Extraction of dispatching rules for single machine total weighted tardiness using a modified genetic algorithm and data mining

Zahmani, Mohamed Habib; Atmani, Baghdad

doi:10.1504/ijmr.2018.092776

Cited by 10 publications

(6 citation statements)

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“…In order to create experimental databases for learning, a GA is proposed in this paper. GA is widely used in the field of project scheduling and can obtain good results on scheduling problems (Armentano and Mazzini, 2000; Zahmani and Atmani, 2018). This data-based approach learns the knowledge contained in the solutions generated by GA (Weckman et al ., 2008).…”

Section: Discussionmentioning

confidence: 99%

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Xie

Chen

et al. 2023

ECAM

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PurposeProject scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.Design/methodology/approachThe resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.FindingsIn this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.Originality/valueThis paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

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Section: Discussionmentioning

confidence: 99%

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Xie

Chen

et al. 2023

ECAM

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“…The spatial solution search performance and calculation ability of GA are excellent, and its robustness is high. It provides a new and effective way to solve complex optimization problems [40,41]. However, the traditional GA often has the defects of premature convergence and local optimum.…”

Section: Resource-constrained Project Scheduling Problemmentioning

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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“…Moreover, for each problem the upper and lower bounds are given and updated, which will be used in this paper for comparison purpose. The availability of lower/upper bounds in addition to the use of these benchmarks by other researchers (Kurdi 2015; Cheng et al 2016;Habib Zahmani and Atmani 2018) to evaluate their approaches motivated the choice of testing the proposed approach using the same benchmarks.…”

Section: Job Shop Problems Datasetsmentioning

confidence: 99%

Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

Zahmani

Atmani

2020

J Sched

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In production planning and scheduling, data mining methods can be applied to transform the scheduling data into useful knowledge that can be used to improve planning/scheduling by enabling real-time decision-making. In this paper, a novel approach combining dispatching rules, a genetic algorithm, data mining, and simulation is proposed. The genetic algorithm (i) is used to solve scheduling problems, and the obtained solutions (ii) are analyzed in order to extract knowledge, which is then used (iii) to automatically assign in real-time different dispatching rules to machines based on the jobs in their respective queues. The experiments are conducted on a job shop scheduling problem with a makespan criterion. The obtained results from the computational study show that the proposed approach is a viable and effective approach for solving the job shop scheduling problem in real time.

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Extraction of dispatching rules for single machine total weighted tardiness using a modified genetic algorithm and data mining

Cited by 10 publications

References 0 publications

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm

Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

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