Angela Park²؛ Adam Rose^{3، 4}؛ Denis C . Pinha^{* 5}؛ Rashpal S. Ahluwalia⁵

¹Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA

²New England Veterans Engineering Resource Center, Jamaica Plain Veterans Affairs Medical Center, Boston, USA

³Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial Veterans Affairs Hospital, Bedford, MA, USA

⁴Department of Medicine, Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA

⁵West Virginia University, Morgantown, WV, USA

چکیده

In practice, most projects result in cost overruns and schedule slippage due to poor resource management. This paper presents an approach that aims at reducing project duration and costs by empowering project managers to assess different scenarios. The proposed approach addresses combinatorial modes for tasks, multi-skilled resources, and multiple calendars for resources. A case study reported in the literature is presented to demonstrate the capabilities of this method. As for practical implications, this approach enhances the decision-making process which results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this paper adds empirical evidence to enrich the existing literature, as it highlights relevant issues to model properly the complexity of real-life projects.

کلیدواژه‌ها

Resource management . Project scheduling . Discrete event simulation . Decision support system

مراجع

Abrantes R, Figueiredo J (2015) Resource management process framework for dynamic NPD portfolios. Int J Project Manage 33(6):1274–1288
Alcaraz J, Maroto C, Ruiz R (2003) Solving the multi-mode resourceconstrained project scheduling problem with genetic algorithms. J Oper Res Soc 54:614–626
AlSehaimi A, Koskela L, Tzortzopoulos P (2013) Need for alternative research approaches in construction management: case of delay studies. J Manage Eng 29(4):407–413
Arabzadeh V, Niaki STA, Arabzadeh VJ (2017) Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms. J Ind Eng Int. https://doi.org/10.1007/s40092-017-0240-8
Arau´zo J, Pajares J, Lopez-Paredes A (2010) Simulating the dynamic scheduling of project portfolios. Simul Model Pract Theory 18(10):1428–1441
Baumann P, Trautmann N (2013) Optimal scheduling of workcontent constrained projects. In: Proceedings of the IEEE
international conference on industrial engineering and engineering management

Bes¸ikci U, Bilge U, Ulusoy G (2015) Multi-mode resource constrained multi-project scheduling and resource portfolio problem. Eur J Oper Res 240(1):22–31
Bianco L, Caramia M (2013) A new formulation for the project scheduling problem under limited resources. Flex Serv Manuf J 25:6–24
Bouleimen K, Lecocq H (2003) A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur J Oper Res 149(2):268–281

Browning T, Yassine A (2010) Resource-constrained multi-project scheduling: priority rule performance revisited. Int J Prod Econ 126(2):212–228
Brucker P, Drexl A, Mohring R, Neumann K, Pesch E (1999) Resource-constrained project scheduling: notation, classification, models, and methods. Eur J Oper Res 112:3–41
Chen P, Shahandashti S (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom Constr 18(4):434–443
Cho S, Steven D (2005) A simulation-based process model for managing complex design projects. IEEE Trans Eng Manage
52(3):316–328
Chryssolouris G (1999) A planning and control method for shipyard processes: a ship-repair yard case study. In: 10th international conference on computer applications in shipbuilding, ICCAS 99
Chryssolouris G, Makris S, Xanthakis V, Mourtzis D (2004) Towards the internet-based supply chain management for the ship repair industry. J Comput Integr Manuf 17(1):45–57
Dandage RV, Mantha SS, Rane SB (2017) Analysis of interactions among barriers in project risk management. J Ind Eng Int. https://doi.org/10.1007/s40092-017-0215-9
Dao SD, Abhary K, Marian R (2017) An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling. J Ind Eng Int 13:143. https://doi.org/10.1007/s40092-016-0181-7
DoN (2013) http://www.onr.navy.mil/*/media/Files/FundingAnnouncements/BAA/2013/13-020.ashx. Accessed 7 Mar 2015
Drexl A, Nissen R, Patterson J, Salewski F (2000) Progen/px—an instance generator for resource-constrained project scheduling problems with partially renewable resources and further extensions. Eur J Oper Res 125(1):59–72
Elmaghraby S (1977) Task networks: project planning and control by network models. Wiley, New York
Fundeling C, Trautmann N (2010) A priority-rule method for project scheduling with work-content constraints. Eur J Oper Res 203:568–574
Garmdare HS, Lotfi MM, Honarvar M (2017) Integrated model for pricing, delivery time setting, and scheduling in make-to-order environments. J Ind Eng Int. https://doi.org/10.1007/s40092-017-0205-y
Garmsiri M, Abassi MR (2012) Resource leveling scheduling by an ant colony-based model. J Ind Eng Int 8:7. https://doi.org/10.1186/2251-712X-8-7
Hartmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. Eur J Oper Res 207(1):1–14
Hashemin SS, Fatemi Ghomi SMTJ (2012) Constrained consumable resource allocation in alternative stochastic networks via multiobjective decision making. J Ind Eng Int 8:18. https://doi.org/10.1186/2251-712X-8-18
Hu D, Mohamed Y (2013) Time-stepped, simulation-based scheduling system for large-scale industrial construction projects. In: Simulation conference (WSC), 2013 Winter, pp 3249–3256. https://doi.org/10.1109/wsc.2013.6721690
Ighravwe DE, Oke SA, Adebiyi KA (2016) A reliability-based maintenance technicians’ workloads optimisation model with
stochastic consideration. J Ind Eng Int 12:171. https://doi.org/10.1007/s40092-015-0134-6
Jain M, Mittal RJ (2016) Adaptive call admission control and resource allocation in multi server wireless/cellular network.
J Ind Eng Int 12:71. https://doi.org/10.1007/s40092-015-0129-3
Jo´zefowska J, Weglarz J (2006) Perspectives in modern project scheduling. Springer, New York
Jozefowska J, Mika M, Rozycki R, Waligora G, Weglarz J (2001) Simulated annealing for multi-mode resource-constrained project scheduling. Ann Oper Res 102:137–155
Khadem MMRK, Piya S, Shamsuzzoha AJ (2017) Quantitative risk management in gas injection project: a case study from Oman oil and gas industry. J Ind Eng Int. https://doi.org/10.1007/s40092-017-0237-3
Kolisch R, Drexl A (1997) Local for multi-mode resource-constrained project. IIE Trans 29(11):987–999
Laslo Z, Goldberg A (2008) Resource allocation under uncertainty in a multi-project matrix environment: is organizational conflict inevitable? Int J Project Manage 26(8):773–788
Lau S, Lu M, Poon C (2014) Formalized approach to discretize a continuous plant in construction simulations. J Const Eng
Manage 140(8):04014032

Leadership (2013) https://www.vsm.de/sites/default/files/dokumente/bc3eeb94a6e55303989d809f6512192c/leadership2020-final-repo
rt_en.pdf. Accessed 21 Oct 2015
Liu H, Al-Hussein M, Lu M (2015) BIM-based integrated approach for detailed construction scheduling under resource constraints. Autom Constr 53:29–43
Lu M (2003) Simplified discrete-event simulation approach for construction simulation. J Const Eng Manage 129(5):537–546
Lu M, Lam H, Dai F (2008) Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization. Autom Constr 17(6):670–681
MARAD (2013) https://www.marad.dot.gov/wp-content/uploads/pdf/MARAD_Econ_Study_Final_Report_2013.pdf. Accessed 16 Jul 2016
Mortaji STH, Noori S, Noorossana R (2017) An ex ante control chart for project monitoring using earned duration management observations. J Ind Eng Int. https://doi.org/10.1007/s40092-017-0251-5
Mourtzis D (2005) An integrated system for managing ship repair operations. Int J Comput Integr Manuf 18(8):721–733
MP (2015) http://office.microsoft.com/en-us/project. Accessed 11 Mar 2015
Naber A, Kolisch R (2014) MIP models for resource-constrained project scheduling with flexible resource profiles. Eur J Oper Res 239(2):335–348
Noori-Darvish S, Tavakkoli-Moghaddam R (2012) Minimizing the total tardiness and makespan in an open shop scheduling problem with sequence-dependent setup times. J Ind Eng Int 8:25. https://doi.org/10.1186/2251-712X-8-25
NRC (2009) https://www.nap.edu/catalog/12717/advancing-the-competi tiveness-and-efficiency-of-the-us-construction-industry. Accessed 6 Feb 2015
NSRP (2013) https://www.nsrp.org/portfolio_year_tag/2013/. Accessed 21 July 2016
Peteghem V, Vanhoucke M (2010) A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. Eur J Oper Res 201(2):409–418
Pinha D (2015) Short-term resource allocation and management. Ph.D. dissertation, West Virginia University, USA
Pinha D, De Queiroz MH, Cury JER (2011) Optimal scheduling of a repair shipyard based on supervisory control theory. In:
Proceedings of the ieee conference on automation science and engineering (CASE), pp 39–44
Pinha D, Ahluwalia R, Carvalho A (2015) Parallel mode schedule generation scheme. IFAC-PapersOnLine 48(3):794–799
Pinha D, Ahluwalia R, Senna P (2016) The combinatorial multi-mode resource constrained multi-project scheduling problem. Int J Supply Oper Manage 3(3):1391–1412
PMI KPMG (2013) Study on project schedule and cost overruns. http://www.infrapppworld.com/documents/reports/PMI-KPMG2013.pdf. Accessed 16 July 2016
PMI (2013b) A guide to the project management body of knowledge, 5th edn. Project Management Institute, Newtown Square, PA

Primavera (2015) https://docs.oracle.com/cd/E16688_01/Moving_From_P3_to_P6/MovingfromP3toP6.pdf. Accessed 11 Apr 2016
Pritsker A, Watters L, Wolfe P (1969) Multi project scheduling with limited resources: a zero-one programming approach. Manage Sci 16:93–108
Ranjbar M, Kianfar F (2010) Resource-constrained project scheduling problem with flexible work profiles: a genetic algorithm approach. Trans E Ind Eng 17:25–35
Rehm M, Thiede J (2012) A survey of recent methods for solving project scheduling problems. Technische Universita¨t Dresden, Fakulta¨t Wirtschaftswissenschaften
Reichelt K, Lyneis J (1999) The dynamics of project performance: benchmarking the drivers of cost and schedule overrun. Eur Manage J 17(2):135–150
Rieck J, Zimmermann J, Gather T (2012) Mixed-integer linear programming for resource leveling problems. Eur J Oper Res
221:27–37
Sabzehparvar M, Seyed-Hosseini S (2008) A mathematical model for the multimode resource-constrained project scheduling problem with mode dependent time lags. J Supercomput 44(3):257–273
Shahriari M (2016) Multi-objective optimization of discrete time–cost tradeoff problem in project networks using non-dominated sorting genetic algorithm. J Ind Eng Int 12:159. https://doi.org/10.1007/s40092-016-0148-8
Shahriari M, Shoja N, Zade AE et al (2016) JIT single machine scheduling problem with periodic preventive maintenance. J Ind Eng Int 12:299. https://doi.org/10.1007/s40092-016-0147-9
Siu M, Lu M, AbouRizk S (2014) Bi-level project simulation methodology to integrate superintendent and project manager in decision making: shutdown/turnaround applications. In: Proceedings of the 2014 winter simulation conference (WSC
‘14). IEEE Press, Piscataway, NJ, pp 3363–3364
Siu M, Lu M, AbouRizk S (2015) Zero-one programming approach to determine optimum resource supply under time-dependent resource constraints. J Comput Civ Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000498
Speranza MG, Vercellis C (1993) Hierarchical models for multiproject planning and scheduling. Eur J Oper Res 64(2):312–325
Vanhoucke M (2013) Project management with dynamic scheduling, baseline scheduling, risk analysis and project control, vol XVIII, 2nd edn. Springer, Berlin (123 illus)
Wongwai N, Malaikrisanachalee S (2011) Augmented heuristic algorithm for multi-skilled resource scheduling. Autom Constr 20(4):429–445
Xu J, Feng C (2014) Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project. Sci World J 2014:463692. https://doi.org/10.1155/2014/463692
Xue H, Wei S, Wang Y (2010) Resource-constrained multi-project scheduling based on ant colony neural network. In: Apperceiving computing and intelligence analysis (ICACIA) international conference, pp 179–182
Yaghoubi S, Noori S, Mazdeh MM (2013) A heuristic method for consumable resource allocation in multi-class dynamic PERT networks. J Ind Eng Int 9:17. https://doi.org/10.1186/2251-712X-9-17
Zhang L, Sun R (2011) An improvement of resource-constrained multi-project scheduling model based on priority-rule based heuristics. In: 8th international conference on service systems and service management (ICSSSM), pp 1–5