A Genetic Algorithm for the Proactive Resource-Constrained Project Scheduling Problem With Activity Splitting

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

doi:10.1109/tem.2018.2819689

Cited by 24 publications

(21 citation statements)

References 39 publications

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“…With respect to the proactive scheduling, we use the surrogate robustness measure proposed in Section 2.2 as the objective function for maximization. This proactive scheduling problem has been proven to be NP-hard in the strong sense (Ma et al, 2019), which makes the achievement of optimal solutions a computationally difficult proposition, especially for large projects. For this reason, we apply a heuristic algorithm, the tabu search algorithm proposed by Lambrechts et al (2008b), to solve the problem to generate a robust schedule.…”

Section: Experimental Designmentioning

confidence: 99%

A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments

Demeulemeester

et al. 2019

Computers & Industrial Engineering

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Highlights✓ A general framework of the slack-based surrogate robustness measures is proposed ✓ The best surrogate measures are found for two kinds of uncertain environments ✓ The improvement is 13.79% under the environment of stochastic activity durations ✓ The impact of parameters on the performance of the robustness measure is analyzed ✓ The time buffering strategies are different under the two uncertain environments 1 A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments Abstract This paper addresses the proactive resource-constrained project scheduling problem, aiming to explore better surrogate robustness measures for project managers that want to generate robust baseline schedules under uncertain environments. The contribution of this paper is threefold.First, we propose a general framework of slack-based surrogate robustness measures and introduce three parameters to distinguish different alternative calculations of the measure. A computational experiment based on reactive simulation is constructed where the performance of the surrogate measures is evaluated by the reactive cost and two types of uncertain environments, i.e. stochastic resource availabilities and stochastic activity durations, are taken into account. Second, we analyze the impact of the three parameters as well as the two uncertain environments on the surrogate robustness measures and find the best measures for different situations. The proposed surrogate robustness measures are shown to be effective. Compared with benchmark measures, the improvements are respectively 2.67% and 13.79% under the two uncertain environments. Third, we investigate the difference of buffering strategies between the two uncertain environments. For the environment of stochastic resource availabilities, it turns out to be better to have a uniform distribution of time buffers throughout the schedule, while the reverse is true for the environment of stochastic activity durations.

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Section: Experimental Designmentioning

confidence: 99%

A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments

Demeulemeester

et al. 2019

Computers & Industrial Engineering

Self Cite

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“…From the perspective of design ability and application effect, GA is considered to be the best solution among many solutions [58,59]. Since the introduction of GA by John Holland [60] in 1975, GA has been widely used to solve engineering planning problems [60], such as flow operation [61], construction projects [62], and highway construction [63]. The proposal of GA provides an effective solution for complex project scheduling optimization problems, and its good search and computing capabilities are generally recognized by researchers.…”

Section: Meta-heuristic Algorithmmentioning

confidence: 99%

Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm

2021

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Prefabricated buildings are the direction of the future development of the construction industry and have received widespread attention. The effective execution of prefabricated construction project scheduling should consider resource constraints and the supply arrangement of prefabricated components. However, the traditional construction resource-constrained project scheduling implementation method cannot simultaneously consider the characteristics of the linkage between component production and on-site assembly construction. It cannot also fully adapt to the scheduling implementation method of the prefabricated construction projects. It is difficult to work out a reasonable project schedule and resource allocation table. In order to determine the relevant schedule parameters that can reflect the actual construction situation of the prefabricated building and meet the scheduling requirements of the prefabricated project, this study proposes a prefabricated construction project scheduling model that considers project resource constraints and prefabricated component supply constraints. Additionally, it improves the design of traditional genetic algorithms (GAs). Research results of the experimental calculation and engineering application show that the proposed project scheduling optimization model and GA are effective and practical, which can help project managers in effectively formulating prefabricated construction project scheduling plans, reasonably allocating resources, reducing completion time, and improving project performance.

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“…The project is composed of a series of interrelated activities. Its scheduling decision needs to meet the temporal constraints and resource constraints on project activities at the same time, to optimize the management objectives [12] . MRCPSP (Multi-mode Resource-Constrained Project Scheduling Problem) is an extension of traditional RCPSP, it considers that each activity in a project has a variety of execution modes that can be selected, each execution mode corresponds to a certain duration and resource requirements.…”

Section: Rcpsp (Resource-constrained Project Schedulingmentioning

confidence: 99%

Complex Equipment Remanufacturing Schedule Management Based on Multi-Layer Graphic Evaluation and Review Technique Network and Critical Chain Method

Zhang

Liu

2020

IEEE Access

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In order to solve the problem of project schedule delays caused by resource conflicts and uncertainty of the environment, work duration, and logical relationship, this paper proposed CCPSP (critical chain project scheduling problem) optimal scheduling model which integrates CCM (critical chain method) and multi-level GERT (graphical evaluation and review technique) network. DEACA (differential evolution ant colony algorithm) is designed to solve the problem, in which DE (differential evolution algorithm) is used to deal with the cross problem and mutation problem, and ACA (ant colony algorithm) is used to deal with each stage of the optimization process. The activity resource list is used as the coding rule of each feasible scheduling solution. The activity resource list is used as the coding rule of every feasible scheduling solution, and the activity list is generated by roulette using pheromone and heuristics. Insert and swap neighborhood search structures are introduced to optimize the solution iteratively, and the Pareto file is used to save the non-dominated solution, which is updated in each generation of ant colony search. This paper makes full use of the advantages of the two algorithms to achieve fast convergence and a global search for optimal solutions. At the same time of seeking the optimization algorithm, considering the multiresource constraints, environmental uncertainties and other factors of the project, a buffer is set up, and a "minimum duration-maximum robustness" project schedule is formulated. Finally, through the analysis and verification of the project example, it shows that the project schedule made by this method is feasible. INDEX TERMS GERTs (graphic evaluation and review technique simulation), CCM (critical chain method), CCPSP (critical chain project scheduling problem), Schedule plan, DEACA (Differential Evolution Ant Colony Algorithm), schedule buffer

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A Genetic Algorithm for the Proactive Resource-Constrained Project Scheduling Problem With Activity Splitting

Cited by 24 publications

References 39 publications

A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments

A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments

Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm

Complex Equipment Remanufacturing Schedule Management Based on Multi-Layer Graphic Evaluation and Review Technique Network and Critical Chain Method

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