Stochastic resource leveling optimization method for trading off float consumption and project completion probability

Gwak, Han-Seong; Lee, Dong‐Eun

doi:10.1111/mice.12668

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…Construction PSE primarily addresses the RCPSP and resource‐leveling problem (RLP; Gwak & Lee, 2021). RCPSP is widely acknowledged as NP‐hard in the realm of construction project management (Zhang et al., 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Data‐driven out‐of‐order model for synchronized planning, scheduling, and execution in modular construction fit‐out management

Jiang,

Li,

et al. 2024

Computer aided Civil Eng

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Fit‐out operations in modular construction exhibit unique features, such as limited room space and diversly distributed operations in the building. These features pose significant challenges to planning, scheduling, and execution (PSE) of fit‐out activities due to operational parallelism, distributional diversity, and narrower constrained time window in modular construction. Hence, logistics‐operation and multi‐operations synchronizations in a real‐time manner are crucial for PSE of modular construction fit‐out management. With the support of cutting‐edge information technologies, the real‐time data inside the generated digital twins can simplify online optimization models and convert stochastic factors into deterministic parameters. This paper formulates a novel real‐time data‐driven out‐of‐order (OoO) model for synchronized PSE in modular construction fit‐out management. Drawing inspiration from OoO mechanism in Central Processing Unit (CPU), a real‐time data‐driven and rolling‐horizon‐based OoO model is proposed for PSE of modular construction fit‐out, employing a forward heuristic algorithm for solution. Time–space–state data from digital twins are updated to facilitate dynamic decision‐making of managers. Through stochastic computational experiments, we demonstrate the effectiveness of OoO model in optimizing project metrics and the improved resilience in uncertain environments.

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Section: Literature Reviewmentioning

confidence: 99%

Data‐driven out‐of‐order model for synchronized planning, scheduling, and execution in modular construction fit‐out management

Jiang,

Li,

et al. 2024

Computer aided Civil Eng

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“…Li et al [38] researched the resource leveling problem with uncertain time lags and activity durations, and compared the performance of an evolutionary algorithm and a Bat algorithm. Gwak and Lee [39] also conducted research on the resource leveling problem under uncertainty. The proposed mode can minimize resource fluctuations while maximizing project completion probability.…”

Section: Resource Leveling Problemmentioning

confidence: 99%

Linear Project-Scheduling Optimization Considering a Reverse Construction Scenario

Yu,

Wang,

Zhao

et al. 2023

Applied Sciences

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The linear scheduling method (LSM) for optimization in linear projects has been the focus of numerous academic studies over the years. However, research on incorporating reverse construction activities and other practical scenarios, such as flexible activity–section–crew assignment demands, in linear project-scheduling optimization remains insufficient. This has further spurred research on practical scenario-based linear project-scheduling optimization. We performed an analysis of a description method system within the LSM framework, outlining the spatiotemporal logical relationship in scenarios involving reverse activities. A scheduling optimization model incorporating a flexible constraint system, capable of handling multisection–multicrew, multicrew–multimode, and multicrew–multiconstruction direction scenarios is developed to enhance the practicability of linear project-scheduling optimization. Additionally, an advanced grey wolf optimization (GWO) algorithm is devised and validated through a real-world case study. The case study provides insights into the impact of reverse construction activities on the linear project-scheduling problem, specifically in the dimension of project duration.

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“…(2018) summarized various indicators for measuring resource usage. Among them, indicators related to resource leveling, such as resource fluctuation, are the most widely used (Gwak & Lee, 2021; Z. Wang et al., 2020). Therefore, the resource fluctuation indicator was adopted to measure resource usage in this study.…”

Section: Model Formulationmentioning

confidence: 99%

Scenario‐oriented repetitive project scheduling optimization

Wang

Tang

2022

Computer aided Civil Eng

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Research on project scheduling optimization is trending toward many-objective optimization, in which the number of objectives exceeds three. However, existing studies usually only consider the necessary logical constraints, ignoring descriptions of practical scenarios and corresponding complex constraints, which limit the designed algorithms in problems with such scenarios and constraints. This study focuses on the many-objective repetitive project scheduling problem considering practical scenarios with complex constraints (MRPSP-PSCC). Various constraints are described under flexible matching/mapping between multi-crew, multi-mode, and multi-section scenarios. A many-objective project scheduling model is proposed for synchronous optimization of time, cost, quality, resource usage, and interruption time. The multiphase balance of diversity and convergence nondominated sorting genetic algorithm III (B-NSGA-III) with unique advantages for continuous many-objective optimization problems is transformed for discrete many-objective optimization. A series of unique designs is employed in the algorithm, including three-layer coding rules, constraint handling, and local search, to improve the problem-solving efficiency of the algorithm. The effectiveness and superiority of the model and algorithm for MRPSP-PSCC were verified through a case study. INTRODUCTIONProject scheduling consists of deciding when certain activities should be started and finished and how to organize resources to complete these activities. Scheduling is among the most important managerial functions for construction projects. Project process control necessitates a predetermined schedule (Wu et al., 2014). At present, studies on project scheduling optimization show the following trends: (1) Integration of basic optimization problems, such as multi-mode resource-constrained project scheduling problem (MRCPSP) and time-cost trade-off problem (TCTP;

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Stochastic resource leveling optimization method for trading off float consumption and project completion probability

Cited by 5 publications

References 52 publications

Data‐driven out‐of‐order model for synchronized planning, scheduling, and execution in modular construction fit‐out management

Data‐driven out‐of‐order model for synchronized planning, scheduling, and execution in modular construction fit‐out management

Linear Project-Scheduling Optimization Considering a Reverse Construction Scenario

Scenario‐oriented repetitive project scheduling optimization

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