307 research outputs found

    A survey of variants and extensions of the resource-constrained project scheduling problem

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    The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

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

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    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

    Meta-heuristic based Construction Supply Chain Modelling and Optimization

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    Driven by the severe competition within the construction industry, the necessity of improving and optimizing the performance of construction supply chain has been aroused. This thesis proposes three problems with regard to the construction supply chain optimization from three perspectives, namely, deterministic single objective optimization, stochastic optimization and multi-objective optimization respectively. Mathematical models for each problem are constructed accordingly and meta-heuristic algorithms are developed and applied for resolving these three problems

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

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    [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-

    A Decision Support System for Dynamic Integrated Project Scheduling and Equipment Operation Planning

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    Common practice in scheduling under limited resource availability is to first schedule activities with the assumption of unlimited resources, and then assign required resources to activities until available resources are exhausted. The process of matching a feasible resource plan with a feasible schedule is called resource allocation. Then, to avoid sharp fluctuations in the resource profile, further adjustments are applied to both schedule and resource allocation plan within the limits of feasibility constraints. This process is referred to as resource leveling in the literature. Combination of these three stages constitutes the standard approach of top-down scheduling. In contrast, when scarce and/or expensive resource is to be scheduled, first a feasible and economical resource usage plan is established and then activities are scheduled accordingly. This practice is referred to as bottom-up scheduling in the literature. Several algorithms are developed and implemented in various commercial scheduling software packages to schedule based on either of these approaches. However, in reality resource loaded scheduling problems are somewhere in between these two ends of the spectrum. Additionally, application of either of these conventional approaches results in just a feasible resource loaded schedule which is not necessarily the cost optimal solution. In order to find the cost optimal solution, activity scheduling and resource allocation problems should be considered jointly. In other words, these two individual problems should be formulated and solved as an integrated optimization problem. In this research, a novel integrated optimization model is proposed for solving the resource loaded scheduling problems with concentration on construction heavy equipment being the targeted resource type. Assumptions regarding this particular type of resource along with other practical assumptions are provided for the model through inputs and constraints. The objective function is to minimize the fraction of the execution cost of resource loaded schedule which varies based on the selected solution and thus, considered to be the model's decision making criterion. This fraction of cost which hereafter is referred to as operation cost, encompasses four components namely schedule delay cost, shipping, rental and ownership costs for equipment

    Heuristic algorithms for payment models in project scheduling

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    Imagine that the city council of Ghent has approved the construction of a new bridge across the Leie. The bridge will serve as a means to reduce traffic congestion in the city center, and the city council imposes a deadline to ensure the bridge is completed in time. Based on the specifications, a contractor subsequently determines the required resources (e.g. manpower, machines) and constructs a project schedule. This schedule holds the start and finish times of each activity (e.g. pouring concrete for the bridge foundations), and respects the imposed resource restrictions and the order in which the activities have to be executed (e.g. excavate the river banks before pouring concrete for the foundations). Whereas the objective of the client (i.e. the city council) is clear, they want the bridge to be constructed within the specified deadline, the objective for the contractor is less obvious. Is the goal to minimize the project duration, minimize total costs, maximize net present value (NPV), etc.? Assume that the contractor can construct two schedules. The first schedule minimizes the project duration, obtains a duration of 6 weeks less than the deadline and has a NPV of € 1 mio. The second schedule, on the contrary, maximizes the project NPV, which results in a duration equal to the deadline and a NPV of € 1.2 mio. The latter schedule is obtained by delaying certain activities within the imposed restrictions, starting from the first schedule. If we assume that sufficient margins are included in the proposed schedules to compensate for any delays, the contractor would obviously prefer the second schedule, since the financial return is larger. The crucial question here is, however, how the second schedule can be obtained in an effective and efficient manner starting from the first schedule. This dissertation aims to develop algorithms, which optimize the project NPV under different restrictions, by means of five studies. The first paper chapter focuses on NPV optimization subject to precedence and resource restrictions. It is furthermore assumed that both cash inflows (payments received from the client) and cash outflows (payments to subcontractors) occur at the end of each activity. This way, the size of payments is set in advance by the client and corresponds with each activity’s cash flows, whereas the timing depends on the project schedule by means of the selected activity finish times, and is controlled by the contractor. The second and third studies consider other payment models, in which the client determines the payment times in advance, rather than the size of payments. As an example, the client may stipulate that the contractor is paid every month, whereas the size of the payments depends on the work performed by the contractor in each month. Both studies furthermore include several alternatives or modes for each activity. These modes constitute different duration-resource combinations for an activity, out of which one has to be selected by the contractor, and allow for a greater degree of flexibility. The fourth paper chapter introduces capital management on the side of the contractor, by imposing that the total funds available should not become negative during the project. The total funds or cash balance consider the initial capital available and respectively add or subtract cash in- and outflows. A general model is constructed which affects the capital availability throughout the project. The fifth and final study integrates the resource availability in the scheduling process, and as such optimizes the NPV of the project including the resource usage cost, rather than decide on the amount of a resource made available first and schedule the activities second

    A Priority Rule-Based Heuristic for Resource Investment Project Scheduling Problem with Discounted Cash Flows and Tardiness Penalties

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    Resource investment problem with discounted cash flows (RIPDCFs) is a class of project scheduling problem. In RIPDCF, the availability levels of the resources are considered decision variables, and the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, we consider a new RIPDCF in which tardiness of project is permitted with defined penalty. We mathematically formulated the problem and developed a heuristic method to solve it. The results of the performance analysis of the proposed method show an effective solution approach to the problem

    Employment risk and household formation : evidence from differences in firing costs

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    The rate of new household formation among young adults who live with their parents has decreased in the last twenty years, specially in Southern Europe. At the same time, exposure to the risk that a young adult loses his or her job has increased. We use differences in firing costs across contract types in the Spanish labor market to identify if there is a causal link between both developments. Our first identification strategy exploits a legally-induced sharp increase in firing costs 3 years after the starting of a fixed-term contract between 1987 and 1996. The second uses variation in regional incentives to promote high-firing cost contracts between 1997 and 2001. Both strategies fail to detect a causal impact of job insecurity on the probability of forming a new household. Tentative evidence supports the notion that lower job insecurity has an impact on the form of tenure of the first house of residence, favoring home-ownership over rentin

    Quantitative Methods for Economics and Finance

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    This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice
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