A Taghuchi based Multi Objective Time-Cost Constrained Scheduling for Resource Availability Cost Problem: A Case Study

Document Type: Research Paper

Authors

1 Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

2 Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.

Abstract

In this paper, a new multi-objective time-cost constrained resource availability cost problem is proposed. The mathematical model is aimed to minimize resource availability cost by considering net present value of resource prices in order to evaluate the economic aspects of project to maximize the quality of project's resources to satisfy the expectations of stakeholders and to minimize the variation of resource usage during project. Since the problem is NP-hard, to deal with the problem a simulated annealing approach is applied, also to validate our results GAMS software is used in small size test problems. Due to the dependency of SA algorithm to its initial parameters a taghuchi method is used to find the best possible SA parameters combinations to reach near optimum solutions in large size problems.

Keywords

Main Subjects


[1]     Zhu, X., Ruiz, R., Li, S., & Li, X. (2017). An effective heuristic for project scheduling with resource availability cost. European journal of operational research257(3), 746-762.

[2]     Möhring, R. H. (1984). Minimizing costs of resource requirements in project networks subject to a fixed completion time. Operations research32(1), 89-120.

[3]     Hartmann, S. (1998). A competitive genetic algorithm for resourceā€constrained project scheduling. Naval Research Logistics (NRL)45(7), 733-750.

[4]     Bouleimen, K. L. E. I. N., & Lecocq, H. O. U. S. N. I. (2003). A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. European journal of operational research149(2), 268-281.

[5]     Brucker, P. (2002). Scheduling and constraint propagation. Discrete applied mathematics123(1), 227-256.

[6]     Li, H., & Womer, N. K. (2015). Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming. European journal of operational research246(1), 20-33.

[7]     Ma, W., Che, Y., Huang, H., & Ke, H. (2016). Resource-constrained project scheduling problem with uncertain durations and renewable resources. International journal of machine learning and cybernetics7(4), 613-621.

[8]     Vanhoucke, M., & Coelho, J. (2016). An approach using SAT solvers for the RCPSP with logical constraints. European journal of operational research249(2), 577-591.

[9]     Yassine, A. A., Mostafa, O., & Browning, T. R. (2017). Scheduling multiple, resource-constrained, iterative, product development projects with genetic algorithms. Computers & industrial engineering107, 39-56.

[10] Kreter, S., Rieck, J., & Zimmermann, J. (2016). Models and solution procedures for the resource-constrained project scheduling problem with general temporal constraints and calendars. European journal of operational research251(2), 387-403.

[11] Drexl, A., & Kimms, A. (2001). Optimization guided lower and upper bounds for the resource investment problem. Journal of the operational research society52(3), 340-351.

[12] Yamashita, D. S., Armentano, V. A., & Laguna, M. (2006). Scatter search for project scheduling with resource availability cost. European journal of operational research169(2), 623-637.

[13] Yamashita, D. S., Armentano, V. A., & Laguna, M. (2007). Robust optimization models for project scheduling with resource availability cost. Journal of scheduling10(1), 67-76.

[14] Ranjbar, M., Kianfar, F., & Shadrokh, S. (2008). Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm. Applied mathematics and computation196(2), 879-888.

[15] Van Peteghem, V., & Vanhoucke, M. (2013). An artificial immune system algorithm for the resource availability cost problem. Flexible services and manufacturing journal25(1-2), 122-144.

[16] Rad, M. S., Jamili, A., Tavakkoli-Moghaddam, R., & Paknahad, M. (2016, January). Resource constraint project scheduling to meet net present value and quality objectives of the program. Proceeding of 12th International Conference on Industrial Engineering (ICIE), 58-62. 10.1109/INDUSENG.2016.7519349

[17] Yassine, A. A., Mostafa, O., & Browning, T. R. (2017). Scheduling multiple, resource-constrained, iterative, product development projects with genetic algorithms. Computers & industrial engineering107, 39-56.

[18] Kreter, S., Rieck, J., & Zimmermann, J. (2016). Models and solution procedures for the resource-constrained project scheduling problem with general temporal constraints and calendars. European journal of operational research251(2), 387-403.

[19] Tavana, M., Abtahi, A. R., & Khalili-Damghani, K. (2014). A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems. Expert systems with applications41(4), 1830-1846.

[20] Zhalechian, M., Tavakkoli-Moghaddam, R., & Rahimi, Y. (2017). A self-adaptive evolutionary algorithm for a fuzzy multi-objective hub location problem: An integration of responsiveness and social responsibility. Engineering applications of artificial intelligence62, 1-16.

[21] Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems159(2), 193-214.

[22] Verbeeck, C., Van Peteghem, V., Vanhoucke, M., Vansteenwegen, P., & Aghezzaf, E. H. (2017). A metaheuristic solution approach for the time-constrained project scheduling problem. OR spectrum39(2), 353-371.

[23] [24] Van Peteghem, V., & Vanhoucke, M. (2015). Heuristic methods for the resource availability cost problem. Handbook on project management and scheduling (pp. 339-359). Springer.

[24] Eshraghi, A. (2016). A new approach for solving resource constrained project scheduling problems using differential evolution algorithm. International journal of industrial engineering computations7(2), 205-216.

[25] Kreter, S., Rieck, J., & Zimmermann, J. (2016). Models and solution procedures for the resource-constrained project scheduling problem with general temporal constraints and calendars. European journal of operational research251(2), 387-403.

[26] Alhumrani, S. A., & Qureshi, R. J. (2016). Novel approach to solve Resource Constrained Project Scheduling Problem (RCPSP). International journal of modern education and computer science8(9), 60.

[27] Bilolikar, V. S., Jain, K., & Sharma, M. (2016). An adaptive crossover genetic algorithm with simulated annealing for multi mode resource constrained project scheduling with discounted cash flows. International journal of operational Research25(1), 28-46.

[28] Azadeh, A., Habibnejad-Ledari, H., Abdolhossein Zadeh, S., & Hosseinabadi Farahani, M. (2017). A single-machine scheduling problem with learning effect, deterioration and non-monotonic time-dependent processing times. International journal of computer integrated manufacturing30(2-3), 292-304.