Abstract
The paper deals with a class of problems often met in modern project management under the term “resource leveling optimization problems”. The problems of this kind refer to the optimal allocation of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers in facing project complexity, controlling related budgeting and finances and managing the construction production line. For the effective resolution of resource leveling optimization problems, the use of nature inspired intelligent methodologies is proposed. Traditional approaches, such as exhaustive or greedy search methodologies, often fail to provide near-optimum solutions in a short amount of time, whereas the proposed intelligent approaches manage to timely achieve high quality near-optimal solutions. In the paper, extensive experimental results are presented, based on available data collections existing in literature for a number of known benchmark project management problems. The comparative analysis of three different intelligent metaheuristics, shows that a hybrid nature inspired intelligent approach, combining ant colony optimization and genetic algorithms, proves to be the most effective approach in the majority of benchmark problems and special decision making settings tested.
Similar content being viewed by others
References
Antill JM, Woodhead RW (1982) Critical path methods in construction practice, 3rd edn. Wiley, London
Bandelloni M, Tucci M, Rinaldi R (1994) Optimal resource leveling using non-serial dyanamic programming. Euro J Oper Res 78:162–177
Burges A, Killebrew J (1962) Variation in activity level in a cyclical arrow diagram. J Ind Eng 13(2):76–83
Chan WT, Chua DKH, Kannan G (1996) Construction resource scheduling with genetic algorithms. J Constr Eng Manag 122:125–132
Chen ZY, Du ZD, Zhou H (2007) Research on the unlimited resource leveling optimization with PSO. China Civil Eng J 40:93–96
Dorigo M, Stultze T (2004) Ant colony optimization. The MIT Press, USA
Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. In: Gendreau M, Potvin Y (Eds) Handbook of metaheuristics, 2nd edition. International Series in Operations Research and Management Science, Springer, Verlag, New York 146:227–263
Elwany MH, Korish IE, Barakat MA, Hafez SM (1998) Resource smoothening in repetitive projects. Comput Ind Eng 35:415–418
Galbreath R (1965) Computer program for leveling resource usage. J Constr Div, ASCE 91(1):107–124
Geng JQ, Weng LP, Liu SH (2011) An improved ant colony optimization algorithm for nonlinear resource-leveling problems. Comput Math Appl 61:2300–2305
Georgy ME (2008) Evolutionary resource scheduler for linear projects. Autom Constr 17:573–583
Guo X, Li N, Li XS (2010) Multi-resource leveling in multiple projects and vector evaluated particle swarm optimization based on Pareto. Control Deci 25:789–793
Harris RB (1990) Packing method for resource leveling _PACK. J Constr Eng Manag ASCE 116(2):331–350
Hegazy T (1999) Optimization of resource allocation and leveling using genetic algorithm. J Constr Eng Manag 125(3):167–175
Holland JH (1992) Genetic algorithms. Scient Am 267(1):66–72
Holsapple CW, Jacob VS, Whinston AB (1994) Operations research and artificial intelligence. Ablex Publishing Corporation, New York
Hu Y, Xie J, Liu C (2005) Using hopfield to solve resource-leveling problem. J Comput Inf Syst 1:235–240
Huang JW, Wang XX, Chen R (2010) Genetic algorithms for optimization of resource allocation in large scale construction project management. J Comput 5:1916–1924
Ippolito MG, Morana G, Riva Sanseverino E, Vuinovich F (2005) Ant colony search algorithm for optimal strategical planning of electrical distribution systems expansion. Appl Intel 23:139–152
Jeetendra VA, Krishnaiah COV, Prashanth R (2000) Petri nets for project management and resource levelling. Int J Adv Manuf Technol 16:516–520
Kartam N, Tongthong T (1998) An artificial neural network for resource leveling problems. Artif Intell Eng Des Anal Manuf 12:273–287
Kelley J (1961) Critical path planning and scheduling: mathematical basis. Oper Res 9(3):296–320
Kelley J, Walker M (1959) Critical-path planning and scheduling. Proceedings of the Eastern Joint Computer Conference, Boston, USA, pp 130–143
Leu SS, Hung TH (2002) An optimal construction resource leveling scheduling simulation model. Can J Civil Eng 29:267–275
Leu SS, Yang CH (1999) GA-based multicriteria optimal model for construction scheduling. J Constr Eng Manag 125:420–427
Leu SS, Chen AT, Huang CH (1999) A fuzzy optimal model for construction resource leveling scheduling. Can J Civil Eng 26:673–684
Leu SS, Yang CH, Huang JC (2000) Resource leveling in construction by genetic algorithm-based optimization and its decision support system application. Automa Constr 10:27–41
Li JH (2010) Combination of genetic and ant colony algorithms for multi-project resource leveling problem. Comput Integr Manuf Syst CIMS 16:643–649
Lim A, Lin J, Xiao F (2007) Particle swarm optimization and hill climbing for the bandwidth minimization problem. Appl Intell 26:175–182
Liu SX, Wang MG (2001) Genetic algorithm for resource leveling problem in project scheduling. Syst Eng Theor Pract 21:24
Megow N, Mohring RH, Schulz J (2011) Decision support and optimization in shutdown and turnaround scheduling. INFORMS J Comput 23:189–204
Moder JJ, Philips CR, Davis EW (1983) Project management with CPM, PERT and precedence diagramming, 3rd edn. Van Nostrand-Reinhold, New York
Neumann K, Zimmermann J (1999) Resource levelling for projects with schedule-dependent time windows. Euro J Operat Res 117:591–605
Neumann K, Zimmermann J (2000) Procedures for resource leveling and net present value problems in project scheduling with general temporal and resource constraints. Euro J Oper Res 127:425–443
Nosbisch MR, Winter RM (2006) Managing resource leveling. Cost Eng Morgant W Va 48:24–34
Nudtasomboon N, Randhawa SU (1997) Resource-constrained project scheduling with renewable and non-renewable resources and time-resource tradeoffs. Comput Ind Eng 32:227–242
Raja K, Kumanan S (2007) Resource leveling using Petrinet and memetic approach. Am J Appl Sci 4:317–322
Savin D, Alkass S, Fasio P (1996) Construction resource leveling using neural networks. Can J Civil Eng 23:917–925
Senouci AB, Eldin NN (2004) Use of genetic algorithms in resource scheduling of construction projects. J Const Eng Manag 130:869–877
Shaffer L, Ritter J, Mayer W (1965) The critical path method. McGraw- Hill Book C, NY
Soak SM, Lee SW (2012) A memetic algorithm for the quadratic multiple container packing problem. Appl Intel 36:119–135
Takada M, Terano T (2003) Resource leveling scheduling system: a two-phase CLP relaxation method. Electro Commun Jpn Part II Electron 86:62–72
Wiest JD, Levy FK (1977) A management guide to PERT/CPM. Prentice-Hall, NJ
Younis MA, Saad B (1996) Optimal resource leveling of multi-resource projects. Comput Ind Eng 31:1–4
Zhou L, Peng W, Zhang Z (2010) An ant colony system for solving resource leveling problem. International Conference on Intelligent Computation Technology & Automation (ICICTA), China, 489–492
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kyriklidis, C., Vassiliadis, V., Kirytopoulos, K. et al. Hybrid nature-inspired intelligence for the resource leveling problem. Oper Res Int J 14, 387–407 (2014). https://doi.org/10.1007/s12351-014-0145-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12351-014-0145-x