Abstract
Resource leveling problems arise whenever it is expedient to reduce the fluctuations in resource utilization over time, while maintaining a prescribed project completion deadline. Several resource leveling objective functions may be defined, consideration of which results in well-balanced resource profiles. In this paper, we concentrate on a special objective function that determines the costs arising from increasing or decreasing the resource utilizations. The resulting total adjustment cost problem occurs, for example, in the construction industry and can be formulated using mixed-integer linear programming models. Apart from a discrete time-based formulation, two polynomial formulations, namely an event-based model and a start-based model, which exploit structural properties of the problem are presented. In addition, a heuristic solution algorithm is proposed to generate start solutions for the problem. We use CPLEX 12.4 to solve medium-scale instances known from the literature. A computational performance analysis shows that the discrete time-based model and the start-based model are suitable for practical applications.
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Ahuja, H. N. (1976). Construction performance control by networks. New York: Wiley.
Ahuja, R., Magnanti, T., & Orlin, J. (1993). Network flows. Englewood Cliffs: Prentice Hall.
Al-Joma, M. & Mangin, J.-C. (2004). A scheduling model for repetitive activities. In F. Khosrowshahi, (Ed.), Proceedings 20th Annual Association of Researchers in Construction Management (ARCOM) Conference (pp. 1197–1206), Edinburgh.
Ballestin, F., Schwindt, C., & Zimmermann, J. (2007). Resource leveling in make-to-order production: Modeling and heuristic solution method. International Journal of Operations Research, 13(2), 76–83.
Bandelloni, M., Tucci, M., & Rinaldi, R. (1994). Optimal resource leveling using non-serial dynamic programming. European Journal of Operational Research, 78, 162–177.
Bartusch, M., Möhring, R., & Radermacher, F. (1988). Scheduling project networks with resource constraints and time windows. Annals of Operations Research, 16, 201–240.
Blaskova, M., & Grazulis, V. (2009). Motivation of human potential: Theory and practice. Vilnius, Lithuania: Publishing Centre of Mykolas Romeris University.
Burgess, A., & Killebrew, J. (1962). Variation in activity level on a cyclical arrow diagram. Journal of Industrial Engineering, 13, 76–83.
Demeulemeester, E., & Herroelen, W. (2002). Project scheduling: A research handbook. Boston: Kluwer.
Easa, S. M. (1989). Resource leveling in construction by optimization. Journal of Construction Engineering and Management, 115, 302–316.
Galbreath, R. V. (1965). Computer program for leveling resource usage. Journal of the Construction Division, Proceedings of the American Society of Civil Engineers, 91, 107–124.
Gather, T., Zimmermann, J., & Bartels, J.-H. (2011). Exact methods for the resource levelling problem. Journal of Scheduling, 14(6), 557–569.
Geng, J., Weng, L., & Liu, S. (2011). An improved ant colony optimization algorithm for nonlinear resource-leveling problems. Computers and Mathematics with Applications, 61, 2300–2305.
Harris, R. B. (1978). Precedence and arrow networking techniques for construction. New York: Wiley.
Harris, R. B. (1990). Packing method for resource leveling (Pack). Journal of Construction Engineering and Management, 116, 39–43.
Harris, R. B., & Ioannou, P. G. (1998). Scheduling projects with repeating activities. Journal of Construction Engineering and Management, 124, 269–279.
Hartmann, S., & Briskorn, D. (2010). A survey of variants and extensions of the resource-constrained project scheduling problem. European Journal of Operational Research, 207, 1–14.
Haubrich, H.-J., Siemes, P., & Ohrem, S. (2008). Möglichkeiten der Netzintegration von Offshore-Großwindanlagen. In P. Dietz (Ed.), Netzintegration von Offshore-Großwindanlagen: Grundlast von der Nordsee (pp. 167–177). Clausthal-Zellerfeld: Papierflieger.
Koné, O., Artigues, C., Lopez, P., & Mongeau, M. (2011). Event-based MILP models for resource-constrained project scheduling problems. Computers and Operations Research, 38, 3–13.
Moder, J. J., & Phillips, C. R. (1970). Project Management with CPM and PERT (2nd ed.). New York: Van Nostrand Reinhold Company.
Neumann, K., Schwindt, C., & Zimmermann, J. (2003a). Order-based neighborhoods for project scheduling with nonregular objective functions. European Journal of Operational Research, 149, 325–343.
Neumann, K., Schwindt, C., & Zimmermann, J. (2003b). Project scheduling with time windows and scarce resources (2nd ed.). Berlin: Springer.
Neumann, K., Schwindt, C., & Zimmermann, J. (2006). Resource-constrained project scheduling with time windows. In J. Józefowska & J. Weglarz (Eds.), Perspectives in modern project scheduling (pp. 375–407). New York: Springer.
Neumann, K., & Zimmermann, J. (1999). Resource levelling for projects with schedule-dependent time windows. European Journal of Operational Research, 117, 591–605.
Neumann, K., & Zimmermann, J. (2000). Procedures for resource levelling and net present value problems in project scheduling with general temporal and resource constraints. European Journal of Operational Research, 127, 425–443.
Nübel, H. (2001). The resource renting problem subject to temporal constraints. OR Spektrum, 23, 359–381.
Petrovic, R. (1969). On optimization of resource leveling in project plans. In H. J. Lombaers (Ed.), Project Planning by Network Analysis: Proceedings of the Second International Congress (pp. 268–273). Amsterdam, North-Holland.
Pritsker, A. A. B., Watters, L. J., & Wolfe, P. M. (1969). Multi-project scheduling with limited resources: A zero-one programming approach. Management Science, 16, 93–108.
Raja, K., & Kumanan, S. (2007). Resource leveling using petrinet and memetic approach. American Journal of Applied Science, 4(5), 317–322.
Rieck, J., Zimmermann, J., & Gather, T. (2012). Mixed-integer linear programming for resource leveling problems. European Journal of Operational Research, 221, 27–37.
Savin, D., Alkass, S., & Fazio, P. (1997). A procedure for calculating the weight-matrix of a neural network for resource leveling. Advances in Engineering Software, 28, 277–283.
Schwindt, C. (1998). Generation of resource-constrained project scheduling problems subject to temporal constraints. WIOR-Report 543, Institute for Economic Theory and Operations Research, University Karlsruhe, Germany.
Schwindt, C. (2005). Resource allocation in project management. Berlin: Springer.
Steinboeck, A., Wild, D., & Kugi, A. (2013). Nonlinear model predictive control of a continuous slab reheating furnace. Control Engineering Practice, 21, 495–508.
Takamoto, M., Yamada, N., Kobayashi, Y., & Nonaka, H. (1995). Zero-one quadratic programming algorithm for resource leveling of manufacturing process schedules. Systems and Computers in Japan, 26(10), 68–76.
Younis, M. A., & Saad, B. (1996). Optimal resource leveling of multi-resource projects. Computers and Industrial Engineering, 31, 1–4.
Zhuchkov, S. M. (2004). Conserving energy and resources in the production of rolled sections and wire rod. Metallurgist, 48, 528–535.
Zimmermann, J. (1997). Heuristics for resource levelling problems in project scheduling with minimum and maximum time lags. WIOR-Report 491, Institute for Economic Theory and Operations Research, University Karlsruhe, Germany.
Zimmermann, J. (2001). Ablauforientiertes Projektmanagement: Modelle Verfahren und Anwendungen. Wiesbaden: Gabler.
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The benchmarks for the total adjustment cost problem presented herein and the results obtained (i.e., upper and lower bounds) may be downloaded from http://www.wiwi.tu-clausthal.de/abteilungen/unternehmensforschung/forschung.
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Kreter, S., Rieck, J. & Zimmermann, J. The total adjustment cost problem: Applications, models, and solution algorithms. J Sched 17, 145–160 (2014). https://doi.org/10.1007/s10951-013-0344-y
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DOI: https://doi.org/10.1007/s10951-013-0344-y