Tabu search for proactive project scheduling problem with flexible resources

Ma, Yong; He, Zhengwen; Wang, Nengmin; Demeulemeester, Erik

doi:10.1016/j.cor.2023.106185

Cited by 12 publications

(5 citation statements)

References 63 publications

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“…The framework refines Genetic Programming (GP) by simultaneously filtering both the attribute set and depth range of gene expression trees, offering a unique hyper-heuristic perspective for project scheduling in stochastic environments. Ma et al [29] explore a proactive project scheduling problem with flexible resources possessing multiple skills, allowing skill changes at discrete intervals. They introduce a tabu search algorithm to generate a feasible skill allocation plan, aiming to maximize schedule robustness.…”

Section: Related Workmentioning

confidence: 99%

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

Akbar,

Zubair,

Khan

et al. 2024

IEEE Access

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This study addresses the Weighted Multi-Skill Resource Constrained Project Scheduling Problem (W-MSRCSPSP) with the aim of minimizing software project makespan. Unlike previous works, our investigation regards heterogeneous resources characterized by varying skill proficiency levels. Another major problem with existing methodologies is the potential underutilization of human resources due to varying task durations. This work introduces an innovative scheduling approach known as the Greedy and Parallel Scheduling (GPS) algorithm to handle the said issues. GPS focuses on assigning the most suitable resources available to project activities at each scheduling point. The fundamental goal of our proposed approach is to reduce resource wastage while efficiently allocating surplus resources, if any, to project tasks, ultimately leading to a decrease in the makespan. To empirically evaluate the efficacy of the GPS algorithm, we conduct a comparative analysis against the Parallel Scheduling Scheme (PSS). The advantage of our proposed approach lies in its ability to optimize the utilization of available resources, resulting in accelerated project completion. Results from extensive simulations substantiate this claim, demonstrating that the GPS scheme outperforms the PSS approach in minimizing project duration.

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Section: Related Workmentioning

confidence: 99%

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

Akbar,

Zubair,

Khan

et al. 2024

IEEE Access

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“…Liang et al (2019) designed the expected penalty cost procedure that iteratively allocated time buffers for resolving the max-net present value (NPV) project scheduling problem with stochastic activity durations. Ma et al (2023) constructed an integer linear programming model for the proactive project scheduling problem with flexible resources, and they developed a tabu search embedded with the time buffer-adding procedure to address the studied problem. Other studies (Zhang et al, 2017;Tian et al, 2022) also adopted adding time buffers to tackle the project scheduling problem under uncertainty.…”

Section: Robust Project Schedulingmentioning

confidence: 99%

“…Ma et al. (2023) constructed an integer linear programming model for the proactive project scheduling problem with flexible resources, and they developed a tabu search embedded with the time buffer‐adding procedure to address the studied problem. Other studies (Zhang et al., 2017; Tian et al., 2022) also adopted adding time buffers to tackle the project scheduling problem under uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Hal ini mengindikasikan bahwa tabu search merupakan algoritma yang reliabel untuk menyelesaikan berbagai permasalahan. Contoh permasalahan yang telah diselesaikan dengan tabu search yaitu project scheduling [23], VRP [24], probabilistic orienteering problem [25], TTP [26], dan lain-lain.…”

Section: G Tabu Searchunclassified

Perbandingan Algoritma Heuristic Selection untuk Penjadwalan Ujian Menggunakan Kerangka Kerja Hyper-Heuristics

Hutama

Nahdliyah²,

Nabila³

et al. 2023

jtek

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— Examination timetabling is an example of a problem in operations research and an exciting issue in higher education. For example, the problems that occur in an institution are the limited number and capacity of rooms, few exam supervisors, and limited exam time. So far, exam scheduling is done manually, so it is pretty time-consuming. Until now, no algorithm can solve this problem in polynomial time. So that in the optimization world, the problem of scheduling exams is an NP-Hard problem. Solving this problem can be done using a heuristic algorithm to produce a reasonably good solution fairly quickly. Currently, the algorithm has been developed in various ways with a hyper-heuristic approach. This study will discuss the scheduling of exams using carter benchmark datasets. The charter dataset is a dataset of real-world exam scheduling problems. The trials carried out in this study were to apply hyper-heuristics by combining a move acceptance algorithm, namely late acceptance hill climbing, with several heuristic selection algorithms to get the heuristic selection algorithm with the best performance to be combined. The result is the combination of Reinforcement Learning with Late Acceptance Hill Climbing can outperform the results of the comparison algorithm in most of the datasets. Key words — Carter Datasets; Examination Timetabling; Hyper-Heuristics; Late Acceptance Hill Climbing; Reinforcement Learning; Tabu Search Abstrak — Penjadwalan ujian merupakan salah satu contoh permasalahan dalam riset operasi serta permasalahan yang menarik untuk dihadapi di pergurugan tinggi. Sebagai contoh, permasalahan yang terjadi di sebuah perguruan tinggi yaitu terbatasnya jumlah dan kapasitas ruangan, terbatasnya pengawas ujian, serta terbatasnya waktu ujian. Selama ini, penjadwalan ujian dilakukan secara manual sehingga cukup memakan waktu. Hingga saat ini belum ada algoritma yang dapat menyelesaikan permasalahan tersebut dalam waktu polinomial. Sehingga dalam dunia optimasi, permasalahan penjadwalan ujian merupakan NP-Hard problem. Penyelesaian permasalahan ini dapat dilakukan dengan menggunakan algoritma heuristic untuk menghasilkan solusi yang cukup baik dengan waktu yang cukup singkat. Saat ini algoritma telah berkembang secara variatif dengan pendekatan hyper-heuristics. Penelitian ini akan membahas penjadwalan ujian menggunakan benchmark dataset carter. Dataset carter merupakan dataset permasalahan penjadwalan ujian real-world. Uji coba yang dilakukan pada penelitian ini yaitu menerapkan hyper-heuristics dengan mengombinasikan sebuah algoritma move acceptance yaitu late acceptance hill climbing dengan beberapa algoritma heuristic selection untuk mendapatkan algoritma heuristic selection dengan performa terbaik untuk dikombinasikan. Hasilnya yaitu kombinasi Reinforcement Learning dengan Late Acceptance Hill Climbing mampu mengungguli hasil algoritma pembanding dalam sebagian besar dataset. Kata kunci — Dataset Carter; Hyper-Heuristics; Late Acceptance Hill Climbing; Penjadwalan Ujian; Reinforcement Learning; Tabu Search

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Tabu search for proactive project scheduling problem with flexible resources

Cited by 12 publications

References 63 publications

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

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

Perbandingan Algoritma Heuristic Selection untuk Penjadwalan Ujian Menggunakan Kerangka Kerja Hyper-Heuristics

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