An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

On the use of schedule risk analysis for project management

The purpose of this paper is to give an overview on the existing literature and recent developments on the research on Schedule Risk Analysis (SRA) in Project Management (PM) to measure the sensitivity of activities and resources in the project network. SRA is a technique that relies on Monte-Carlo simulation runs to analyze the impact of changes in activity durations and costs on the overall project time and cost objectives. First, the paper gives an overview of the most commonly known sensitivity metrics from literature that are widely used by PM software tools to measure the time and cost sensitivity of activities as well as sensitivity for project resources. Second, the relevance of these metrics in an integrated project control setting is discussed based on some recent research studies. Finally, a short discussion on the challenges for future research is given. All sections in this paper are based on research studies done in the past for which references will be given throughout the manuscript

Hard or soft: Planning on medium size construction projects

Some data suggest that the approach to planning in construction seeks to impose a managed future on construction work by providing plans which are strictly time scheduled and produced by initially identifying those activities which are critical to the plan and allowing other activities to “fit in” to this critical path. This is referred to in the paper as “hard” planning. The paper seeks to demonstrate that the reality for some managers and planners is that the planning process is “soft” and that in producing plans they seek initially to take account of the vast uncertainties of construction by removing criticality from all activities. The paper is based on data obtained from longitudinal case study research of four live, medium size, projects in the North East of England. The data analysis uses the Grounded Theory approach

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

A strategic approach to supply chain event management

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2003.Includes bibliographical references (p. 35).This thesis project explores the possibility to apply project management techniques, specifically critical path method, and PERT, to supply chain event management. The idea behind the project is to create a framework for putting supply chain events into a broader supply chain context and assessing their criticality. Such a framework can then be utilized as a starting point for supply chain event management software applications. The problem has been approached from a "micro" point of view, with the analysis and PERT modeling of a single order fulfillment process, and from a "macro" point of view, with the analysis and a very simple model of the inventory itself. Finally, there are important factors that can drive the development and adoption of such systems in the future, including a higher level of supply chain informatization, removal of inter-and intra-company communication barriers, and better software integration technologies to effectively link all the element of the supply chain network.by Esmè Fantozzi.M.Eng.in Logistic

Applying Bayesian networks to model uncertainty in project scheduling

PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge. An important component of Project Risk Management (PRM) is risk analysis, which attempts to measure risk and its impact on different project parameters such as time, cost and quality. By highlighting the trade-off between project parameters, the thesis concentrates on project time management under uncertainty. The earliest research incorporating uncertainty/risk in projects started in the late 1950’s. Since then, several techniques and tools have been introduced, and many of them are widely used and applied throughout different industries. However, they often fail to capture uncertainty properly and produce inaccurate, inconsistent and unreliable results. This is evident from consistent problems of cost and schedule overrun. The thesis will argue that the simulation-based techniques, as the dominant and state-of-the-art approach for modelling uncertainty in projects, suffers from serious shortcomings. More advanced techniques are required. Bayesian Networks (BNs), are a powerful technique for decision support under uncertainty that have attracted a lot of attention in different fields. However, applying BNs in project risk management is novel. The thesis aims to show that BN modelling can improve project risk assessment. A literature review explores the important limitations of the current practice of project scheduling under uncertainty. A new model is proposed which applies BNs for performing the famous Critical Path Method (CPM) calculation. The model subsumes the benefits of CPM while adding BN capability to properly capture different aspects of uncertainty in project scheduling

The Fuzzy Project Scheduling Problem with Minimal Generalized Precedence Relations

In scheduling, estimations are affected by the imprecision of limited information on future events, and the reduction in the number and level of detail of activities. Overlapping of processes and activities requires the study of their continuity, along with analysis of the risks associated with imprecision. In this line, this paper proposes a fuzzy heuristic model for the Project Scheduling Problem with flows and minimal feeding, time and work Generalized Precedence Relations with a realistic approach to overlapping, in which the continuity of processes and activities is allowed in a discretionary way. This fuzzy algorithm handles the balance of process flows, and computes the optimal fragmentation of tasks, avoiding the interruption of the critical path and reverse criticality. The goodness of this approach is tested on several problems found in the literature; furthermore, an example of a 15-story building was used to compare the better performance of the algorithm implemented in Visual Basic for Applications (Excel) over that same example input in Primavera© P6 Professional V8.2.0, using five different scenarios.This research was supported by the FAPA program of Universidad de Los Andes, Colombia. The authors would like to thank the research group of Construction Engineering and Management (INgeco) of Universidad de Los Andes, and the five anonymous referees for their helpful and constructive suggestions.Ponz Tienda, JL.; Pellicer Armiñana, E.; Benlloch Marco, J.; Andrés Romano, C. (2015). The Fuzzy Project Scheduling Problem with Minimal Generalized Precedence Relations. Computer-Aided Civil and Infrastructure Engineering. 30(11):872-891. doi:10.1111/mice.12166S8728913011Adeli, H., & Park, H. S. (1995). Optimization of space structures by neural dynamics. Neural Networks, 8(5), 769-781. doi:10.1016/0893-6080(95)00026-vAdeli, H., & Karim, A. (1997). Scheduling/Cost Optimization and Neural Dynamics Model for Construction. Journal of Construction Engineering and Management, 123(4), 450-458. doi:10.1061/(asce)0733-9364(1997)123:4(450)Adeli, H., & Wu, M. (1998). Regularization Neural Network for Construction Cost Estimation. Journal of Construction Engineering and Management, 124(1), 18-24. doi:10.1061/(asce)0733-9364(1998)124:1(18)Alarcón, L. F., Ashley, D. B., de Hanily, A. S., Molenaar, K. R., & Ungo, R. (2011). Risk Planning and Management for the Panama Canal Expansion Program. Journal of Construction Engineering and Management, 137(10), 762-771. doi:10.1061/(asce)co.1943-7862.0000317Ammar, M. A. (2013). LOB and CPM Integrated Method for Scheduling Repetitive Projects. Journal of Construction Engineering and Management, 139(1), 44-50. doi:10.1061/(asce)co.1943-7862.0000569Arditi, D., & Bentotage, S. N. (1996). System for Scheduling Highway Construction Projects. Computer-Aided Civil and Infrastructure Engineering, 11(2), 123-139. doi:10.1111/j.1467-8667.1996.tb00316.xBai, L., Yan, L., & Ma, Z. M. (2014). Querying fuzzy spatiotemporal data using XQuery. Integrated Computer-Aided Engineering, 21(2), 147-162. doi:10.3233/ica-130454Ballesteros-Pérez, P., González-Cruz, M. C., Cañavate-Grimal, A., & Pellicer, E. (2013). Detecting abnormal and collusive bids in capped tendering. Automation in Construction, 31, 215-229. doi:10.1016/j.autcon.2012.11.036Bartusch, M., Möhring, R. H., & Radermacher, F. J. (1988). Scheduling project networks with resource constraints and time windows. Annals of Operations Research, 16(1), 199-240. doi:10.1007/bf02283745Bianco, L., & Caramia, M. (2011). Minimizing the completion time of a project under resource constraints and feeding precedence relations: a Lagrangian relaxation based lower bound. 4OR, 9(4), 371-389. doi:10.1007/s10288-011-0168-6Bonnal, P., Gourc, D., & Lacoste, G. (2004). Where Do We Stand with Fuzzy Project Scheduling? Journal of Construction Engineering and Management, 130(1), 114-123. doi:10.1061/(asce)0733-9364(2004)130:1(114)Brunelli, M., & Mezei, J. (2013). How different are ranking methods for fuzzy numbers? A numerical study. International Journal of Approximate Reasoning, 54(5), 627-639. doi:10.1016/j.ijar.2013.01.009Buckley, J. J., & Eslami, E. (2002). An Introduction to Fuzzy Logic and Fuzzy Sets. doi:10.1007/978-3-7908-1799-7Castro-Lacouture, D., Süer, G. A., Gonzalez-Joaqui, J., & Yates, J. K. (2009). Construction Project Scheduling with Time, Cost, and Material Restrictions Using Fuzzy Mathematical Models and Critical Path Method. Journal of Construction Engineering and Management, 135(10), 1096-1104. doi:10.1061/(asce)0733-9364(2009)135:10(1096)Chanas, S., & Kamburowski, J. (1981). The use of fuzzy variables in pert. Fuzzy Sets and Systems, 5(1), 11-19. doi:10.1016/0165-0114(81)90030-0In Seong Chang, Yasuhiro Tsujimura, Mitsuo Gen, & Tatsumi Tozawa. (1995). An efficient approach for large scale project planning based on fuzzy Delphi method. Fuzzy Sets and Systems, 76(3), 277-288. doi:10.1016/0165-0114(94)00385-4Chen, C.-T., & Huang, S.-F. (2007). Applying fuzzy method for measuring criticality in project network. Information Sciences, 177(12), 2448-2458. doi:10.1016/j.ins.2007.01.035Shyi-Ming Chen, & Tao-Hsing Chang. (2001). Finding multiple possible critical paths using fuzzy PERT. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 31(6), 930-937. doi:10.1109/3477.969496Damci, A., Arditi, D., & Polat, G. (2013). Resource Leveling in Line-of-Balance Scheduling. Computer-Aided Civil and Infrastructure Engineering, 28(9), 679-692. doi:10.1111/mice.12038Dell’Orco, M., & Mellano, M. (2013). A New User-Oriented Index, Based on a Fuzzy Inference System, for Quality Evaluation of Rural Roads. Computer-Aided Civil and Infrastructure Engineering, 28(8), 635-647. doi:10.1111/mice.12021Deng, H. (2014). Comparing and ranking fuzzy numbers using ideal solutions. Applied Mathematical Modelling, 38(5-6), 1638-1646. doi:10.1016/j.apm.2013.09.012De Reyck, B., & Herroelen, willy. (1998). A branch-and-bound procedure for the resource-constrained project scheduling problem with generalized precedence relations. European Journal of Operational Research, 111(1), 152-174. doi:10.1016/s0377-2217(97)00305-6De Reyck, B., & Herroelen, W. (1999). The multi-mode resource-constrained project scheduling problem with generalized precedence relations. European Journal of Operational Research, 119(2), 538-556. doi:10.1016/s0377-2217(99)00151-4Dubois, D., Fargier, H., & Galvagnon, V. (2003). On latest starting times and floats in activity networks with ill-known durations. European Journal of Operational Research, 147(2), 266-280. doi:10.1016/s0377-2217(02)00560-xElmaghraby, S. E., & Kamburowski, J. (1992). The Analysis of Activity Networks Under Generalized Precedence Relations (GPRs). Management Science, 38(9), 1245-1263. doi:10.1287/mnsc.38.9.1245Fondahl , J. W. 1961 A Non-Computer Approach to the Critical Path Method for the Construction IndustryFougères, A.-J., & Ostrosi, E. (2013). Fuzzy agent-based approach for consensual design synthesis in product configuration. Integrated Computer-Aided Engineering, 20(3), 259-274. doi:10.3233/ica-130434Gil-Aluja, J. (2004). Fuzzy Sets in the Management of Uncertainty. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-540-39699-4Hajdu, M. (1997). Network Scheduling Techniques for Construction Project Management. Nonconvex Optimization and Its Applications. doi:10.1007/978-1-4757-5951-8Harris, R. B., & Ioannou, P. G. (1998). Scheduling Projects with Repeating Activities. Journal of Construction Engineering and Management, 124(4), 269-278. doi:10.1061/(asce)0733-9364(1998)124:4(269)Hejducki, Z. (2004). Sequencing problems in methods of organising construction processes. Engineering, Construction and Architectural Management, 11(1), 20-32. doi:10.1108/09699980410512638Hebert, J. E., & Deckro, R. F. (2011). Combining contemporary and traditional project management tools to resolve a project scheduling problem. Computers & Operations Research, 38(1), 21-32. doi:10.1016/j.cor.2009.12.004Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289-306. doi:10.1016/j.ejor.2004.04.002IBM 1968Jahani, E., Muhanna, R. L., Shayanfar, M. A., & Barkhordari, M. A. (2013). Reliability Assessment with Fuzzy Random Variables Using Interval Monte Carlo Simulation. Computer-Aided Civil and Infrastructure Engineering, 29(3), 208-220. doi:10.1111/mice.12028Karim, A., & Adeli, H. (1999). OO Information Model for Construction Project Management. Journal of Construction Engineering and Management, 125(5), 361-367. doi:10.1061/(asce)0733-9364(1999)125:5(361)Karim, A., & Adeli, H. (1999). CONSCOM: An OO Construction Scheduling and Change Management System. Journal of Construction Engineering and Management, 125(5), 368-376. doi:10.1061/(asce)0733-9364(1999)125:5(368)KARIM, A., & ADELI, H. (1999). A new generation software for construction scheduling and management. Engineering, Construction and Architectural Management, 6(4), 380-390. doi:10.1108/eb021126Kim, S.-G. (2012). CPM Schedule Summarizing Function of the Beeline Diagramming Method. Journal of Asian Architecture and Building Engineering, 11(2), 367-374. doi:10.3130/jaabe.11.367Kis, T. (2005). A branch-and-cut algorithm for scheduling of projects with variable-intensity activities. Mathematical Programming, 103(3), 515-539. doi:10.1007/s10107-004-0551-6Kolisch, R., & Sprecher, A. (1997). PSPLIB - A project scheduling problem library. European Journal of Operational Research, 96(1), 205-216. doi:10.1016/s0377-2217(96)00170-1Krishnan, V., Eppinger, S. D., & Whitney, D. E. (1997). A Model-Based Framework to Overlap Product Development Activities. Management Science, 43(4), 437-451. doi:10.1287/mnsc.43.4.437LEACHMAN, R. C., DTNCERLER, A., & KIM, S. (1990). Resource-Constrained Scheduling of Projects with Variable-Intensity Activities. IIE Transactions, 22(1), 31-40. doi:10.1080/07408179008964155Lim, T.-K., Yi, C.-Y., Lee, D.-E., & Arditi, D. (2014). Concurrent Construction Scheduling Simulation Algorithm. Computer-Aided Civil and Infrastructure Engineering, 29(6), 449-463. doi:10.1111/mice.12073Long, L. D., & Ohsato, A. (2008). Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. International Journal of Project Management, 26(6), 688-698. doi:10.1016/j.ijproman.2007.09.012Lootsma, F. A. (1989). Stochastic and fuzzy Pert. European Journal of Operational Research, 43(2), 174-183. doi:10.1016/0377-2217(89)90211-7Malcolm, D. G., Roseboom, J. H., Clark, C. E., & Fazar, W. (1959). Application of a Technique for Research and Development Program Evaluation. Operations Research, 7(5), 646-669. doi:10.1287/opre.7.5.646Maravas, A., & Pantouvakis, J.-P. (2011). Fuzzy Repetitive Scheduling Method for Projects with Repeating Activities. Journal of Construction Engineering and Management, 137(7), 561-564. doi:10.1061/(asce)co.1943-7862.0000319PONZ TIENDA, J. L., BENLLOCH MARCO, J., ANDRÉS ROMANO, C., & SENABRE, D. (2011). Un algoritmo matricial RUPSP / GRUPSP «sin interrupción» para la planificación de la producción bajo metodología Lean Construction basado en procesos productivos. Revista de la construcción, 10(2), 90-103. doi:10.4067/s0718-915x2011000200009Ponz-Tienda, J. L., Pellicer, E., & Yepes, V. (2012). Complete fuzzy scheduling and fuzzy earned value management in construction projects. Journal of Zhejiang University SCIENCE A, 13(1), 56-68. doi:10.1631/jzus.a1100160Ponz-Tienda, J. L., Yepes, V., Pellicer, E., & Moreno-Flores, J. (2013). The Resource Leveling Problem with multiple resources using an adaptive genetic algorithm. Automation in Construction, 29, 161-172. doi:10.1016/j.autcon.2012.10.003Prade, H. (1979). Using fuzzy set theory in a scheduling problem: A case study. Fuzzy Sets and Systems, 2(2), 153-165. doi:10.1016/0165-0114(79)90022-8Quintanilla, S., Pérez, Á., Lino, P., & Valls, V. (2012). Time and work generalised precedence relationships in project scheduling with pre-emption: An application to the management of Service Centres. European Journal of Operational Research, 219(1), 59-72. doi:10.1016/j.ejor.2011.12.018Rommelfanger, H. J. (1994). Network analysis and information flow in fuzzy environment. Fuzzy Sets and Systems, 67(1), 119-128. doi:10.1016/0165-0114(94)90212-7Senouci, A. B., & Adeli, H. (2001). Resource Scheduling Using Neural Dynamics Model of Adeli and Park. Journal of Construction Engineering and Management, 127(1), 28-34. doi:10.1061/(asce)0733-9364(2001)127:1(28)Seppänen, O., Evinger, J., & Mouflard, C. (2014). Effects of the location-based management system on production rates and productivity. Construction Management and Economics, 32(6), 608-624. doi:10.1080/01446193.2013.853881Shi, Q., & Blomquist, T. (2012). A new approach for project scheduling using fuzzy dependency structure matrix. International Journal of Project Management, 30(4), 503-510. doi:10.1016/j.ijproman.2011.11.003Srour, I. M., Abdul-Malak, M.-A. U., Yassine, A. A., & Ramadan, M. (2013). A methodology for scheduling overlapped design activities based on dependency information. Automation in Construction, 29, 1-11. doi:10.1016/j.autcon.2012.08.001Valls, V., & Lino, P. (2001). Annals of Operations Research, 102(1/4), 17-37. doi:10.1023/a:1010941729204Valls, V., Mart�, R., & Lino, P. (1996). A heuristic algorithm for project scheduling with splitting allowed. Journal of Heuristics, 2(1), 87-104. doi:10.1007/bf00226294Wang, Y.-M., Yang, J.-B., Xu, D.-L., & Chin, K.-S. (2006). On the centroids of fuzzy numbers. Fuzzy Sets and Systems, 157(7), 919-926. doi:10.1016/j.fss.2005.11.006Wiest, J. D. (1981). Precedence diagramming method: Some unusual characteristics and their implications for project managers. Journal of Operations Management, 1(3), 121-130. doi:10.1016/0272-6963(81)90015-2Yan, L., & Ma, Z. M. (2013). Conceptual design of object-oriented databases for fuzzy engineering information modeling. Integrated Computer-Aided Engineering, 20(2), 183-197. doi:10.3233/ica-130427Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZeng, Z., Xu, J., Wu, S., & Shen, M. (2014). Antithetic Method-Based Particle Swarm Optimization for a Queuing Network Problem with Fuzzy Data in Concrete Transportation Systems. Computer-Aided Civil and Infrastructure Engineering, 29(10), 771-800. doi:10.1111/mice.12111Zhang, X., Li, Y., Zhang, S., & Schlick, C. M. (2013). Modelling and simulation of the task scheduling behavior in collaborative product development process. Integrated Computer-Aided Engineering, 20(1), 31-44. doi:10.3233/ica-12041

Characterization of gradient estimators for stochastic activity networks

This thesis aims to characterize the statistical properties of Monte Carlo simulation-based gradient estimation techniques for performance measures in stochastic activity networks (SANs) using the estimators' variance as the comparison criterion. When analyzing SANs, both performance measures and their sensitivities (gradient, Hessian) are important. This thesis focuses on analyzing three direct gradient estimation techniques: infinitesimal perturbation analysis, the score function or likelihood ratio method, and weak derivatives. To investigate how statistical properties of the different gradient estimation techniques depend on characteristics of the SAN, we carry out both theoretical analyses and numerical experiments. The objective of these studies is to provide guidelines for selecting which technique to use for particular classes of SANs based on features such as complexity, size, shape and interconnectivity. The results reveal that a specific weak derivatives-based method with common random numbers outperforms the other direct techniques in nearly every network configuration tested