3,432 research outputs found

    Solution and quality robust project scheduling: a methodological framework.

    Get PDF
    The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a methodological framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of Critical Chain scheduling and indicate in which environments it is useful.Framework; Information; Management; Processes; Project management; Project scheduling; Project scheduling under uncertainty; Stability; Robust scheduling; Quality; Scheduling; Stability; Uncertainty;

    A Parallel Branch and Bound Algorithm for the Resource Leveling Problem with Minimal Lags

    Full text link
    [EN] The efficient use of resources is a key factor to minimize the cost while meeting time deadlines and quality requirements; this is especially important in construction projects where field operations take fluctuations of resources unproductive and costly. Resource Leveling Problems (RLP) aim to sequence the construction activities that maximize the resource consumption efficiency over time, minimizing the variability. Exact algorithms for the RLP have been proposed throughout the years to offer optimal solutions; however, these problems require a vast computational capability ( combinatorial explosion ) that makes them unpractical. Therefore, alternative heuristic and metaheuristic algorithms have been suggested in the literature to find local optimal solutions, using different libraries to benchmark optimal values; for example, the Project Scheduling Problem LIBrary for minimal lags is still open to be solved to optimality for RLP. To partially fill this gap, the authors propose a Parallel Branch and Bound algorithm for the RLP with minimal lags to solve the RLP with an acceptable computational effort. This way, this research contributes to the body of knowledge of construction project scheduling providing the optimums of 50 problems for the RLP with minimal lags for the first time, allowing future contributors to benchmark their heuristics meth-ods against exact results by obtaining the distance of their solution to the optimal values. Furthermore, for practitioners,the time required to solve this kind of problem is reasonable and practical, considering that unbalanced resources can risk the goals of the construction project.This research was supported by the FAPA program of the Universidad de Los Andes (Colombia). The authors would like to thank the research group of Construction Engineering and Management (INgeco), especially J. S. Rojas-Quintero, and the Department of Systems Engineering at the Universidad de Los Andes. The authors are also grateful to the anonymous reviewers for their valuable and constructive suggestions.Ponz Tienda, JL.; Salcedo-Bernal, A.; Pellicer Armiñana, E. (2017). A Parallel Branch and Bound Algorithm for the Resource Leveling Problem with Minimal Lags. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING. 32:474-498. doi:10.1111/mice.12233S47449832Adeli, H. (2000). High-Performance Computing for Large-Scale Analysis, Optimization, and Control. Journal of Aerospace Engineering, 13(1), 1-10. doi:10.1061/(asce)0893-1321(2000)13:1(1)ADELI, H., & KAMAL, O. (2008). Parallel Structural Analysis Using Threads. Computer-Aided Civil and Infrastructure Engineering, 4(2), 133-147. doi:10.1111/j.1467-8667.1989.tb00015.xAdeli, H., & Kamal, O. (1992). Concurrent analysis of large structures—II. applications. Computers & Structures, 42(3), 425-432. doi:10.1016/0045-7949(92)90038-2Adeli, H., Kamat, M. P., Kulkarni, G., & Vanluchene, R. D. (1993). High‐Performance Computing in Structural Mechanics and Engineering. Journal of Aerospace Engineering, 6(3), 249-267. doi:10.1061/(asce)0893-1321(1993)6:3(249)Adeli, 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., & Kumar, S. (1995). Concurrent Structural Optimization on Massively Parallel Supercomputer. Journal of Structural Engineering, 121(11), 1588-1597. doi:10.1061/(asce)0733-9445(1995)121:11(1588)ADELI, H., & VISHNUBHOTLA, P. (2008). Parallel Processing. Computer-Aided Civil and Infrastructure Engineering, 2(3), 257-269. doi:10.1111/j.1467-8667.1987.tb00150.xAdeli, 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)Alsayegh, H., & Hariga, M. (2012). Hybrid meta-heuristic methods for the multi-resource leveling problem with activity splitting. Automation in Construction, 27, 89-98. doi:10.1016/j.autcon.2012.04.017Anagnostopoulos, K., & Koulinas, G. (2012). Resource-Constrained Critical Path Scheduling by a GRASP-Based Hyperheuristic. Journal of Computing in Civil Engineering, 26(2), 204-213. doi:10.1061/(asce)cp.1943-5487.0000116Anagnostopoulos, K. P., & Koulinas, G. K. (2010). A simulated annealing hyperheuristic for construction resource levelling. Construction Management and Economics, 28(2), 163-175. doi:10.1080/01446190903369907Arditi, 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.xBandelloni, M., Tucci, M., & Rinaldi, R. (1994). Optimal resource leveling using non-serial dyanamic programming. European Journal of Operational Research, 78(2), 162-177. doi:10.1016/0377-2217(94)90380-8Benjaoran, V., Tabyang, W., & Sooksil, N. (2015). Precedence relationship options for the resource levelling problem using a genetic algorithm. Construction Management and Economics, 33(9), 711-723. doi:10.1080/01446193.2015.1100317Bianco, L., Caramia, M., & Giordani, S. (2016). Resource levelling in project scheduling with generalized precedence relationships and variable execution intensities. OR Spectrum, 38(2), 405-425. doi:10.1007/s00291-016-0435-1Chakroun, I., & Melab, N. (2015). Towards a heterogeneous and adaptive parallel Branch-and-Bound algorithm. Journal of Computer and System Sciences, 81(1), 72-84. doi:10.1016/j.jcss.2014.06.012Christodoulou, S. E., Ellinas, G., & Michaelidou-Kamenou, A. (2010). Minimum Moment Method for Resource Leveling Using Entropy Maximization. Journal of Construction Engineering and Management, 136(5), 518-527. doi:10.1061/(asce)co.1943-7862.0000149Clausen, J., & Perregaard, M. (1999). Annals of Operations Research, 90, 1-17. doi:10.1023/a:1018952429396Coughlan, E. T., Lübbecke, M. E., & Schulz, J. (2010). A Branch-and-Price Algorithm for Multi-mode Resource Leveling. Lecture Notes in Computer Science, 226-238. doi:10.1007/978-3-642-13193-6_20Coughlan, E. T., Lübbecke, M. E., & Schulz, J. (2015). A branch-price-and-cut algorithm for multi-mode resource leveling. European Journal of Operational Research, 245(1), 70-80. doi:10.1016/j.ejor.2015.02.043Crainic, T. G., Le Cun, B., & Roucairol, C. (s. f.). Parallel Branch-and-Bound Algorithms. Parallel Combinatorial Optimization, 1-28. doi:10.1002/9780470053928.ch1Damci, 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.12038Damci, A., Arditi, D., & Polat, G. (2013). Multiresource Leveling in Line-of-Balance Scheduling. Journal of Construction Engineering and Management, 139(9), 1108-1116. doi:10.1061/(asce)co.1943-7862.0000716Damci, A., Arditi, D., & Polat, G. (2015). Impacts of different objective functions on resource leveling in Line-of-Balance scheduling. KSCE Journal of Civil Engineering, 20(1), 58-67. doi:10.1007/s12205-015-0578-7De Reyck, B., & Herroelen, W. (1996). On the use of the complexity index as a measure of complexity in activity networks. European Journal of Operational Research, 91(2), 347-366. doi:10.1016/0377-2217(94)00344-0Hossein Hashemi Doulabi, S., Seifi, A., & Shariat, S. Y. (2011). Efficient Hybrid Genetic Algorithm for Resource Leveling via Activity Splitting. Journal of Construction Engineering and Management, 137(2), 137-146. doi:10.1061/(asce)co.1943-7862.0000261Drexl, A., & Kimms, A. (2001). Optimization guided lower and upper bounds for the resource investment problem. Journal of the Operational Research Society, 52(3), 340-351. doi:10.1057/palgrave.jors.2601099Easa, S. M. (1989). Resource Leveling in Construction by Optimization. Journal of Construction Engineering and Management, 115(2), 302-316. doi:10.1061/(asce)0733-9364(1989)115:2(302)El-Rayes, K., & Jun, D. H. (2009). Optimizing Resource Leveling in Construction Projects. Journal of Construction Engineering and Management, 135(11), 1172-1180. doi:10.1061/(asce)co.1943-7862.0000097Florez, L., Castro-Lacouture, D., & Medaglia, A. L. (2013). Sustainable workforce scheduling in construction program management. Journal of the Operational Research Society, 64(8), 1169-1181. doi:10.1057/jors.2012.164Gaitanidis, A., Vassiliadis, V., Kyriklidis, C., & Dounias, G. (2016). Hybrid Evolutionary Algorithms in Resource Leveling Optimization. Proceedings of the 9th Hellenic Conference on Artificial Intelligence - SETN ’16. doi:10.1145/2903220.2903227Gather, T., Zimmermann, J., & Bartels, J.-H. (2010). Exact methods for the resource levelling problem. Journal of Scheduling, 14(6), 557-569. doi:10.1007/s10951-010-0207-8Georgy, M. E. (2008). Evolutionary resource scheduler for linear projects. Automation in Construction, 17(5), 573-583. doi:10.1016/j.autcon.2007.10.005Hariga, M., & El-Sayegh, S. M. (2011). Cost Optimization Model for the Multiresource Leveling Problem with Allowed Activity Splitting. Journal of Construction Engineering and Management, 137(1), 56-64. doi:10.1061/(asce)co.1943-7862.0000251Harris, R. B. (1990). Packing Method for Resource Leveling (Pack). Journal of Construction Engineering and Management, 116(2), 331-350. doi:10.1061/(asce)0733-9364(1990)116:2(331)Hegazy, T. (1999). Optimization of Resource Allocation and Leveling Using Genetic Algorithms. Journal of Construction Engineering and Management, 125(3), 167-175. doi:10.1061/(asce)0733-9364(1999)125:3(167)Heon Jun, D., & El-Rayes, K. (2011). Multiobjective Optimization of Resource Leveling and Allocation during Construction Scheduling. Journal of Construction Engineering and Management, 137(12), 1080-1088. doi:10.1061/(asce)co.1943-7862.0000368Hiyassat, M. A. S. (2000). Modification of Minimum Moment Approach in Resource Leveling. Journal of Construction Engineering and Management, 126(4), 278-284. doi:10.1061/(asce)0733-9364(2000)126:4(278)Hiyassat, M. A. S. (2001). Applying Modified Minimum Moment Method to Multiple Resource Leveling. Journal of Construction Engineering and Management, 127(3), 192-198. doi:10.1061/(asce)0733-9364(2001)127:3(192)Ismail, M. M., el-raoof, O. abd, & Abd EL-Wahed, W. F. (2014). A Parallel Branch and Bound Algorithm for Solving Large Scale Integer Programming Problems. Applied Mathematics & Information Sciences, 8(4), 1691-1698. doi:10.12785/amis/080425Kolisch, 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)00170-1Koulinas, G. K., & Anagnostopoulos, K. P. (2013). A new tabu search-based hyper-heuristic algorithm for solving construction leveling problems with limited resource availabilities. Automation in Construction, 31, 169-175. doi:10.1016/j.autcon.2012.11.002Lai, T.-H., & Sahni, S. (1984). Anomalies in parallel branch-and-bound algorithms. Communications of the ACM, 27(6), 594-602. doi:10.1145/358080.358103Leu, S.-S., Yang, C.-H., & Huang, J.-C. (2000). Resource leveling in construction by genetic algorithm-based optimization and its decision support system application. Automation in Construction, 10(1), 27-41. doi:10.1016/s0926-5805(99)00011-4Li, H., Xu, Z., & Demeulemeester, E. (2015). Scheduling Policies for the Stochastic Resource Leveling Problem. Journal of Construction Engineering and Management, 141(2), 04014072. doi:10.1061/(asce)co.1943-7862.0000936Lim, 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.12073Menesi, W., & Hegazy, T. (2015). Multimode Resource-Constrained Scheduling and Leveling for Practical-Size Projects. Journal of Management in Engineering, 31(6), 04014092. doi:10.1061/(asce)me.1943-5479.0000338Neumann, K., Schwindt, C., & Zimmermann, J. (2003). Project Scheduling with Time Windows and Scarce Resources. doi:10.1007/978-3-540-24800-2Neumann, K., & Zimmermann, J. (1999). Methods for Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions and Schedule-Dependent Time Windows. International Series in Operations Research & Management Science, 261-287. doi:10.1007/978-1-4615-5533-9_12Neumann, K., & Zimmermann, J. (2000). Procedures for resource leveling and net present value problems in project scheduling with general temporal and resource constraints. European Journal of Operational Research, 127(2), 425-443. doi:10.1016/s0377-2217(99)00498-1Nübel, H. (2001). The resource renting problem subject to temporal constraints. OR-Spektrum, 23(3), 359-381. doi:10.1007/pl00013357Perregaard, M., & Clausen, J. (1998). Annals of Operations Research, 83, 137-160. doi:10.1023/a:1018903912673Ponz-Tienda, J. L., Pellicer, 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.12166Ponz-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.2012.10.003Pritsker, A. A. B., Waiters, L. J., & Wolfe, P. M. (1969). Multiproject Scheduling with Limited Resources: A Zero-One Programming Approach. Management Science, 16(1), 93-108. doi:10.1287/mnsc.16.1.93Ranjbar, M. (2013). A path-relinking metaheuristic for the resource levelling problem. Journal of the Operational Research Society, 64(7), 1071-1078. doi:10.1057/jors.2012.119Rieck, J., & Zimmermann, J. (2014). Exact Methods for Resource Leveling Problems. Handbook on Project Management and Scheduling Vol.1, 361-387. doi:10.1007/978-3-319-05443-8_17Rieck, J., Zimmermann, J., & Gather, T. (2012). Mixed-integer linear programming for resource leveling problems. European Journal of Operational Research, 221(1), 27-37. doi:10.1016/j.ejor.2012.03.003Saleh, A., & Adeli, H. (1994). Microtasking, Macrotasking, and Autotasking for Structural Optimization. Journal of Aerospace Engineering, 7(2), 156-174. doi:10.1061/(asce)0893-1321(1994)7:2(156)Saleh, A., & Adeli, H. (1994). Parallel Algorithms for Integrated Structural/Control Optimization. Journal of Aerospace Engineering, 7(3), 297-314. doi:10.1061/(asce)0893-1321(1994)7:3(297)Son, J., & Mattila, K. G. (2004). Binary Resource Leveling Model: Activity Splitting Allowed. Journal of Construction Engineering and Management, 130(6), 887-894. doi:10.1061/(asce)0733-9364(2004)130:6(887)Son, J., & Skibniewski, M. J. (1999). Multiheuristic Approach for Resource Leveling Problem in Construction Engineering: Hybrid Approach. Journal of Construction Engineering and Management, 125(1), 23-31. doi:10.1061/(asce)0733-9364(1999)125:1(23)Tang, Y., Liu, R., & Sun, Q. (2014). Two-Stage Scheduling Model for Resource Leveling of Linear Projects. Journal of Construction Engineering and Management, 140(7), 04014022. doi:10.1061/(asce)co.1943-7862.0000862Wah, Guo-jie Li, & Chee Fen Yu. (1985). Multiprocessing of Combinatorial Search Problems. Computer, 18(6), 93-108. doi:10.1109/mc.1985.1662926Yeniocak , H. 2013 An efficient branch and bound algorithm for the resource leveling problem Ph.D. dissertation, Middle East Technical University, School of Natural and Applied SciencesYounis, M. A., & Saad, B. (1996). Optimal resource leveling of multi-resource projects. Computers & Industrial Engineering, 31(1-2), 1-4. doi:10.1016/0360-8352(96)00116-

    The Multimode Resource Constrained Project Scheduling Problem for Repetitive Activities in Construction Projects

    Full text link
    [EN] In construction projects, resource availability might limit the implementation of ideal schedules. Especially, when repetitive activities are involved, traditional resource¿constrained project scheduling problem (RCPSP) models fail to allocate the resource consumption in an efficient manner. Besides, actual models only provide local optimal solutions and do not incorporate activity acceleration routines. To fulfill this gap, partially, a mathematical optimization model, the multimode RCPSP for repetitive activities in construction projects, is proposed and solved to optimality; it takes into account acceleration routines under real construction scenarios using spreadsheets. The article shows a complete computational experimentation over a real construction project, considering several scenarios of resource availabilities and continuity conditions. The model allows analyzing the resources efficiency indexes comparing them to resource consumptions, continuity of activities, and objective functions that reveal that fragmented activities do not provide better resource efficiency outcomes.This research was partially supported by the FAPA program of Universidad de Los Andes, Colombia (code P14.246922.005/01). The authors would also like to thank the research group of Construction Engineering and Management (INgeco) at Universidad de los Andes.García-Nieves, J.; Ponz-Tienda, JL.; Salcedo-Bernal, A.; Pellicer Armiñana, E. (2018). The Multimode Resource Constrained Project Scheduling Problem for Repetitive Activities in Construction Projects. Computer-Aided Civil and Infrastructure Engineering. 33(8):655-671. https://doi.org/10.1111/mice.12356S65567133

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

    Get PDF
    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

    Computational experience with a branch-and-bound procedure for the resource-constrained project scheduling problem with generalized precedence relations.

    Get PDF
    In a previous paper (De Reyck and Herroelen, 1996a), we presented an optimal procedure for the resource-constrained project scheduling problem (RCPSP) with generalised precedence relations (further denoted as RCPSP-GPR) with the objective of minimizing the project makespan. The RCPSP-GPR extends the RCPSP to arbitrary minimal and maximal time lags between the starting and completion times of activities. The procedure is a depth-first branch -and-bound algorithm in which the nodes in the search tree represent the original project network extended with extra precedence relations, which resolve a resource conflict present in the project network of the parent node. Resource conflicts are resolved using the concept of minimal delaying alternatives, i.e. minimal sets of activities which, when delayed, release enough resources to resolve the conflict. Precedence- and resource-based lower bounds as well as dominance rules are used to fathom large portions of the search tree. In this paper we report new computational experience with the algorithm using a new RCPSP-GPR random problem generator developed by Schwindt (1995). A comparison with other computational results reported in the literature is included.Scheduling;

    Airport under Control:Multi-agent scheduling for airport ground handling

    Get PDF

    A branch-and-bound procedure for the resource-constrained project scheduling problem with generalized precedence relations.

    Get PDF
    We present an optimal procedure for the resource-constrained project scheduling problem (RCPSP) with generalized precedence relations (further denoted as RCPSP-GPR) with the objective of minimizing the project makespan. The RCPSP-GPR extends the RCPSP to arbitrary minimal and maximal time lags between the starting and completion times of activities. The procedure is a depth-first branch-and-bound algorithm in which the nodes in the search tree represent the original project network extended with extra precedence relations which resolve a resource conflict present in the parent node. Resource conflicts are resolved using the concept of minimal delaying alternatives, i.e. minimal sets of activities which, when delayed, release enough resources to resolve the conflict. Precedence and resource-based lower bounds as well as dominance rules are used to fathom large portions of the search tree. The procedure can be extended to other regular measures of performance by some minor modifications. Even non-regular measures of performance, such as the maximinization of the net present value of the project or resource levelling objectives, can be handled. The procedure has been programmed in Microsoft* Visual C++ for use on a personal computer. Extensive computational experience is obtained.Scheduling;

    Scheduling of Construction Projects under Resource-Constrained Conditions with a Specifically Developed Software using Genetic Algorithms

    Get PDF
    The purpose of this study is to develop a genetic algorithm (GA) based software that can perform resource allocation close to optimum and that can determine the critical path by minimizing the project duration according to the resource profile for a present work schedule and resource pool using a programmable objective function. In this context, the methodology of GAs was presented, the software was developed and the performance of this software was tested with a sample project. With the developed software, by minimizing the activity durations in both constrained and unconstrained resource conditions, projects can be scheduled, total duration and the critical path of the projects can be determined. With this software, any construction company will be able to determine how much time would be required to complete a project at the bidding stage by considering its resources and constraints and can take the required precautions. The main difference of this present study is that the developed code performs minimization of schedule duration integrated with resource allocation and levelling. It also determines the critical path of the final solutions. Both renewable and non-renewable resources are included in the code which is not often considered in the literature. By minimizing project duration and optimizing resource allocation, construction projects can become more sustainable, and the environmental impact of the construction process could be minimized

    An optimal procedure for the resource-constrained project scheduling problem with discounted cash flows and generalized precedence relations.

    Get PDF
    In this paper, we study the resource-constrained project scheduling problem (RCPSP) with discounted cash flows and generalized precedence relations (further denoted as RCPSPDC-GPR). The RCPSPDC-GPR extends the RCPSP to (a) arbitrary minimal and maximal time lags between the starting and completion times of activities and (b) the non-regular objective function of maximizing the net present value of the project with positive and/or negative cash flows associated with the activities.). To the best of our knowledge, the literature on the RCPSPDC-GPR is completely void. We present a depth-first branch-and-bound algorithm in which the nodes in the search tree represent the original project network extended with extra precedence relations which resolve a number of resource conflicts. These conflicts are resolved using the concept of a minimal delaying mode (De Reyck and Herroelen, 1996b). An upper bound on the project net present value as well as several dominance rules are used to fathom large portions of the search tree. Extensive computational experience on a randomly generated benchmark problem set is obtained.Scheduling; Optimal; Discounted cash flow; Cash flow;
    corecore