Resource allocation in robust scheduling

Pang, Nansheng; Meng, Qichen

doi:10.1080/01605682.2022.2029593

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…Deblaere et al (2007) proposed integer programming algorithms for the resource flow network based on three objective functions and a heuristic algorithm for allocating resources. Pang and Meng (2023) proposed a heuristic resource allocation algorithm (MaxPR) to optimize the schedule. Some researchers (Liang et al, 2020;Shi et al, 2021) also solved the robust scheduling problem through robust resource allocation.…”

Section: Robust Project Schedulingmentioning

confidence: 99%

“…. , 10} (Shi et al, 2021;Pang and Meng, 2023). The weight w i0 of the dummy start activity is set to 0.…”

Section: Experimental Designmentioning

confidence: 99%

“…The global coordination decision‐making layer mainly addresses the multi‐skilled staff allocation. Resource flow network construction (robust resource allocation) is an effective strategy to maintain the stability of scheduling and reasonably allocate resources through efficiently transferring resources between activities (Pang and Meng, 2023). In this subsection, a GRAH algorithm based on the resource flow network is developed to deal with a global problem.…”

Section: Two‐layer Approachmentioning

confidence: 99%

“…Three levels of σ (i.e.,

\sigma \in \lbrace 0.3,0.6,0.9\rbrace

) represent a duration uncertainty that is low, medium, or high, respectively. The weight

w_{ij}

for nondummy activity

a_{ij}

is drawn from a discrete triangular distribution with

P(w_{ij}=x)=0.21-0.02\cdot x

, in which

x\in \lbrace 1,2,\ldots ,10\rbrace

(Shi et al., 2021; Pang and Meng, 2023). The weight

w_{i0}

of the dummy start activity is set to 0.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…This subsection analyzes how the manager sets reasonable deadlines so that the baseline schedule can be completed within a given time with a high probability.

TPCP

is common in robust project scheduling (Hu et al., 2016; Pang and Meng, 2023), which indicates the probability that the project can be completed before the deadline. Since the results of all instances in each problem subset show the same trend, this paper only shows the simulation results of an instance in each problem subset.…”

Section: Computational Experimentsmentioning

confidence: 99%

See 4 more Smart Citations

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

You,

Xu,

et al. 2023

Int Trans Operational Res

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Uncertain activity durations and multi‐skilled staff allocation are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized resource‐constrained multiproject scheduling problem with uncertain activity durations and multi‐skilled staff allocation. The robust project scheduling is employed to tackle uncertainty. We name it the robust decentralized resource‐constrained multiproject scheduling problem with multi‐skilled staff (RDMPSP‐MS). To formulate this problem, a two‐layer model containing local scheduling and global coordination decision‐making is established based on the multiagent system. To solve this model, a two‐layer approach (TLA) is developed. In the TLA, a starting time criticality heuristic method is adopted to handle the local scheduling problem, and a global resource allocation heuristic algorithm is designed to allocate multi‐skilled staff. The computational experiments are conducted on the adapted Multi‐Project Scheduling Problem LIBrary (MPSPLIB) data set. The results show that the proposed TLA outperforms seven heuristic algorithms based on priority rules regarding solution quality and efficiency on most problem subsets. It is further verified that the robustness of the baseline schedule deteriorates with the increase in problem size and activity duration variability level. Additionally, the results of this research provide practical guidance for managers who expect to determine reasonable project deadlines in a decentralized multiproject environment.

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Section: Robust Project Schedulingmentioning

confidence: 99%

“…. , 10} (Shi et al, 2021;Pang and Meng, 2023). The weight w i0 of the dummy start activity is set to 0.…”

Section: Experimental Designmentioning

confidence: 99%

Section: Two‐layer Approachmentioning

confidence: 99%

“…Three levels of σ (i.e.,

\sigma \in \lbrace 0.3,0.6,0.9\rbrace

) represent a duration uncertainty that is low, medium, or high, respectively. The weight

w_{ij}

for nondummy activity

a_{ij}

is drawn from a discrete triangular distribution with

P(w_{ij}=x)=0.21-0.02\cdot x

, in which

x\in \lbrace 1,2,\ldots ,10\rbrace

(Shi et al., 2021; Pang and Meng, 2023). The weight

w_{i0}

of the dummy start activity is set to 0.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…This subsection analyzes how the manager sets reasonable deadlines so that the baseline schedule can be completed within a given time with a high probability.