Fuzzy resource-constrained project scheduling using taboo search algorithm

Atli, Ömer; Kahraman, Cengiz

doi:10.1002/int.21552

Cited by 21 publications

(19 citation statements)

References 30 publications

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“…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. Following the study of Prade (1979) [34], which is first applied fuzzy theory to the project scheduling problems in 1979, many researchers considered the fuzzy factors in MRCPSP, such as Chen et al (2011) [9], Bhaskar et al (2011) [7], Atli and Kahraman (2012) [4]. However, the objective and subjective uncertainty are considered as separate aspects in the above literatures.…”

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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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“…Since maximization of ranking fuzzy objective function leads to the same result in comparison with maximization of the fuzzy objective function, the fuzzy objective function can be substituted with its ranking in Eq. 8as follows [32]:…”

Section: Calculating Objective Functionmentioning

confidence: 99%

“…Wang et al [31] considered fuzzy RCPSP and solved it with an e cient genetic algorithm and compared the di erent solutions gained by di erent ranking methods of fuzzy numbers. Atli and Kahraman [32] proposed a mathematical model to deal with project scheduling problem under vagueness and provided a framework of a heuristic approach for Fuzzy Resource-Constrained Project Scheduling Problem (F-RCPSP) using heuristic and metaheuristic scheduling methods. Kaur and Kumar [33] provided a fuzzy arithmetic in fully fuzzy linear programming for getting an optimal solution.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm to Maximize Net Present Value of Project Cash Flows in Resource Constrained Project Scheduling Problem with fuzzy parameters

Fathallahi

Najafi

2016

Scientia Iranica

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This paper studies a speci c resource-constrained project scheduling problem under uncertainty. To do so, the problem is investigated in fuzzy environment and the goal is to maximize the net present value of the project cash ows. The problem is rst mathematically formulated. Then, a hybrid Genetic Algorithm is proposed and tuned to solve this NP-hard problem. The performance of the proposed algorithm is evaluated with comparing two well-known metaheuristic algorithms through a set of instances. Finally, comprehensive computational results are illustrated and the results are analyzed and discussed.

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“…Therefore, uncertainty of the project parameters may not be handled by random variables due to the lack of statistical past data. In this case, fuzzy set theory may be a useful tool to study the project scheduling problems [3]. For this reason, fuzzy set theory can be easily utilized in such cases where the uncertain project parameters are estimated by project managers based on their expertise, knowledge and judgements.…”

Section: Introductionmentioning

confidence: 99%

“…Such a human expertise on these uncertain parameters may generally involve ambiguous or vague information which cannot be modelled by using stochastic or probabilistic approaches. Moreover, as it was emphasized by Atli and Kahraman [3]- [4] that some of the stochastic project scheduling models are generally computationally too expensive and theoretically too complex. For this reason, it is difficult to apply them to solve practical large-scaled project scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

An Interval Programming Based Approach for fully uncertain Resource-Constrained Project Scheduling Problem Considering Project Manager’s Attitude toward Risk

Subulan¹

2019

Pamukkale J Eng Sci

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In recent years, there has been a growing attention to model and solve resource-constrained project scheduling problem (RCPSP) under uncertain environments. In most of the real-life cases, project managers may face with many uncertainties in activity durations, resource availabilities, resource requirements of the activities, the earliest and latest finishing times of the activities etc. In addition to these input parameters, project schedule which represents the starting and/or completion times of the activities should also be considered as uncertain variables in such a fully uncertain environments where all of the project data are imprecise. Based on this motivation, this paper presents an interval programming based transformation approach to overcome fully uncertain nature of the problem. In detail, classical discrete-time binary integer programming model of the deterministic problem was extended by incorporating interval-valued parameters and decision variables. Then, fully uncertain RCPSP was transformed into the crisp equivalent form by making use of interval programming, interval ranking and interval arithmetic operations. In the proposed approach, interval arithmetic operations are performed by using supplementary information obtained from the project manager. Thus, the proposed approach is also able to take into account the project managers' attitude toward risk and produces more acceptable and risk-free solutions. Finally, a real-life liquefied natural gas (LNG) storage tank construction project in a petroleum refinery is presented for testing its validity and practicality. The computational results have shown that more applicable and information efficient project schedules can be derived via the proposed approach according to the project manager's attitude toward risk.

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Fuzzy resource-constrained project scheduling using taboo search algorithm

Cited by 21 publications

References 30 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 Genetic Algorithm to Maximize Net Present Value of Project Cash Flows in Resource Constrained Project Scheduling Problem with fuzzy parameters

An Interval Programming Based Approach for fully uncertain Resource-Constrained Project Scheduling Problem Considering Project Manager’s Attitude toward Risk

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