A genetic algorithm for proactive project scheduling with resource transfer times

Ma, Zhiqiang; Zheng, Weibo; He, Zhengwen; Wang, Nengmin; Hu, Xuejun

doi:10.1016/j.cie.2022.108754

Cited by 13 publications

(2 citation statements)

References 41 publications

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“…In a similar vein, in [29], the authors provide a solution to the same problem (when now the tasks are allowed to be interrupted at discrete times) using a GA, while in [33] the same problem is addressed using a quantum-inspired GA that differs from the classical GA in the way the initial and the updated populations are implemented. In [34] authors investigate the resource-constrained project scheduling problem with resource transfer times under uncertain environment using GA. Hybrid approaches [37] combine the power of different kind of algorithms (ILPs, metaheuristics, heuristics) to solve large scale problems.…”

Section: Related Workmentioning

confidence: 99%

A Practical Approach for Resource-Constrained Project Scheduling

Manousakis,

Savva,

Papadouri

et al. 2024

IEEE Access

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This work considers project scheduling and planning for the decision support of organizations, that continuously require the implementation of many projects for their successful operation. The efficient scheduling and planning of these projects is essential for the timely completion of all projects, utilizing the appropriate resources. To this end, this work presents a novel integer linear program (ILP) formulation, that takes into account the project requirements, the involved teams, their interdependencies, as well as other constraints, so as to provide an optimal scheduling plan. Moreover, additional constraints are considered to address practical challenges such as complexity and uncertainties. Finally, techniques are introduced in this work to address scalability issues, as well as dynamic changes that may occur when the obtained schedule is currently being implemented. All aforementioned techniques present a number of advantages for an organization, as they reduce considerably the person-hours required by the management team to perform the scheduling, they produce scheduling plans that span large planning horizons, and they decrease the project completion times, thus reducing the cost that the organization incurs for implementing the projects.Realistic scenarios are considered, where real data on projects and teams are taken into account. From the results obtained, it is evident that the proposed ILP can obtain the optimal solution in terms of minimizing the duration for the completion of all projects, while the proposed practical (heuristic) approaches, can obtain solutions close to the optimal in terms of planning horizon and objective score with significant reduction in computation time, from hours to seconds/minutes. Moreover, it is shown that the scheduling plan can be adapted in the event of miscalculations related to the effort required for implementing the projects or new projects can be added within the existing scheduling plan.INDEX TERMS project scheduling and planning; decision support systems; integer linear programming.

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Section: Related Workmentioning

confidence: 99%

A Practical Approach for Resource-Constrained Project Scheduling

Manousakis,

Savva,

Papadouri

et al. 2024

IEEE Access

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“…Inspired by existing methods, there are mainly two strategies for tackling activity duration uncertainty. One of the strategies is robust project scheduling (Van de Vonder et al., 2008; Ma et al., 2022; Peng et al., 2023). The approach generates a baseline schedule that can absorb the execution variability as much as possible.…”

Section: Introductionmentioning

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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A predictive-reactive strategy for flight test task scheduling with aircraft grounding

Tian,

Xiao,

Shen

2024

Complex Intell. Syst.

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In flight test engineering, the flight test duration (FTD) affects the aircraft’s delivery node and directly impacts costs. In the actual flight test process, the environmental status updates frequently, and various uncertain events are often encountered, which affect the flight test progress and project implementation. Therefore, when scheduling flight test tasks, rescheduling should be taken into account. This paper proposes a predictive-reactive strategy based on a deep reinforcement learning approach to solve the flight test task scheduling problem with consideration of aircraft grounding. In the predictive stage, a constructive heuristic algorithm is designed to generate an initial schedule. The rescheduling problem is solved by the appropriate rescheduling method that aims to optimize the FTD deviation, task reallocation, and workload cost simultaneously. The problem is modeled as a Markov decision process, including the well-designed state features, rewards, and actions based on different rescheduling methods. The policy is trained by the proximal policy optimization algorithm. At last, numerical results are provided to demonstrate the effectiveness and superiority of the proposed approach.

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A genetic algorithm for proactive project scheduling with resource transfer times

Cited by 13 publications

References 41 publications

A Practical Approach for Resource-Constrained Project Scheduling

A Practical Approach for Resource-Constrained Project Scheduling

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

A predictive-reactive strategy for flight test task scheduling with aircraft grounding

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