The integration of resource allocation and time buffering for bi-objective robust project scheduling

Liang, Yangyang; Cui, Nanfang; Hu, Xuejun; Demeulemeester, Erik

doi:10.1080/00207543.2019.1636319

Cited by 22 publications

(13 citation statements)

References 37 publications

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“…The resource allocation and time buffering problems were recently introduced as a biobjective robust project scheduling model (Liang, Cui, Hu and Demeulemeester, 2019). The authors managed to fill the previous studies' gaps where integration between resource allocation and time buffering was proposed.…”

Section: Resource Allocation Scheduling Problemmentioning

confidence: 99%

Optimized resource-constrained method for project schedule compression

Elkabalawy

Moselhi

2021

ECAM

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PurposeThis paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.Design/methodology/approachThe proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.FindingsThe developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.Originality/valueThe novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

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Section: Resource Allocation Scheduling Problemmentioning

confidence: 99%

Optimized resource-constrained method for project schedule compression

Elkabalawy

Moselhi

2021

ECAM

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“…(1) well measures the schedule risk, and hence is a good proxy of the magnitude of the schedule robustness (Van de Vonder et al, 2008;Liang et al, 2019). A lower value of j jN stc   is corresponding to a more robust project schedule or production plan.…”

Section: Decision Variablesmentioning

confidence: 99%

“…This can be attributed to the fact that the objective of MABO is to minimize the SC alone under duration uncertainty, whereas our study adopts the sum of the starting time criticality as a robustness measure and also seeks to optimize the resource transfer cost at the same time. It is worth noting that MABO has proven to be superior to any existing resource allocation algorithm with the objective of minimizing the SC Liang et al, 2019). Yet, our method is capable of achieving a lower SC than MABO under a comparatively low uncertainty.…”

Section: Insert Here Tablementioning

confidence: 99%

“…The second strategy aims at a robust resource allocation, which boils down to determining the sequence of resource transfers across the activities in such a way that schedule stability is achieved. As a consequence, precedence relations will be added to the original precedence graph between activities using the same resource unit(s), and the resulting resource flow dependencies shall remain constant during project implementation (Leus & Herroelen, 2004;Liang et al, 2019). In this paper, we concentrate on the latter option to increase the schedule stability and assume the activity durations to be random, following a certain probability distribution.…”

Section: Introductionmentioning

confidence: 99%

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A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs

Wang

Demeulemeester

et al. 2019

International Journal of Production Research

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Resource allocation is one of the core issues in project scheduling to ensure the effective use of scare renewable resource, and has been regularly encountered in production systems in the manufacturing and service industries. The transfers of renewable resources between activities generally incur certain scheduling costs and affect the robustness of a certain schedule in an uncertain environment. To address this issue, a bi-objective optimization model is proposed to make the resource transfer decisions, which aims to minimize the transfer cost and maximize solution robustness in the presence of activity duration variability. The proposed model employs a novel resourceoriented flow formulation that is different from those of the previous literature. A NSGA-II and a Pareto simulated annealing (PSA) algorithm have been applied as the solution methodologies. Besides, the effectiveness of the metaheuristics are evaluated in comparison with a  -constraint method. In detail, the algorithms are carried out on a set of benchmarks and are compared to test their efficiencies based on four performance metrics: number of nondominated solutions, general distance, hypervolume and spacing. Finally, a case study of a real project further indicates that the suggested model and algorithms are applicable and beneficial to the problem in practice.

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“…Thirdly, about resource allocation, resource flow network is introduced and many algorithms have been developed, such as branch-and-bound algorithm [16], integer programming-based heuristics [17], multi-objective algorithms [18], and heuristic algorithm of maximizing the use of precedence relations [19]. In addition, the scheduling problem of integrating resource allocation and time buffering together has also been investigated [20].…”

Section: Introductionmentioning

confidence: 99%

Proactive Project Scheduling With Activity Splitting and Resource Transfer Times Under Uncertain Environments

Ning

Wang

2022

IEEE Access

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Proactive project scheduling aims to generate robust baseline schedules that are protected against the potential schedule disruptions during project execution. In this paper, we propose a new problem named the proactive resource-constrained project scheduling problem with activity split-ting and resource transfer times, and try to investigate the trade-off between the benefits of activity splitting and the drawbacks of the resulting more resource transfer times. The proposed problem is formulated into an integer programming model with considering the constraints of a maximum allowed number of splitting and a minimum period of continuous execution. Since the problem is NP-hard in the strong sense, a genetic algorithm is then developed for problem solving. From the obtained results of a designed computational experiment, we prove that the developed genetic algorithm is effective in solving the defined problem, and the benefits of activity splitting to schedule robustness improvement can be weaken with the resulting more resource transfer times among the subactivities. Besides, with the increase of the maximum allowed number of activity splitting, the decrease of the minimum continuous execution time, the decrease of resource transfer times, and the extension of project deadlines, schedule robustness increases.

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The integration of resource allocation and time buffering for bi-objective robust project scheduling

Cited by 22 publications

References 37 publications

Optimized resource-constrained method for project schedule compression

Optimized resource-constrained method for project schedule compression

A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs

Proactive Project Scheduling With Activity Splitting and Resource Transfer Times Under Uncertain Environments

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