Proactive policies for the stochastic resource-constrained project scheduling problem

Deblaere, Filip; Demeulemeester, Erik; Herroelen, Willy

doi:10.1016/j.ejor.2011.04.019

Cited by 98 publications

(41 citation statements)

References 17 publications

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“…In [13], Freeman first applied probability theory in the project scheduling problem. After that, many scholars considered randomness in the scheduling process, such as activity durations (Ke and Liu , 2005 [18]; Zhu et al, 2007 [49]; Klerides and Hadjiconstantinou, 2010 [21]; Deblaere et al, 2011 [11]), task costs (Choi and et al 2004, [10]; Aytug et al 2005, [5]), resource consumption and availabilities (Lambrechts et al, 2008 [26]; Keller and Bayraksan, 2009 [19]) and so on. Expect for the stochastic factor mentioned above (objective uncertainty) which is from external cause (such as treacherous weather and equipment failure), the other one is from internal (such as the perception and dissension of decision makers) in the scheduling process, i.e., subjective uncertainty, and it can be cope with fuzzy theory.…”

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confidence: 99%

Bi-level multiple mode resource-constrained project scheduling problems under hybrid uncertainty

Zhang¹,

Xu²

2015

JIMO

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This study focuses on the multi-mode resource-constrained projects scheduling problem (MRCPSP), which considers the complex hierarchical organization structure and hybrid uncertainty environment in the decision making process. A bi-level multi-objective MRCPSP model with fuzzy random coefficients and bi-random coefficients is developed for the MRCPSP. In the model, construction contractor, the upper level decision maker (ULDM), aims to minimize the consumption of resources and maximize the quality level of project. Meanwhile, outsourcing partner, the lower level decision maker (LLDM), tries to schedule the activities under resource allocation with the objective of minimizing the total tardiness penalty cost. To deal with the uncertainty variables, the fuzzy random parameters are transformed into the trapezoidal fuzzy variables, which are de-fuzzified by the expected value subsequently. For the bi-random parameters, the expected value operator is employed. After obtaining the equivalent crisp model, the passive congregation-based bi-level multiple objective particle swarm optimization algorithm (PC-based BL-MOPSO) is designed to obtain the Pareto solutions. Finally, a practical application is presented to verify the practicability of the proposed bi-level multi-objective MRCPSP model and the efficiency of algorithm.2010 Mathematics Subject Classification. Primary: 93A30, 90B50.

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confidence: 99%

Bi-level multiple mode resource-constrained project scheduling problems under hybrid uncertainty

Zhang¹,

Xu²

2015

JIMO

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“…However, all these papers deal with the resource constrained project scheduling problem and its variants (see [3,9,19,22,31,32). To the best of our knowledge, this is the first contribution where both cost, completion time, and stability are investigated in the context of the DTCTP.…”

Section: Introductionmentioning

confidence: 98%

A hybrid simulation-optimization approach for the robust Discrete Time/Cost Trade-off Problem

Said

Haouari

2015

Applied Mathematics and Computation

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“…For the subjective uncertainty, many researchers are devoted to RCPSP under a fuzzy environment, such as Wang et al (2005) [14], Chen et al (2011) [2], and Masmoudi and Haït (2013) [11]. On the other hand, Ke and Liu (2005) [6], Deblaere et al (2011) [5] considered activity durations as random variable, and Choi and et al (2004) [4], Aytug et al (2005) [1] deemed task costs as stochastic. However, the subjective and objective uncertainty are considered as separate aspects, but we may face a hybrid uncertain environment in the practical RCPSP which fuzziness and randomness co-exist.…”

Section: Introductionmentioning

confidence: 99%

A MODM Bi-level Model with Fuzzy Random Coefficients for Resource-Constrained Project Scheduling Problems

Zhang

2014

2014 Seventh International Joint Conference on Computational Sciences and Optimization

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The aim of this paper is to solve resourceconstrained projects scheduling problems (RCPSP) with complex hierarchical organization structure. A bi-level MODM model with fuzzy random coefficients is developed for RCPSP under hybrid uncertainty environment. In this model, construction contractor is considered as the upper level decision maker (ULDM) and outsourcing partner is the lower level decision maker (LLDM). The bi-level multiple objective particle swarm optimization algorithm (BL-MOPSO) is designed to obtain the optimal schedule. An illustrative example is given in order to show the effectiveness of the proposed model and algorithm.

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Proactive policies for the stochastic resource-constrained project scheduling problem

Cited by 98 publications

References 17 publications

Bi-level multiple mode resource-constrained project scheduling problems under hybrid uncertainty

Bi-level multiple mode resource-constrained project scheduling problems under hybrid uncertainty

A hybrid simulation-optimization approach for the robust Discrete Time/Cost Trade-off Problem

A MODM Bi-level Model with Fuzzy Random Coefficients for Resource-Constrained Project Scheduling Problems

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