Multi-objective Project Scheduling Considering Discrete Resource Constraints Problem with Multiple Crashable Modes and Mode-identity Capabilities

زارعی، مصطفی؛ حسن‌پور، حسینعلی (1394). موازنه زمان ـ هزینه برای بیشینه‌سازی ارزش خالص فعلی پیمانکار با الگوهای پرداخت هزینه و محدودیت منابع با استفاده از الگوریتم‌های تکاملی (مطالعه موردی: بخش محدودی از پروژه احداث پالایشگاه میعانات گازی بندرعباس). مدیریت صنعتی، 7(1)، 43-64.

شهرخی، محمود (1397). ارائه رویکردی برای محاسبه قابلیت اطمینان فازی بر پایه آهنگ خرابی فازی. ه مدیریت صنعتی، 10(2)، 183-200.

صفری، حسین؛ فقیه، علیرضا (1394). حل مسائل زمان‌بندی پروژه با محدودیت منابع (RCPSP) با استفاده از الگوریتم رقابت استعماری اصلاح­شده (DICA). مدیریت صنعتی، 7(2)، 333-364.

عالم‌تبریز، اکبر؛ خالدیان، فرنوش؛ مهدی‌پور، مصطفی (1395). پیش‌بینی زمان پروژه از طریق طول زمان کسب‌شده و مدیریت ریسک. مدیریت صنعتی، 8(2)، 217-240.

References

Afshar, A., Zolfaghar Dollabi, H.R. (2014). Multi-objective optimization of time-cost-safety using genetic algorithm. International Journal of Optimization in Civil Engineering, 4 (4), 433-450.

Alamtabriz, A., Khaledian, F., & Mehdipour, M.  (2016). Forecasting project duration by Earned Duration Management and Risk Management. Journal of Industrial Management, 8(2), 217-240. (in Persian)

Blazewicz, J., Lenstra, JK. Rinnoy Kan, A., (1983). Scheduling subject to resource constraints: Classification and Complexity. Discrete Applied Mathematics, 5, 11-24.

Choi, B. Ch., & Park, M.J. (2015). A continuous time-cost trade-off problem with multiple milestones and completely ordered jobs. European Journal of Operational Research, 244 (3), 748-752.

Das, I., Dennis, J. (1998). Normal-boundary intersection: A new method for generating Pareto surface in nonlinear multicriterion optimization problems. SIAM Journal on Optimization, 8 (3), 631-657.

De, P., Dunne, E.J., Ghosh, & J.B., Wells, C.E. (1997). Complexity of the discrete time-cost trade-off problem for project network. Operations Research, 45 (2), 302-306.

Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18 (4), 577-601.

Deb, K., Pratap, A., Agrawal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE, 6 (2), 182-197.

Erenguc, SS., Ahn, T., & Conway, DG. (2001). The resource - constrained project scheduling problem with multiple crashable modes: An exact solution method. Naval Research Logistics, 48 (2), 107-27.

Fulkerson, D.R. (1961). A network flow computation for project cost curves. Management Science, 7 (2), 167-178.

Garey, MR., & Johnson, DS. (1979). Computers and intractability. San Francisco Freeman.

Ge, Q., Peng, H., Houtum, G., & Adan, I. (2018). Reliability optimization for series systems under uncertain component failure rates in the design phase. International Journal of Production Economics, 196, 163-175.

Gladysz, B., Skorupka, D., Kuchta, D., & Duchaczek, A. (2015). Project risk time management- a proposed model and a case study in the construction industry. Procedia Computer Science, 64, 24-31.

Gong, C., Zhou, W., (2017). Improvement of equivalent component approach for reliability analyses of series systems. Structural Safety, 68, 65-72.

He, Zh., He, H., Liu, R., & Wang, N. (2017). Variable neighbourhood search and tabu search for a discrete time-cost trade-off problem to minimize the maximal cash flow gap. Computers and Operations Research, 78, 564-577.

Hindelang, T.J., & Muth, J.F. (1979). A dynamic programming algorithm for decision CPM networks. Operations Research, 27 (2), 225-241.

Kelly, J.E. Jr, (1961). Critical path planning and scheduling: Mathematical basis. Operations Research, 9 (3), 296-320.

Lova, A., Tormos, P., & Barber, F. (2006). Multi mode resource - constrained project scheduling: scheduling schemes, priority rules and mode selection rules. Intelinencia Artificial, 30, 69-86.

Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27 (4), 1053-1073.

Muriana, C., & Vizzini, G. (2017). Project risk management: A deterministic quantative technique for assessment and mitigation. International Journal of Project Management, 35 (3), 320-340.

Muritiba, A., Rodrigues, C., & Costa, F., (2018). A path - relinking algorithm for the multi-mode resource - constrained project scheduling problem. Computer and Operations Research, 92, 145-154.

Nwaneri, S.C., Anyaeche, C.O. (2014). An investigation of time-cost-risk trade-off in the installation of X-Ray machine using response surface methodology. Nijerian Journal of Technology, 33 (4), 482-489.

Paryzad, B., & Shahsavari Pour, N. (2016). Time-cost-quality-risk trade-off in GIGA projects using specific techniques of hunting dolphins. International Journal of Industrial and Systems Engineering, 22 (4), 484-499.

PMI Project Management Institute. A Guide to Project Management Body of Knoledge, (PMBOK Guide) - Sixth Edition, (2017(.

Rahmati, S.H.A., Hajipour, V., & Akhavan Niaki, S.T. (2013). A soft-computing pareto-based meta-heuristic for a multi-objective multi-server facility location problem. Applied Soft Computing, 13 (4), 1728-1740.

Safari, H., & Faghih, A. (2015). Solving the Resource-Constrained Project Scheduling Problems (RCPSP) Using Developed Imperialistic Competition Algorithm (DICA). Journal of Industrial Management, 7(2), 333-364. (in Persian)

Salewski, F., Schirmer, A., & Drexel, A. (1997). Project scheduling under resource and Mode - Identity constraints: Model, Complexity, Methods and Application. European Journal of Operational Research, 102, 88-110.

Saputra, YA., & Latiffianti, E. (2015). Project reliability model considering time-cost-resource relationship under uncertainty. Procedia Computer Science, 72, 561-568.

Shahrokhi, M. (2018). Developing an Approach to Calculate Fuzzy Reliability Based on Fuzzy Failure Rate. Journal of Industrial Management, 10(2), 183-200. (in Persian)

Slowinsky, R., Soniewicki, B., & Weglarz, J. (1994). DSS for multi objective project scheduling. European Journal of Operational Research, 79, 220-229.

Szmerekovsky, J.G., & Venkateshan, P., (2012). An integer programming formulation for the project scheduling problem with irregular time-cost trade-offs. Computers and Operations Research, 39 (7), 1402-1410.

Talbot, F.B. (1982). Resource-constrained scheduling with time-resource trade-offs: The nonpreemptive case. Management Science, 28 (10), 1197-1210.

Tavana, M., Li, ZH., Mobin, M., Komaki, M., & Teymourian, E. (2016). Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS. Expert Systems with Applications, 50, 17-39.

Vanhouck, M., & Debels, D. (2007). The discrete time-cost trade off problem: extensions and heuristic procedures. Journal of Scheduling, 10 (4-5), 311-326.

Wang, G., Duan, F., & Zhou, Y. (2018). Reliability evaluation of multi-state series systems with performance sharing. Reliability Engineering and System Safety, 173, 58-63.

Xie, Y.L., Xia, D.H., Ji, L., Zho, W.N., & Huang, G.H. (2017). An inexact cost-risk balanced model for regional energy structure adjustment management and resources environmental effect analyses- a case study of Shandong province, China. Energy, 126, 374-391.

Zarei, M., & Hasanpour, H. (2015). Time-cost trade-off to maximization the net present value of contractor using evolutionary algorithms with patterns of payment and resource constraints. Journal of Industrial Management, 7(1), 43-64. (in Persian)

Zhu, G., Bard, J., & Tu, G. (2006). A branch and cut procedure for the multi mode resource – constrained project scheduling problem. Journal of Computing, 18 (3), 377-390.

Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: Methods and applications. Swiss Federal Institute of Technology Zurikh, Diss. ETH No. 13398.

Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8 (2), 172-195.

Zitzler, E., & Thiele, L. (1998). An evolutionary algorithm for Multiobjective optimization: The strength Pareto approach. Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETK), TIK Report, 43.

Zitzler, E., & Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms – A comparative case study. In: Eiben A.E., Back, T., Schoenauer, M., Schwefel, HP. (eds) Parallel problem solving from nature – PPSN V. PPSN 1998 Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, 1498, 292-301.